# Education for digital transformation How to use digital technologies and media to enhance learning and teaching? How to develop digital literacy, critical thinking and media culture among children and young people? **Section Editor:** Nataša Rogulja, Krešo Tomljenoviću # The Digital Literacy of First Grade Primary School Students
[![logo stoo2_1 (no).png](https://hub.ufzg.hr/uploads/images/gallery/2024-09/scaled-1680-/logo-stoo2-1-no.png)](https://hub.ufzg.hr/uploads/images/gallery/2024-09/logo-stoo2-1-no.png) **Teaching (Today for) Tomorrow:** **Bridging the Gap between the Classroom and Reality** 3rd International Scientific and Art Conference Faculty of Teacher Education, University of Zagreb in cooperation with the Croatian Academy of Sciences and Arts
##### **Nikolina Hutinski, Predrag Oreški** *Faculty of education, University of Zagreb, Croatia* *nhutinski1996@gmail.com*
**Section - Education for digital transformation****Paper number: 38****Category: Original scientific paper**
##### **Abstract**
The research presented in this paper aims to explore the digital literacy of first grade primary school students. The research sample consists of 104 students from northwestern Croatia. They were invited to fill out the self-assessment questionnaire consisting of eleven items including statements about their gender, place of residence (rural or urban), and simple yes/no statements concerning the knowledge of using the computer hardware and software. The research results show a statistically significant difference in respondents’ asking for parents’ or guardians' permission to use a computer by gender (χ2=4.27, df=1, *p*=0.039). There are more female respondents (81.3%) than male respondents (60.7%) who ask their parents or guardians for permission to use the computer. Most of the respondents (88.5%) know how to turn on/off computers, 87.5% of respondents know how to write a text using a computer and 94.2% of respondents know how to make a drawing using a computer. There is 94.2% of respondents who know how to use the Internet and there is a statistically significant difference by the place of residence (χ2=4.63, df=1, *p*=0.031). There are more urban respondents (100.0%) than rural respondents (88.2%) who know how to search the Internet. Most of the respondents (91.3%) understand and apply the rules of conduct on the Internet. Most respondents (87.5%) self-assess themselves as having acquired the learning outcomes specified in the informatics curriculum.
***Key words:***
digital competence; informatics curriculum; primary education
**Introduction** Children begin to use digital technologies at a very early age: two-year-old toddlers regularly watch films and videos and listen to music on tablet computers (Ólafsson et al, 2014). Children's Internet use is generally over 85% for the age group beginning at six, rising to around 95% for older children (14 and older). One study finds that even 40% of 3 to 6-year-olds use the Internet at least once a week, predominantly with a tablet device (Ólafsson et al, 2014). Today's children use digital devices, such as tablets, smartphones and computers, from an early age. Radesky et al. (2020) report research results on the sample of 346 parents and guardians of children aged 3 to 5 years where children were using tablets and smartphones to access applications such as YouTube, YouTube Kids, Internet browser, Quick Search Box or Siri, and streaming video services. 121 children (35%) had their own devices, and their average daily usage was 115 minutes (SD 115.1; range 0.20–632.5). It is important to prepare children and young people to use information and communication technology safely and responsibly. In the era when Artificial Intelligence (AI) is having a growing influence on people’s everyday lives, it is important to acquire knowledge and skills to learn and work with the newest digital technologies and to be prepared for the future. This set of knowledge and skills is known as digital literacy. “Digital literacy is the set of knowledge, skills, attitudes and values that enable children to confidently and autonomously play, learn, socialize, prepare for work and participate in civic action in digital environments. Children should be able to use and understand technology, to search for and manage information, communicate, collaborate, create and share content, build knowledge and solve problems safely, critically and ethically, in a way that is appropriate for their age, local language and local culture” (Nascimbeni & Vosloo (UNICEF), (2019), p. 32). In the European Union, digital literacy is defined through digital competence. “Digital competence involves the confident, critical and responsible use of, and engagement with, digital technologies for learning, at work, and for participation in society. It includes information and data literacy, communication and collaboration, media literacy, digital content creation (including programming), safety (including digital well-being and competences related to cybersecurity), intellectual property related questions, problem solving and critical thinking” (European Union, 2019, p. 10). Digital competence is one of the key competences for lifelong learning (European Union, 2019, p. 5): Literacy competence, Multilingual competence, Mathematical competence and competence in science, technology and engineering, Digital competence, Personal, social and learning to learn competence, Citizenship competence, Entrepreneurship competence, Cultural awareness and expression competence. More than one in five young people fail to reach a basic level of digital skills across the European Union (European Commission, 2020b). Providing schooling in computing equips young people with a solid comprehension of the digital realm. Initiating students into computing early on and employing inventive and engaging teaching methods across both formal and informal settings, aids in building problem-solving, creativity, and teamwork skills. Furthermore, it nurtures enthusiasm for STEM fields and potential careers, simultaneously addressing gender stereotypes. Endeavours to enhance computing education's quality and inclusivity can significantly influence the enrolment of female students in IT-related higher education programs and subsequently their participation in digital professions across various economic sectors (European Commission, 2020a). The Digital Education Action Plan (2021-2027) has two strategic priorities (European Commission, 2020b): - to foster a high-performing digital education ecosystem, and - to enhance digital skills and competences for the digital age. The latter includes the following activities: - support the provision of basic digital skills and competences from an early age: - digital literacy, including management of information overload and recognising disinformation - computing (informatics or computer science) education - good knowledge and understanding of data-intensive technologies, such as AI - boost advanced digital skills: enhancing the number of digital specialists and girls and women in digital studies and careers. One of the Action Plan activities is to encourage female participation in STEM. Female students generally perform better than male students in the Programme for International Student Assessment (PISA) and International Computer and Information Literacy Study (ICILS) international skills tests. However, only one in three STEM graduates is a woman (European Commission, 2020a). Digital literacy and communication include knowing the possibilities of hardware and software solutions and developing cooperation and communication skills in an online environment. Knowledge of the possibilities of current technology and computer programs is a prerequisite for their proper selection and effective and innovative application in various fields. It is necessary to develop digital literacy from an early age and throughout schooling so that students are prepared for life and work in a digital society (Ministry of Science and Education, 2018). According to the same source, after the first year of studying the subject Informatics in the field of Digital Literacy and Communication, the students should acquire the following learning outcomes: - C.1.1 with the support of the teacher student uses the proposed programs and digital educational content and - C.1.2 with the support of the teacher student creates simple digital content with very simple actions. Digital literacy is essential to learn, work and succeed in today’s digital society and it is important to prepare children and young people to use information and communication technology safely and responsibly from an early age. **Methodology** *** *** ***Aims*** The research aims to explore the digital literacy of first grade primary school students and possible differences by gender and by place of residence (urban or rural). *** *** ***Hypotheses*** H1: There is no statistically significant difference in the self-assessed digital literacy of first grade primary school students by gender. It is expected that the respondents will self-assess their digital literacy equally regardless of their gender (female and male). Female students generally perform better than male students in the Programme for International Student Assessment (PISA) and International Computer and Information Literacy Study (ICILS) international skills tests. However, only one in three STEM graduates is a woman (European Commission, 2020). The research results can show whether there are differences in the digital literacy of female and male students already at this early age. H2: There is no statistically significant difference in the self-assessed digital literacy of first grade primary school students by their place of residence. It is expected that the respondents will self-assess their digital literacy equally regardless of their place of residence (rural and urban). There is a possibility that the availability of optional subjects of informatics in urban and rural schools is not the same and that Internet connectivity in schools and at home is not the same in rural and urban areas. These two factors, the availability of optional subjects of informatics and Internet connectivity, can influence the students’ digital literacy. H3: More than 80% of students use the Internet. From an early age, children are exposed to the Internet through information and communication technology such as smartphones, tablets, laptops and desktop computers and know how to use it to search the Internet. It is expected that more than 80% of students use the Internet. *** *** ***Sample*** The research sample consists of 104 first grade primary school students from four primary schools and two primary district schools (district school in Croatian: područna škola) from northwestern Croatia in the spring of 2023. There are 48 female (46.2%) and 56 male (53.8%) students in the sample. There are 53 students (51.0%) from urban places of residence and 51 students (49.0%) from rural places of residence. There are 98 students (94.2%) who attend the optional subject of informatics in the first grade, and 6 students do not attend (5.8%) (Table 1). Table 1 *Respondents’ demographic data*
Item Number of respondents Percent
Gender
Female 48 46.2
Male 56 53.8
Total 104 100.0
Place of residence
Rural 51 49.0
Urban 53 51.0
Total 104 100.0
Attending the optional subject of Informatics
Yes 98 94.2
No 6 5.8
Total 104 100.0
***Instruments*** Since the respondents were aged six and seven, the data gathering method used was a simple questionnaire containing eleven items written on paper (Table 2). Table 2 *Questionnaire items with answer options*
No. Item Item type Answer options
1 Respondent’s gender Multiple choice Female / Male
2 Respondent’s place of residence Multiple choice Rural / Urban
3 I attend the optional subject of informatics in the first grade Statement - multiple choice Yes / No
4 I have a computer at home (yes/no) Statement - multiple choice Yes / No
5 I always ask parents or guardians for permission to use a computer (yes/no) Statement - multiple choice Yes / No
6 I know how to turn on/off the computer (yes/no) Statement - multiple choice Yes / No
7 I know the names of the computer parts (yes/no) Statement - multiple choice Yes / No
8 I know how to write a text using a computer (yes/no) Statement - multiple choice Yes / No
9 I know how to make a drawing using a computer (yes/no) Statement - multiple choice Yes / No
10 I know how to search the Internet (Google, YouTube) (yes/no) Statement - multiple choice Yes / No
11 I understand and apply rules of conduct on the Internet (yes/no) Statement - multiple choice Yes / No
The first two items dealt with students’ gender and the place of residence. The other nine items were statements concerning attending the optional subject of informatics, having the computer at home, asking parents or guardians for permission to use computers, knowledge of recognizing the computer parts, knowledge of the use of computer hardware and software to perform simple tasks such as turning on or off computers, writing and editing texts, make drawings, searching the Internet, and understanding and applying rules of conduct on the Internet. The students could answer if they agree or disagree with the statement with simple dichotomous options: yes or no. The statements were chosen according to the curriculum of the optional subject Informatics and its learning outcomes in the first grade of primary school in Croatia (Ministry of Science and Education, 2018). The items of the questionnaire were adapted to the target group. ***Procedure*** The survey was implemented using the guidelines of the Ethical Code of Research with Children (National Ethics Committee for Research with Children, 2020). The survey took place in two counties of northwestern Croatia from March to May 2023. The respondents were students of four primary schools, of which two are in urban and the other two in rural areas. The first author of this paper provided assistance and explanations to the respondents when they were filling out the questionnaire. The chi-squared test is used to explore the statistically significant differences between students according to their gender and their place of residence (urban and rural). The statistical software GNU PSPP 1.4.1 was used in the data processing. **Results** Table 3 shows the statements and the number of respondents’ responses (whether they agree with a specific statement or not). There is a total number of responses and there are responses by gender. In the next columns are the results of chi-squared tests (χ2, df, *p*). Table 3. *Number of students’ responses by item and gender*
Total Male Female
No. Item Yes No Yes No Yes No χ2 df *p*
1 I attend the optional subject of informatics in the first grade 98 6 54 2 44 4 0.38 1 0.538
2 I have a computer at home 91 13 51 5 40 8 0.80 1 0.372
3 I always ask parents or guardians for permission to use a computer 73 31 34 22 39 9 4.27 1 0.039
4 I know how to turn on/off the computer 92 12 50 6 42 6 0.00 1 1.000
5 I know the names of the computer parts 93 11 46 10 47 1 5.23 1 0.022
6 I know how to write a text using a computer 91 13 48 8 43 5 0.09 1 0.766
7 I know how to make a drawing using a computer 98 6 51 5 47 1 1.15 1 0.284
8 I know how to search the Internet (Google, YouTube) 98 6 52 4 46 2 0.05 1 0.820
9 I understand and apply rules of conduct on the Internet 95 9 51 5 44 4 0.00 1 1.000
Most of the respondents (98 out of 104, 94.2%) attend the optional subject of informatics in the first grade and there is no statistically significant difference by gender (χ2= 0.38, df=1, p=0.538). Most of the respondents (91 out of 104, 87.5%) have a computer at home and there is no statistically significant difference by gender (χ2= 0.80, df=1, p=0.372). Most of the respondents (73 out of 104, 70.2%) always ask parents or guardians for permission to use a computer and there is a statistically significant difference by gender (χ2= 4.27, df=1, p=0.039). There are more female respondents (81.3%) than male respondents (60.7%) who ask their parents or guardians for permission to use the computer. Most of the respondents (92 out of 104, 88.5%) know how to turn on and off computers and there is no statistically significant difference by gender (χ2= 0.00, df=1, p=1.000). Most of the respondents (93 out of 104, 89.4%) know the names of the computer parts and there is a statistically significant difference by gender (χ2= 5.23, df=1, p=0.022). There are more female respondents (97.9%) than male respondents (81.1%) who know the names of computer parts. Most of the respondents (91 out of 104, 87,5%) know how to write a text using a computer and there is no statistically significant difference by gender (χ2= 0.09, df=1, p=0.766). Most of the respondents (98 out of 104, 94.2%) know how to make a drawing using a computer and there is no statistically significant difference by gender (χ2= 1.15, df=1, p=0.284). Most of the respondents (98 out of 104, 94.2%) know how to search the Internet (Google, YouTube) and there is no statistically significant difference by gender (χ2= 0.05, df=1, p=0.820). Most of the respondents (95 out of 104, 91.3%) understand and apply the rules of conduct on the Internet and there is no statistically significant difference by gender (χ2= 0.00, df=1, p=1.000). Table 4 shows the statements and the number of respondents’ responses (if they agree with a specific statement or not) by the place of residence. In the next columns are the results of chi-squared tests (χ2, df, *p*). Table 4. *Number of students’ responses by item and the place of residence*
Rural Urban
No. Item Yes No Yes No χ2 df *p*
1 I attend the optional subject of informatics in the first grade 50 1 48 5 1.47 1 0.225
2 I have a computer at home 45 6 46 7 0.00 1 1.000
3 I always ask parents or guardians for permission to use a computer 36 15 37 16 0.00 1 1.000
4 I know how to turn on/off the computer 44 7 48 5 0.14 1 0.706
5 I know the names of the computer parts 45 6 48 5 0.00 1 0.946
6 I know how to write a text using a computer 42 9 49 4 1.59 1 0.208
7 I know how to make a drawing using a computer 49 2 49 4 0.14 1 0.710
8 I know how to search the Internet (Google, YouTube) 45 6 53 0 4.63 1 0.031
9 I understand and apply rules of conduct on the Internet 44 7 51 2 2.12 1 0.145
When the place of residence is considered then there is no statistically significant difference between rural and urban respondents except in the item “I know how to search the Internet” where there is a statistically significant difference (χ2= 4.63, df=1, p=0.031). There are more urban respondents (100.0%) than rural respondents (88.2%) who know how to search the Internet (Google, YouTube). The research results show that most of the respondents (over 87.5%) self-assess themselves as having acquired the required learning outcomes specified in the informatics curriculum for the first grade in the field of Digital Literacy and Communication: 88.5% know how to turn on/off computers, 89.4% know the names of the computer parts, 87.5% know how to write a text using a computer, 94.2% know how to make a drawing using a computer, 94.2% know how to use the Internet, and 91.3% understand and apply the rules of conduct on the Internet. 87.5% have computers at home and 92.3% of the respondents attended the optional subject of informatics. **Discussion** ** ** ***Confirmation of the hypotheses*** H1 states that there is no statistically significant difference in the self-assessed digital literacy of first grade primary school students by gender (female and male). There is no statistically significant difference in the following items that contribute to the digital literacy of first grade primary school students: - I know how to turn on/off the computer - I know how to write a text using a computer - I know how to make a drawing using a computer - I know how to search the Internet (Google, YouTube) - I understand and apply rules of conduct on the Internet. A statistically significant difference is observed only in the item “I know the names of the computer parts” where there are more female respondents (97.9%) than male respondents (81.1%) who know the names of computer parts (χ2= 5.23, df=1, p=0.022). The hypothesis H1 is confirmed. H2 states that there is no significant difference in the self-assessed digital literacy of first grade primary school students by their place of residence (rural and urban areas). There is no statistically significant difference in the following items that contribute to the digital literacy of primary school first-grade students: - I know how to turn on/off the computer - I know the names of the computer parts - I know how to write a text using a computer - I know how to make a drawing using a computer - I understand and apply rules of conduct on the Internet. A statistically significant difference is observed only in the item “I know how to search the Internet (Google, YouTube)” where there are more urban respondents (100.0%) than rural respondents (88.2%) who know how to search the Internet (χ2= 4.63, df=1, p=0.031). The hypothesis H2 is confirmed. H3 states that more than 80% of students use the Internet. 98 out of 104 (94.2%) respondents self-assess themselves as they know how to search the Internet. The hypothesis H3 is confirmed. The research in this paper uses respondents’ self-assessed data related to their digital competence. The respondents’ age is six or seven so there is a possibility that they do not understand the questionnaire statements and/or cannot self-assess their knowledge. However, they could get guidance and help from a researcher who was present when they filled out the questionnaire. The questionnaire items were very simple and dichotomous. It is difficult to get valid overviews of skills through questionnaires. The main reason for this is that respondents tend to overestimate themselves, especially when it comes to technical skills (Ala-Mutka, 2011). García-Vandewalle et al. (2021) warn that evaluating subjectivity may have limitations. The respondents’ subjectivity regarding their level of knowledge is one of the main issues with self-assessment. However, self-assessment is still a valid tool for ascertaining how students perceive their learning and enables the detection of their strengths and weaknesses. Godaert et al. (2022) analysed 14 studies concerning the assessment of students’ digital competences in primary school. The studies used various scoring systems: three were dichotomous (1=correct; 0=incorrect), four were 5-point Likert scale, one was a 7-point Likert scale, one scoring rubric (0-2 point, 0-5 points), four combined, and one not mentioned. At least five of them were using self-reported data collection. The age of the target population in the studies was mostly in the range of 9 to 13. Only one study, Jun et al (2014), included the first grade of primary school respondents of age 6. Merritt et al. (2005) report that there were differences in respondents’ self-reported and actual digital literacy. They asked 55 students to self-report their computer literacy and later they were tested in their digital literacy. Research results show that there is a statistically significant difference between self-reported (N=55, M=2.164, SD=0.788) and actual tested (N=55, M=1.873, SD=0.610) levels of digital literacy. Porat et al. (2018) report on digital literacy research results on 280 junior-high-school students where they compared their perceived digital literacy competencies and their actual performance in relevant digital tasks. Participants expressed high confidence in their digital literacy and overestimated their actual tested competence. However, Tzafilkou et al (2022) developed and validated students’ digital competence scale based on self-reported data. Asil et al (2014) used the 5-point Likert scale to collect data on measuring computer attitudes of young students in three separate factors: perceived ease of use, affect towards computers and perceived usefulness. Hernández-Marín (2024) concludes that attitude scales have been consolidated as valuable elements in educational evaluation, allowing participants' perceptions of their learning to be satisfactorily captured. Self-assessment turns out to be an exceptionally effective method for measuring attitudes. However, to gain more perspective, complete and accurate learning, it is necessary to complement the attitude scales with other methods. In their three-year longitudinal study, Lazonder et al (2020) followed the digital literacy progress of 151 fifth and sixth graders in their skills to collect, create, transform, and safely use digital information. They report that the children made the most progress in their ability to collect information. However, their capacity for generating information showed the smallest enhancement. “Development of most skills was moderately related, and it was independent of gender, grade level, migration background, and improvements in reading comprehension and maths. Children's socioeconomic status was weakly associated with the ability to collect and safely use information, but not with the other two digital literacy skills” (Lazonder et al, 2020, p. 1). There are not many research results in the literature which deal with the digital literacy of first grade primary school students. However, the research results of first grade primary school students' self-evaluation agree with the results of Lazonder et al (2020) in the part which states that digital literacy skills are independent of gender. ** ** **Conclusion** Most of the respondents (over 87.5%) self-assess themselves as having acquired the required learning outcomes specified in the informatics curriculum for the first grade in the field of Digital Literacy and Communication. There are no statistically significant differences in digital literacy of first grade primary school students by their gender or by their place of residence. The statistically significant differences were observed only in two items that contribute to digital literacy: more female respondents know the names of computer parts and more respondents coming from urban places of residence know how to search the Internet. From an early age, students are using the Internet and there is a need to educate them to use it safely and responsibly. It is important to include and continue to teach the subject of informatics (computer science) in the initial grades of elementary school not only as an optional but as a compulsory subject. It is important to continue to develop the digital literacy of students at an early age so that they can use information and communication technology safely and responsibly and that they are ready for new technologies and new occupations. The goal is also to achieve the equal representation of female and male students in university STEM study programs. The study presented in this paper shows that, at this early age, there are still no statistically significant differences in respondents’ self-assessed digital literacy by gender. However, there is a need to encourage female students in STEM subjects, such as informatics/computer science, to achieve the goal of equal representation of female and male graduates in the STEM fields. ***Limitations of the research*** The collected data is respondents’ knowledge self-assessment. The authors are aware that the respondents could overestimate their assessment, especially at their current age of six or seven. Actual testing of students’ knowledge would probably get more precise data. The sample size is 104 and the representativeness of the results is limited. ***Funding*** This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. ***Competing interests*** The authors declare that they have no competing interests. ***Statement on the first publication of the research results*** The results of the research presented in this paper have not been published before. **References** Ala-Mutka, Kirsti (2011). 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(UNICEF) (2019). *Digital literacy for children.* Retrieved on 21.1.2024. from [https://www.unicef.org/globalinsight/media/1271/file/%20UNICEF-Global-Insight-digital-literacy-scoping-paper-2020.pdf](https://www.unicef.org/globalinsight/media/1271/file/%20UNICEF-Global-Insight-digital-literacy-scoping-paper-2020.pdf) National Ethics Committee for Research with Children (2020). *Etički kodeks istraživanja s djecom \[Ethical Code of Research with Children\]*. Retrieved on 21.2.2024. from [https://mrosp.gov.hr/istaknute-teme/obitelj-i-socijalna-politika/obitelj-12037/djeca-i-obitelj-12048/nacionalno-eticko-povjerenstvo-za-istrazivanje-s-djecom/12191](https://mrosp.gov.hr/istaknute-teme/obitelj-i-socijalna-politika/obitelj-12037/djeca-i-obitelj-12048/nacionalno-eticko-povjerenstvo-za-istrazivanje-s-djecom/12191) Ólafsson, Kjartan & Livingstone, Sonia & Haddon, Leslie. (2014). *Children’s use of online technologies in Europe.* *A review of the European evidence base*. LSE, London: EU Kids Online. Revised edition. Porat, E., Blau, I., & Barak, A. (2018). Measuring digital literacies : Junior high-school students ’ perceived competencies versus actual performance. In *Computers and Education*, 126, 23–36. [https://doi.org/10.1016/j.compedu.2018.06.030](https://doi.org/10.1016/j.compedu.2018.06.030) Tzafilkou, Katerina & Perifanou, Maria & Economides, Anastasios. (2022). Development and validation of students’ digital competence scale (SDiCoS). In *International Journal of Educational Technology in Higher Education*. [https://doi.org/10.1186/s41239-022-00330-0](https://doi.org/10.1186/s41239-022-00330-0)
[![logo stoo2_1 (no).png](https://hub.ufzg.hr/uploads/images/gallery/2024-09/scaled-1680-/logo-stoo2-1-no.png)](https://hub.ufzg.hr/uploads/images/gallery/2024-09/logo-stoo2-1-no.png) **Odgoj danas za sutra:** **Premošćivanje jaza između učionice i realnosti** 3\. međunarodna znanstvena i umjetnička konferencija Učiteljskoga fakulteta Sveučilišta u Zagrebu Suvremene teme u odgoju i obrazovanju – STOO4 u suradnji s Hrvatskom akademijom znanosti i umjetnosti
**Digitalna pismenost učenica i učenika prvih razreda osnovne škole**
##### **Sažetak**
Cilj istraživanja prikazanog u ovom radu je istražiti digitalnu pismenost učenica i učenika prvih razreda osnovne škole. Uzorak istraživanja čine 104 učenica i učenika iz sjeverozapadne Hrvatsk. Oni su pozvani da ispune anketni upitnik za samoprocjenu svojeg znanja koji se sastoji od jedanaest čestica koje uključuju izjave o njihovom spolu, mjestu stanovanja (ruralno ili urbano) te jednostavne izjave da/ne o poznavanju korištenja računalnog hardvera i softvera. Rezultati istraživanja pokazuju statistički značajnu razliku u traženju dopuštenja roditelja ili skrbnika za korištenje računala prema spolu (χ2=4,27, df=1, p=0,039). Više je ispitanica (81,3%) nego ispitanika (60,7%) koji od roditelja ili skrbnika traže dopuštenje za korištenje računala. Većina ispitanica i ispitanika (88,5%) zna uključiti/isključiti računala, 87,5% ispitanica i ispitanika zna napisati tekst pomoću računala i 94,2% ispitanica i ispitanika zna napraviti crtež pomoću računala. Internet zna koristiti 94,2% ispitanica i ispitanika te postoji statistički značajna razlika prema mjestu stanovanja (χ2=4,63, df=1, p=0,031). Više je ispitanica i ispitanika iz gradova (100,0%) nego ispitanica i ispitanika iz sela (88,2%) koji znaju pretraživati ​​internet. Većina ispitanica i ispitanika (91,3%) razumije i primjenjuje pravila ponašanja na internetu. Većina ispitanica i ispitanika (87,5%) procjenjuje se da su stekli ishode učenja navedene u kurikulumu nastavnog predmeta informatike.
***Ključne riječi:***
digitalna kompetencija; nastavni plan i program informatike; osnovno obrazovanje
# Raznolikost digitalnog okruženja u političkoj edukaciji
[![logo stoo2_1 (no).png](https://hub.ufzg.hr/uploads/images/gallery/2024-09/scaled-1680-/logo-stoo2-1-no.png)](https://hub.ufzg.hr/uploads/images/gallery/2024-09/logo-stoo2-1-no.png) **Odgoj danas za sutra:** **Premošćivanje jaza između učionice i realnosti** 3\. međunarodna znanstvena i umjetnička konferencija Učiteljskoga fakulteta Sveučilišta u Zagrebu Suvremene teme u odgoju i obrazovanju – STOO4 u suradnji s Hrvatskom akademijom znanosti i umjetnosti
##### **Lidija Eret** *Fakultet političkih znanosti, Sveučilište u Zagrebu* *lidija.eret@fpzg.hr*
**Sekcija - Odgoj i obrazovanje za digitalnu transformaciju****Broj rada: 39****Kategorija članka: Pregledni rad**
##### **Sažetak**
Kako se ubrzano razvijaju, digitalne tehnologije neprestano i sve intenzivnije unose novitete u svakodnevan život pa tako i u nastavno okruženje. Dugogodišnja istraživanja koja prate razvoj i napredak digitalnih tehnologija i njihovo uspješno implementiranje u nastavni proces pokazuju kako te promjene nisu nužno uvijek pozitivne. Nova didaktička stremljenja zahtijevaju i nove uloge, ali i kompetencije svih sudionika nastavnog procesa. Ovaj rad napravit će osvrt na novije izazove u nastavnom procesu političkog obrazovanja, a to su, primjerice, nastava na daljinu i upotreba umjetne inteligencije. Okosnice metodičkog promišljanja su svrsishodnost umjetne inteligencije u nastavi političkog i ideološkog obrazovanja, komunikacija i socijalizacija u novim metodičkim okruženjima i uspješnost ishoda učenja i poučavanja. Komparacija rezultata različitih istraživanja ovog metodičkog područja prikazat će prednosti i nedostatke noviteta u obrazovnim digitalnim tehnologijama, sugerirajući način kojim se može nastojati unaprijediti nastavu političkog obrazovanja u narednom vremenu.
***Ključne riječi***
digitalni mediji u političkoj edukaciji; nastava na daljinu u političkom obrazovanju; političko obrazovanje; prednosti i nedostaci digitalnih nastavnih tehnologija; umjetna inteligencija u političkom obrazovanju
**Uvodna razmatranja ** Unatrag tridesetak godina digitalni se mediji unapređuju eksponencijalnom brzinom, a područja pedagoške i didaktičke znanosti i struke pretpostavljaju logičnim odabirom nove multimedije za inovaciju metodike učenja i poučavanja. Ono što istraživanja pokazuju jesu prednosti i nedostaci tih inovativnih pokušaja. Uspješnost primjene novih digitalnih tehnologija ovisi o nekoliko čimbenika od kojih možemo izdvojiti neke: generacijske karakteristike sudionika nastavnog procesa, svrsishodnost novih medija u edukaciji i legitimnost na znanstvenoj osnovi (Eret, 2024). Da bismo to pobliže pojasnili, potrebno je razlučiti 'digitalne imigrante' i 'digitalne urođenike', generacije koje dijelimo na osnovu toga jesu li rođene u 80tim godinama prošlog stoljeća ili nakon njih (Spitzer, 2018). Potonje je esencijalno iz razloga što ova podjela generacija, između ostalog, označava i način odnosa prema digitalnim medijima: koristimo li ih isključivo kao alat ili pak *per se* jer su ponuđeni, pri čemu je bitna pristupačnost podacima dok se legitimnost i kvaliteta eventualno propitkuju naknadno. Time je veća uloga nastavnika koji mogu biti pripadnici obiju generacija, da implementaciju umjetne inteligencije, posebno u nastavni proces političke edukacije, shvate kao ozbiljan zadatak studioznog promišljanja oko novih metodičkih pristupa nastavnom procesu ovog znanstvenog područja. **Prihvaćanje umjetne inteligencije u nastavni proces** Obrazovna sredstva i pomagala usko su povezana s tekovinama tehnološkog napretka pa time inovacije koje se pojavljuju u upotrebi svakodnevnog života nađu i svoju nišu u znanstveno-istraživačkom području odgoja i obrazovanja. Stoga je logičan slijed da je umjetna inteligencija sa svojim inačicama (npr. ChatGPT) također postala predmetom empirije koja se odnosi na nastavne procese. Naravno da nas kod svake inovacije implementirane u nastavu zanima odgovor na pitanje: da ili ne? Možda je upravo dvojakost odgovora koji se pojavljuju u suvremenim istraživanjima ovog područja, paradoksalno, dobar pokazatelj da se problematici pristupa iz različitih kutova, da postoje jasne predodžbe o prednostima i nedostacima koja se rezultatima studija pokazuju i, konačno, smjernica za daljnje (bolje) djelovanje u struci i detaljnija naredna istraživanja (Eret, 2024). Mnoga i različita suvremena istraživanja pokazuju da implementacijom umjetna inteligencija pozitivno utječe na organizaciju, provedbu i ishode nastavnog procesa. Yu (2022) smatra da umjetna inteligencija doprinosi kreativnom, kvalitetnom i poticajnom nastavnom okruženju jer uključuje suvremene metode pristupa velikoj bazi podataka, interaktivno okruženje i način upotrebe nastavne tehnologije koja je primjerena mlađem uzrastu. Tako se u nekim istraživanjima stavlja naglasak na utjecaj umjetne inteligencije na motiviranost učenika i studenata za učenje i poučavanje, pri čemu su pokazani pozitivni rezultati (Pin-Chuan Lin i Chang, 2020). Pozitivan utjecaj umjetne inteligencije na ishode i kompetencije studenata pokazalo je i istraživanje koje su proveli Clarizia i suradnici (2018). No, proporcionalno navedenima, pojavljuju se i rezultati onih istraživanja koja unose sumnju u opravdanost primjene umjetne inteligencije u nastavni proces. Poimajući umjetnu inteligenciju kao softverski algoritam koji rješenja problema pronalazi u internetom dostupnim bazama podataka, a distribuira isto na jeziku upita korisnika (Bishop, 1994), neki znanstvenici u rezultatima svojih istraživanja potvrđuju sumnje u kredibilitet nastavne implementacije umjetne inteligencije, posebice kada se odnosi na političko i ideološko obrazovanje. Nastava ovog područja uključuje učenje o ekonomskim i političkim temama, kao i onima povezanima s vjerovanjima, tradicijom i kulturom suvremenih društava (Yun, Ravi i Jumani, 2023). Temeljne dvojbe oko ovog pitanja donosi Eke (2023) navodeći kako umjetna inteligencija ima tendenciju plasirati informacije koje su diskutabilnog kredibiliteta ili netočne. Što se tiče samog edukativnog aspekta, smatra kako pretjerano korištenje umjetne inteligencije u nastavi političkog i ideološkog obrazovanja dovodi u pitanje socijalizacijski aspekt i interaktivnost nastave, ključne za nastavne predmete ove vrste. Eke (2023) smatra i kako ovisnost studenata o tehnologijama umjetne inteligencije dovodi do smanjenja kvalitete obrazovnog iskustva kod učenika ili studenata političke i ideološke edukacije. **Politička edukacija, umjetna inteligencija i učenje na daljinu** Što se tiče područja istraživanja koje se odnosi na primjenu umjetne inteligencije u učenju na daljinu, svakako je potrebno spomenuti opsežnu studiju koju su proveli Dogan i suradnici 2023. godine. Slijedeći tradicionalnu bibliometrijsku analizu i sistematično analizirajući baze podataka, proveli su studiju koja je komparirala znanstvene radove koja su u recentnom vremenu obuhvaćala empirijska istraživanja, a koja su se odnosila na mogućnosti primjene umjetne inteligencije u učenju na daljinu. Pritom su dobiveni podaci pokazali da u studijama ove vrste prednjače Kina, Indija i Sjedinjene Američke Države pa su pritom uspoređeni podaci 276 recentnih znanstvenih publikacija. Istraživanje je polučilo tri okosnice (klastera): 1) kako primijeniti umjetnu inteligenciju u *online* nastavi, 2) kako algoritam umjetne inteligencije pomaže prepoznati, identificirati i predvidjeti ponašanje studenata/učenika te 3) kako umjetna inteligencija može pomoći u personaliziranom učenju (Dogan i sur., 2023). U prvom redu, umjetna inteligencija u suvremenoj nastavi je prijeko potrebna zbog problemski usmjerenih nastavnih situacija gdje je potrebno na specifičan i konkretan način detektirati i komparirati velik broj podataka (eng. *data mining*, *Big Data*). Tradicionalni algoritmi tako danas više ne mogu služiti svrsi jer nisu dovoljno sofisticirani obzirom su osmišljeni za jednu konkretnu svrhu ili cilj. Umjetna inteligencija ne samo da ima mogućnost prikupljanja, mjerenja i obrade pretraženih podataka, ona ih i oblikuje u zadani kontekst (Dogan i sur., 2023). Da bi se mogla utvrditi uspješnost primjene umjetne inteligencije u *online* nastavi, istraživači se posvećuju analizi okruženja odgajanika iz kognitivnog, bihevioralnog, socio-ekonomskog i povijesnog konteksta: nastoje utvrditi postojeće činjenice i predvidjeti optimalno okruženje za nove digitalne tehnologije. Tako će biti dvije pretpostavke za uvođenje umjetne inteligencije u nastavno okruženje. Prvo je deskriptivna analiza koja ukazuje na usvojene kompetencije učenika/studenta prethodno pristupanju digitalnom okruženju, a potom prediktorska analiza koja će se baviti podacima o studentu/učeniku koji su bihevioralne, povijesne (prethodno obrazovanje kroz specifične predmete i kolegije) i sociodemografske (uspješnost po razinama školovanja, uspješno završena određena razina obrazovanja) naravi (Salas-Pilco i sur., 2022). Pretpostavkom da će upotreba mobilne tehnologije, koja je velikim dijelom sastavni dio privatnog, poslovnog i akademskog života, biti logičan pokušaj osuvremenjivanja nastave političkog i ideološkog obrazovanja u visokom školstvu, Yu (2021) osmišljava nastavni model multimedijskog učenja i poučavanja koji uključuje i umjetnu inteligenciju. Kako Yu (2021) navodi, metodika nastave političkog i ideološkog obrazovanja na kineskim sveučilištima je vrlo zastarjela, što dovodi i do loših ishoda učenja i poučavanja iz tog područja edukacije. Kako bi se nastava osuvremenila i prilagodila studentskom načinu komunikacije i pretraživanju podataka, Yu (2021) model nastave političkog i ideološkog obrazovanja temelji na *online* multimediji. U navedenoj studiji slučaja Yu (2021) proučava koji će se i kakvi rezultati istraživanja pokazati ako nastavu političkog i ideološkog obrazovanja provodi *online* i potpomognutu umjetnom inteligencijom, a što uključuje nastavu na daljinu podržanu internetom, grupne diskusije i nastavne sadržaje 'na zahtjev' (dostupne *online* po potrebi studenta). Ovo istraživanje, za koje Yu (2021) sugerira da se ponovno primijeni na većem uzorku ispitanika kako bi se potvrdili benefiti ovakvog nastavnog pristupa, pokazalo je nekoliko pozitivnih zaključaka. Nastava na daljinu potpomognuta umjetnom inteligencijom nudi raznolike načine komunikacije i unapređuje socijalizaciju između svih sudionika nastavnog procesa (Slika 1), kako nastavnika i studenata tako i studenata međusobno. Ovakav *online* model nastave ideološkog i političkog obrazovanja pruža i udobnost personaliziranog učenja, a time i efektivnost nastavnih ishoda. Također, Yu (2021) ukazuje da je za ove pozitivne metodičke pomake u nastavi političkog i ideološkog obrazovanja potrebno prvo proširiti didaktičke vidike, a zatim osigurati tehnološku i sociološku infrastrukturu prvenstveno ulaganjem u nove digitalne nastavne tehnologije i osvješćivanjem da je za uspješnu nastavu potrebno stvoriti pozitivno (*online*) nastavno okruženje. ![Slika na kojoj se prikazuje tekst, snimka zaslona, dijagram, crta Opis je automatski generiran](https://hub.ufzg.hr/uploads/images/gallery/2025-05/embedded-image-1d4229hw.png) **Slika 1.** Multimedijski model komunikacije političke i ideološke nastave na daljinu (Yu, 2021, str. 5) Nastava političkog i ideološkog obrazovanja na daljinu, a primjenom umjetne inteligencije, u novije je vrijeme predmetom mnogih istraživanja. Zanimljivo je da dobiveni podaci mogu pokazati oprečnosti u poimanju kvalitete takve nastave, odnosno u procjeni doprinosi li ili ne umjetna inteligencija ishodima učenja i poučavanja. Zhong (2021) navodi da su studentske procjene uspješnosti nastave političkog i ideološkog obrazovanja provedene na ovaj način zapravo neopredijeljene ili vrlo niske. U istraživanju koje je provedeno na dva kineska sveučilišta pokazano je da će ovakvi modeli nastave imati utoliko bolju uspješnost ako će se nastavnici-istraživači više posvetiti osuvremenjivanju i kreativnosti u osmišljavanju novih metodičkih okvira nastave. Osim toga, studenti su u svojim procjenama navodili da je manjkavost *online* nastave upotrebom umjetne inteligencije mehanizam procjene ishoda učenja i poučavanja. Također, studenti su procijenili i kako uspješnost nastave političke i ideološke edukacije većim dijelom ovisi o individualnim kompetencijama i uvjerenjima studenata, a ne o ovakvom nastavnom modelu (Zhong, 2021). Cao i Huang (2023) u svom opsežnom istraživanju na velikom broju kineskih sveučilišta koje je uključivalo i velik uzorak ispitanika (studenata i nastavnika) ukazali su da suvremena stremljenja novim metodičkim modelima nastave političkog i ideološkog učenja i poučavanja mogu ukazati na negativne čimbenike koje uključuju kompetencije nastavnika vezane uz medijsku pismenost. Naime, njihovo je istraživanje pokazalo da su nastavnici, sudionici istraživanja, u nedovoljnoj mjeri pismeni u relaciji s upotrebnom umjetne inteligencije u nastavnom procesu i shodno tome teško pronalaze metodičke smjernice kako umjetnom inteligencijom oblikovati nastavni proces. Stoga nedovoljna osposobljenost za potrebu digitalnih medija uzrokuje anksioznost nastavnika, preveliku usmjerenost savladavanju mogućnosti upotrebe umjetne inteligencije, a što dovodi na smanjenu usredotočenost na nastavne sadržaje i, posredno, na opadanje uspješnosti ishoda učenja i poučavanja. **Dilema povezanosti digitalne tehnologije i akademskog nepoštenja** Suvremena istraživanja pokazuju da se pripadnici generacije 'digitalnih urođenika' u velikoj mjeri oslanjaju na digitalne medije kada je riječ o pretrazi informacija. Najčešće je tu riječ o internetskim izvorima, no problem je u tome što djeca i mladi ovog uzrasta rijetko propitkuju vjerodostojnost tih izvora, odnosno informacija koje plasiraju. Ako je riječ o pretragama koje se odnose na internetske tražilice, najčešće će odabrati one izvore koji su im prvi ponuđeni, ne tražeći dalje od internetskih stranica koje su, doslovce, jedna od prve tri ponuđene (Spitzer, 2018). Trend današnjeg vremena i, paralelno, uzrasta odgajanika u pretragama informacija koje su povezane sa školskim obvezama je svakako konzultiranje s umjetnom inteligencijom. Problemi koji se sve češće pojavljuju povezani su s pisanim uradcima koji su dio učeničkih i studentskih obveza. Ako bismo svaku tehnologiju u nastavi, a tako i onu inovativnu, povezivali s logičnim slijedom unapređenja metodike odgojno-obrazovnog procesa, tako bismo i umjetnu inteligenciju i njene varijante (npr. ChatGPT) trebali razmatrati kao alat koji služi bržem, boljem i jednostavnijem tijeku nekog nastavnog zadatka od načina i sredstava putem kojih se dotad izvodio. No, kultura djelovanja današnje djece i mladih tome predmnijeva specifičan *modus operandi*, a to je da je digitalna tehnologija dovoljno dobra sama po sebi. 'Digitalni urođenici' rijetko propitkuju je li informacija koju suvremene digitalne tehnologije nude točna i vjerodostojna (Spitzer, 2018). Tako u današnjem nastavnom procesu nerijetko dobivamo pisane uratke učenika i studenata koje je generirala umjetna inteligencija ili koji sadrže podatke dobivene pretragama internetskih izvora upitnog kredibiliteta. Problem s kojim se nose nastavnici na svim razinama školstva jesu informacije u tim pisanim podnescima koje su djelomično točne ili čak u potpunosti netočne. Popisi literature koje učenici i studenti prilažu svojim pisanim radovima, a koji bi se trebali odnositi na izvore koje su konzultirali pišući svoje tekstove, pokazuju se kao nepostojeće publikacije koje je generirala umjetna inteligencija. Tako se u svemu navedenome postavlja pitanje kako u novome digitalnom okruženju pristupiti onemogućavanju plagiranja učenika i studenata, a u prvom redu i kako njih same educirati o tome problemu čiji izvor sami ne prepoznaju. Iz istoga slijedi i drugačiji način pristupanja ocjenjivanju školskih (pisanih) obaveza učenika i studenata i sagledavanju vjerodostojnosti akademskog uspjeha odgajanika iz nove perspektive (Eke, 2023). ** ** **Zaključak** Novo metodološko okruženje nastave današnjeg vremena svakako je umjetna inteligencija i inačica (npr. ChatGPT) koje proizlaze iz ove tehnološke inovacije. Umjetna inteligencija, u tome smislu, nudi nove tehnološke poligone za oblikovanje i provođenje nastave na daljinu (Eret, 2024). U ovome trenutku već je znatan dio rezultata suvremenih istraživanja odgojno-obrazovnog procesa koji daju odgovore na pitanja koji su prednosti i nedostaci implementacije ove digitalne tehnologije u nastavu (Clarizia i sur., 2018; Dogan i sur., 2023; Eke, 2023). Nastava političkog i ideološkog obrazovanja poseban je predmet interesa ovog metodičkog područja jer se bavi učenjem i poučavanjem o ekonomskim i političkim temama, kao i onima povezanima s vjerovanjima, tradicijom i kulturom suvremenih društava (Yun i sur., 2023). Iako bi svako nastavno sredstvo tek trebalo poslužiti svrsi učinkovitijeg provođenja istog nastavnog procesa nego prethodno korištenim metodama, generacije 'digitalnih urođenika' se u (nove) tehnologije oslanjaju u tolikoj mjeri previše da time dovode u pitanje svoj akademski kredibilitet (Spitzer, 2018). Do toga dolazi iz razloga što umjetna inteligencija može ponuditi djelomično točne ili netočne informacije (Eke, 2023). Tako dolazimo do rezultata istraživanja koji pokazuju kako umjetna inteligencija doprinosi motivaciji, kreativnosti i uspjesima u nastavi političkog i ideološkog obrazovanja (Pin-Chuan Lin i Chang, 2020; Yu, 2021), ali i onih koji ukazuju na metodološke prepreke, jedne od kojih su i suvremene kompetencije tehnološke pismenosti sudionika nastavnog procesa (Cao i Huang, 2023) ili pak vjerodostojnost plasiranih podataka (Eke, 2023). U svakome slučaju, daljnja istraživanja područja primjene umjetne inteligencije u odgoju i obrazovanju svakako će biti dijelom stremljenja osuvremenjivanju i poboljšanju nastavnog procesa. **Literatura:** Cao, D., & Huang, L. (2023). Research on Ideological and Political Education Strategies in Translation Technology Courses in the Age of AI: Taking EC and CE Translation as an Example. *The Frontiers of Society, Science and Technology, 5*(18), 47-52. doi: 10.25236/FSST.2023.051808. Bishop, C. M. (1994). Neural networks and their applications. *Review of scientific instruments*, *65*(6), 1803-1832. doi: [https://doi.org/10.1063/1.1144830](https://doi.org/10.1063/1.1144830). Clarizia, F., Colace, F., Lombardi, M., Pascale, F., & Santaniello, D. (2018). Chatbot: An Education Support System for Student. U: A. Castiglione, F. Pop, M. Ficco, & F. Palmieri (ur.): *Cyberspace Safety and Security* (str. 292-299). Dogan, M. E., Dogan, T. G., & Bozkurt, A. (2023). The use of artificial intelligence (AI) in online learning and distance education processes: A systematic review of empirical studies. *Applied Sciences*, *13*(5), p. 3056. doi: [https://doi.org/10.3390/app13053056](https://doi.org/10.3390/app13053056) Eke, O. D. (2023). ChatGPT and the rise of generative AI: Threat to academic integrity?. *Journal of Responsible Technology*, 13, p. 100060. doi: https://doi.org/10.1016/j.jrt.2023.100060 . Eret, L. (2024). *Digitalni mediji. Kako unaprijediti nastavu*. Zagreb: TIM Press. Pin-Chuan Lin, M., & Chang, D. (2020). Enhancing post-secondary writers’ writing skills with a chatbot. *Journal of Educational Technology & Society*, *23*(1), 78-79. Salas-Pilco, S. Z., Xiao, K., & Hu, X. (2022). Artificial intelligence and learning analytics in teacher education: A systematic review. *Education Sciences*, *12*(8), p. 569. doi: [https://www.mdpi.com/2227-7102/12/8/569](https://www.mdpi.com/2227-7102/12/8/569) Spitzer, M. (2018). *Digitalna demencija: kako mi i naša djeca silazimo s uma*. Zagreb: Ljevak. Yu, H. (2021). Application Analysis of New Internet Multimedia Technology in Optimizing the Ideological and Political Education System of College Students. *Wireless Communications and Mobile Computing*, *2021*(1), p. 5557343. doi: [https://doi.org/10.1155/2021/5557343](https://doi.org/10.1155/2021/5557343) Yu, Y. (2022). Immersive learning method of ideological and political education under big data and artificial intelligence. *Computational Intelligence and Neuroscience, 2022*, 1-8. doi: [https://doi.org/10.1155/2022/4176595](https://doi.org/10.1155/2022/4176595). Yun, G., Ravi, R. V. i Jumani, A. K. (2023). Analysis of the teaching quality on deep learning-based innovative ideological political education platform. *Progress in Artificial Intelligence*, *12*(2), 179-185. Zhong, J. (2021). Exploring and researching ideological and political education of college students’ psychological quality for the development of artificial intelligence. *Mobile Information Systems*, *2021*(1), p. 2453385. doi: [https://doi.org/10.1155/2021/2453385](https://doi.org/10.1155/2021/2453385). # Stavovi nastavnika iz područja prirodnih znanosti o e-učenju
[![logo stoo2_1 (no).png](https://hub.ufzg.hr/uploads/images/gallery/2024-09/scaled-1680-/logo-stoo2-1-no.png)](https://hub.ufzg.hr/uploads/images/gallery/2024-09/logo-stoo2-1-no.png) **Odgoj danas za sutra:** **Premošćivanje jaza između učionice i realnosti** 3\. međunarodna znanstvena i umjetnička konferencija Učiteljskoga fakulteta Sveučilišta u Zagrebu Suvremene teme u odgoju i obrazovanju – STOO4 u suradnji s Hrvatskom akademijom znanosti i umjetnosti
##### **Barbara Popovac Tašev, Anna Alajbeg** *Faculty of Science, University of Split* bptasev@pmfst.hr
**Sekcija - Odgoj i obrazovanje za digitalnu transformaciju****Broj rada: 40****Kategorija: Izvorni znanstveni rad**
##### **Sažetak**
Cilj ovoga rada bio je ispitati i analizirati stavove nastavnika biologije, kemije, matematike i fizike o e-učenju s obzirom na spol, dob i vrstu škole u kojoj rade. U istraživanju su sudjelovali nastavnici iz različitih krajeva Republike Hrvatske (N=208). Za potrebe ovog istraživanja korištena je originalna Skala stavova o e-učenju kojom se ispituju stavovi nastavnika o izazovima e-učenja, prednostima e-učenja, korištenju računalnih sustava i preferencijama u pogledu inovacija e-učenja i korištenja računala u slobodno vrijeme. Upitnik je upotpunjen općim podacima o nastavnicima (spol, dob, nastavni predmet i škola u kojoj rade). Rezultatima istraživanja utvrđeno je da nastavnici imaju uglavnom pozitivne stavove o e-učenju. Nastavnici u odnosu na nastavnice daju nešto veću prednost korištenju računala za pripremu lekcija i radije se informiraju o tehnološkim inovacijama. Stariji nastavnici su nesigurniji u korištenju računala. Nastavnici kemije i biologije e-učenje smatraju izazovnijim, imaju negativniji stav o korištenju računalnih sustava, te manji interes za inovacije e-učenja i korištenja računala u odnosu na nastavnike matematike i fizike, ali se svi slažu da e-učenje ima prednosti u odnosu na druge metode poučavanja. Rezultati ovog istraživanja doprinose boljem razumijevanju prihvaćanja i korištenja e-učenja od strane nastavnika. Važno je da nastavnici budu spremni prilagoditi se novim okolnostima i tražiti mogućnosti za poboljšanje svojih vještina u e-učenju, koje su ključne za stvaranje kvalitetnijeg i suvremenijeg odgojno-obrazovnog procesa.
***Ključne riječi:***
e- učenje; izazovi e-učenja; nastava; nastavnici; prednosti e-učenja
**Uvod** U formalnom i neformalnom suvremenom obrazovanju e-učenje je postalo ključan oblik učenja. U okruženju za učenje obogaćenom tehnologijom, učenici postižu različite rezultate učenja na koje utječu dostupne tehnologije (Lin i sur., 2023). E-učenje je važan način stjecanja znanja, ne samo za učenike u školama i sveučilištima, već i za cjeloživotno učenje, odnosno one koji traže napredak u svom društvenom životu i na radnom mjestu (Zhang i sur., 2021). E-učenje se definira kao primjena informacijsko-komunikacijskih tehnologija (IKT) u procesu učenja, pri čemu su poučavatelj i učenik fizički odvojeni (Vuksanović, 2009). Ovaj oblik obrazovanja omogućava interaktivno učenje prilagođeno individualnim potrebama učenika, čime se osnažuje njihova autonomija i angažman (Ribarić, 2018). E-učenje se dijeli na različite razine, uključujući baze znanja, online potporu, asinkrono i sinkrono učenje, koje doprinose poboljšanju kvalitete obrazovnog procesa (Sinković i Kaluđerčić, 2006). Prema navedenim autorima za e-učenje olakšava i poboljšava proces učenja koristeći se računalom, internetom, te telekomunikacijama. Također navode da su aktivne četiri razine e-učenja: · baze znanja (*knowledge databases*), koje predstavljaju početnu razinu i primarno služe kao repozitoriji informacija. Iako same po sebi ne potiču aktivno učenje, opremljene su softverom koji omogućuje interaktivno pretraživanje, čime olakšavaju pristup i korištenje informacija u procesu učenja; · online potpora (*online support*) usmjerena je na interakciju i razmjenu znanja među sudionicima. Koriste se alati kao što su forumi, chat sobe i e-mail, koji omogućuju komunikaciju i dijeljenje informacija; · Asinkrono učenje (*asinchronous training*) omogućuje fleksibilno, samostalno učenje. Uključuje alate koji omogućuju pristup bazama znanja, forumima i drugim resursima u bilo koje vrijeme. Materijali za učenje mogu biti dostupni putem interneta ili na fizičkim medijima, a sve u skladu s vlastitim tempom i stilom svakog pojedinca; · Sinkrono učenje (*sinchronous training*) podrazumijeva učenje u stvarnom vremenu, uz aktivno sudjelovanje mentora (nastavnika). Takav oblik učenja omogućuje neposrednu komunikaciju između učenika i nastavnika, a često se provodi putem alata za online komunikaciju. Istraživanja pokazuju da e-učenje predstavlja najbolju alternativu u situacijama s ograničenim pristupom obrazovanju (Kisanga i Ireson, 2016). E-učenje ne samo da poboljšava učinkovitost obrazovanja, već i omogućava prilagodbu vremena i stila učenja (El-Sabagh, 2021). Prema istraživanju Karimi i suradnika (2023) stavovi nastavnika prema e-učenju ključni su za njegovu uspješnu integraciju u obrazovni sustav. Nastavnici koji su već upoznati s IKT-om pokazuju veću sigurnost u korištenju digitalnih alata (Pynoo i sur., 2012). Osim toga, globalizacija obrazovanja i porast programa učenja na daljinu ukazuju na potrebu za pristupom e-učenju koji može prevladati fizičke i vremenske barijere (Van Raaij i Schepers, 2008). Ovaj rad fokusira se na stavove nastavnika prirodnih znanosti (biologija, kemija, matematika i fizika) o e-učenju. Prirodne znanosti često zahtijevaju specifične nastavne metode i pristupe, pa je važno razumjeti kako nastavnici iz navednih nastavnih predmeta percipiraju mogućnosti i izazove e-učenja. **Pregled literature** Nove tehnologije mijenjaju klasične modele nastavnih procesa što sigurno predstavlja izazov kako za nastavnike, tako i za učenike. Nalazi dosadašnjih istraživanja (Nouraey i Al-Badi, 2023) ukazuju da su najveći izazovi povezani s e-učenjem pronalaženje prikladnog mjesta za poučavanje i učenje (bez ometanja članova obitelji), infrastruktura i tehnička oprema, održavanje kontakata i dogovori između nastavnika i učenika/studenata, neprepoznavanje truda za pripremu predavanja, provedba ocjenjivanja, razvoj nastavnih materijala. Nadalje, zabilježeno je da je e-učenje dosadnije i zamornije, zbog nedostatka izravnog nadzora i interakcije licem u lice, zbog čega su učenici i nastavnici nerijetko rastreseni. Nastava je obično kraća u usporedbi s izravnom nastavom zbog nedostatka nadzora i motivacije. Sljedeći problem je i obiteljsko okruženje koje često dovodi do nedostatka pozornosti od strane nastavnika i studentima. Na primjer, učenici i studenti bi mogli koristiti svoje telefone, a da nisu praćeni od strane roditelja i nastavnika (Nouraey i Al-Badi, 2023). S druge strane, stvaranje sigurnog internetskog prostora olakšava suradnju i pruža priliku za učenje korištenja različitih tehnologija čime se može smanjiti jaz digitalne podijeljenosti i obrazovne nejednakosti, pružiti učinkovito iskustvo e-učenja i stvoriti zajednička kultura. Dakle, uspješna implementacija sustava e-učenja u upravljanju znanjem i obrazovnim potrebama zahtijeva identifikaciju tehničkih, kulturnih i umjetničkih izazova e-učenja. Prevladavanje tih izazova zahtijeva stvaranje tehnološke infrastrukture i usvajanje standarda te korištenje iskustava razvijenih zemalja u vezi s e-učenjem. (Shahmoradi i sur., 2018). Sintezom nalaza različitih istraživanja Bakkar i Ziden (2023) navode da e-učenje karakterizira fleksibilnost i vremenska učinkovitost, omogućavajući učenicima da pristupe obrazovnim materijalima kada im odgovara. Ovaj pristup također obuhvaća raznolike i inovativne digitalne alate, poboljšavajući angažman i interaktivnost. Naime, e-učenje je prilagodljivo različitim stilovima učenja, nudeći resurse poput interaktivnih modela i multimedijskih sadržaja. Još jedna značajna prednost je sposobnost širenja obrazovnih pristupa, dostupnost korisnicima u udaljenim ili nedovoljno obrazovno depriviranim područjima. Nadalje, e-učenje učenje potiče autonomiju i samousmjereno učenje, neophodno za razvoj kritičkog mišljenja i vještine rješavanja problema (Bakkar I Ziden, 2023). Prema istraživanju Svaline (2022) učitelji često koriste računalo tijekom nastavnog procesa i za pripremu nastavnih materijala, nemaju strah od tehnologije što pokazuje pozitivan stav, ali dio njih iskazuje skeptičnost prema prednostima e-učenja u rješavanju nekih uobičajenih obrazovnih problema. Unatoč skeptičnosti koja može biti posljedica nedovoljne obuke ili iskustva u primjeni tehnologije u nastavi, većina učitelja je intrinzično motivirana za upotrebu e-učenja, jer prepoznaju da se ono ističe među novim pristupima učenju, kako u formalnom tako i u neformalnom kontekstu. Nadalje, nastavnici su otvoreni za korištenje IKT-a u svrhu poučavanja te se osjećaju ugodno u poučavanju i učenju novih sadržaja. Vjeruju da je poučavanje pomoću IKT-a lakše, može donijeti nove mogućnosti organiziranja nastave i učenja, otvoriti neograničene mogućnosti koje prije nisu bile razmatrane, te povećati pristup obrazovanju i kvalitetu materijala za e-učenje. Prema istraživanju o integriranom sustavu za e-učenje koje su proveli Klasnić i sur. (2014), većina studenata (budućih nastavnika) se slaže s idejom povećane integracije navedenog sustava u tradicionalnu nastavu. Oni vjeruju da bi svi nastavnici trebali biti upoznati s korištenjem i mogućnostima sustava, te ga više upotrebljavati u svim područjima znanosti. Istraživanje je pokazalo da su studenti nešto više zadovoljni kvalitetom nego količinom korištenja integriranog sustava. Podržavaju ideju poboljšanja kvalitete nastave putem e-učenja. Smatraju da je praktičnije preuzeti nastavne materijale u e-obliku umjesto zapisivanja predavanja, te da korištenje ovog sustava potiče nastavnike na bolje sistematiziranje gradiva, što olakšava učenje. Oni ne smatraju integrirani sustav teretom, već korisnim alatom za poboljšanje kvalitete obrazovanja. TAM (*technology acceptance model*) je okvir koji objašnjava prihvaćanje tehnologije od strane korisnika (Davis, 1989). TAM sugerira da namjera korisnika da koriste tehnologiju ovisi o tri čimbenika: percipiranoj korisnosti, percipiranoj lakoći korištenja i stavu prema korištenju (Davis, 1989). Namjera je glavni ishod u TAM-u, koji predviđa stvarnu upotrebu (Davis, 1989). Percipirana korisnost je način na koji korisnici vjeruju tehnologiji, odnosno da će poboljšati njihovu izvedbu, dok je percipirana jednostavnost upotrebe ono što korisnici vjeruju da će korištenje tehnologije zahtijevati malo truda. I percipirana korisnost i percipirana jednostavnost korištenja utječu na stav korisnika prema korištenju tehnologije (Wu i sur., 2024). Plantak Vukovac i sur. (2018) navode da osobe mlađe životne dobi pokazuju najveći interes za korištenje, a ujedno i uvođenje novih proizvoda tehnologije (igrifikacije) u svom radu jer nastavu čine zanimljivijom te povećava motivaciju učenika ili studenata. S druge strane, neki nastavnici jednostavno nisu upoznati s tim što neka nova tehnologija podrazumijeva, dok drugi smatraju da je to samo prolazni trend i ne žele ulagati vrijeme u izradu takvih materijala. Razlozi za to su uglavnom nedostatak znanja o konceptu tehnologije i nedostatak vremena za pripremu materijala koji su prilagođeni istom, a uglavnom se odnosi na starije generacije koji pokazuju manju sklonost korištenju novih tehnologija. Iako smo pregledom literature utvrdili da postoje brojna istraživanja o e-učenju, jako malo se istraživanja bavilo stavovima nastavnika prema e-učenju. Posebno nedostaje istraživanja kojima se ispituje stav nastavnika iz prirodnih predmeta, što je ujedno i namjera ovog rada. **Metodologija istraživanja** ***Predmet i cilj istraživanja*** Cilj ovog istraživanja je ispitati i analizirati ulogu nekih sociodemografskih varijabli (spol, dob) na razmišljanja nastavnika o e-učenju i novim načinima poučavanja. Također, ovim istraživanjem želimo utvrditi postoji li statistički značajna razlika u stavovima o e-učenju između nastavnika pojedinih predmeta, kao i ispitati postoje li razlike u stavovima ovisno o ustanovi u kojoj se održava nastava. U skladu s ciljem istraživanja postavljene su slijedeće hipoteze: H1: Ne postoji statistički značajna razlika u stavovima nastavnika o e-učenju s obzirom na spol. H2: Ne postoji statistički značajna razlika u stavovima nastavnika o e-učenju s obzirom na dob. H3: Ne postoji statistički značajna razlika u stavovima nastavnika o e-učenju s obzirom na predmet kojeg predaju. H4: Ne postoji statistički značajna razlika u stavovima nastavnika o e-učenju s obzirom na ustanovu u kojoj predaju. ***Ispitanici*** U ovom istraživanju sudjelovali su nastavnici i profesori koji drže nastavu iz kemije, biologije, matematike i fizike, te su iz različitih mjesta Republike Hrvatske. Ukupan broj ispitanika bio je N=208, od toga je (n=182) nastavnica i (n=26) nastavnika. Broj ispitanika prema ustanovi u kojoj rade bio je sljedeći: osnovna škola n=80, srednja škola n=84, visokoobrazovne ustanove n=44. Od ukupnog broja ispitanika nastavnika kemije bilo je n=71, biologije n=35, matematike n=74 i fizike n=28. Najveći broj ispitanika bio je iz Splitsko-dalmatinske županije n=117. Uzorak je bio neslučajan. ***Način prikupljanja podataka*** Prikupljanje podataka odvijalo se putem alata Google Forms. Upitnik je poslan na e-mail adrese nastavnika iz prirodnih predmeta koji rade u školama i fakultetima iz različitih mjesta Republike Hrvatske. Ispitanicima je dana uputa za ispunjavanje upitnika uz napomenu da je sudjelovanje anonimno i dobrovoljno, te da u svakom trenutku mogu odustati od ispunjavanja upitnika. Upitnik je dobio odobrenje Etičkog povjerenstva Prirodoslovno-matematičkog fakulteta u Splitu. Podatci su se prikupljali tijekom svibnja i lipnja 2024. godine. ***Mjerni instrument*** Za potrebe ovog istraživanja korišten je Upitnik koji se sastojao od dva dijela. Prvi dio sastojao se od pitanja koji se odnose na sociodemografska obilježja ispitanika, a to su spol, godine života, ustanova u kojoj ispitanik radi, predmet iz kojeg ispitanik drži nastavu i u kojoj županiji radi ispitanik. U drugom dijelu upitnika korišten je izvorni mjerni instrument *Test of e-Learning Related Attitudes (TeLRA) scale* (Kisanga i Ireson, 2016). Navedena skala služi za mjerenje stavova nastavnika o e-učenju. Skala se sastoji od 4 subskale. Prva subskala pod nazivom *Izazovi e-učenja* sastojala se od 12 čestica, druga subskala pod nazivom *Prednosti e-učenja* sastojala se od 9 čestica, treća subskala pod nazivom *Stav o korištenju računalnih sustava* sastojala se od 6 čestica i četvrta subskala pod nazivom *Individualne preferencije u pogledu inovacija e-učenja i korištenja računala u slobodno vrijeme* sastojala se od 9 čestica. Skala je Likertovog tipa gdje su ponuđeni odgovori bili: 1-u potpunosti se ne slažem, 2-ne slažem se, 3-slažem se, 4-u potpunosti se slažem. Provjerena je i pouzdanost Cronbach alpha testom, te je utvrđeno da je α>0.70 za svaku subskalu **Rezultati** Dobiveni podatci su obrađeni u statističkom programu IBM SPSS (verzija 26). ***Deskriptivna statistika*** S obzirom da smo u ovom istraživanju htjeli ispitati stavove nastavnika o e-učenju, provedena je deskriptivna analiza, a rezultati su prikazani u Tablici 1. Tablica 1 Deskriptivni pokazatelji stavova nastavnika o e-učenju
Subskala N Min Max Srednja vrijednost Medijan Mod
Izazovi e-učenja 208 1 4 2,49 2,42 2,33
Prednosti e-učenja 208 1 4 2,45 2,44 2,44
Stav o korištenju računalnih sustava 208 1 4 2,13 2,17 2,17
Individualne preferencije 208 1 4 2,59 2,78 2,89
Deskriptivnom analizom odgovora za čestice u subskali Izazovi e-učenja utvrđeno je da se srednja vrijednost odgovora kreće oko 2,5 što nam pokazuje da nastavnici smatraju da im e-učenje predstavlja određeni izazov. U subskali Prednosti e-učenja srednja vrijednost odgovora kreće se također oko 2,5, što nam sugerira da se nastavnici uglavnom slažu da e-učenje ima prednosti u odnosu na druge metode učenja. Treća subskala Stav o korištenju računalnih sustava ima srednju vrijednost nižu od prethodne dvije i kreće se oko 2 što nam pokazuje da nastavnici uglavnom imaju pozitivan stav o korištenju računalnih sustava. Četvrta subskala Individualne preference u pogledu inovacija e-učenja i korištenja računala u slobodno vrijeme ima srednju vrijednost odgovora između 2,5 i 3 što nam sugerira da nastavnici imaju dovoljno interesa za inovacije i korištenje računala. ***Inferencijalna statistika*** **Stavovi nastavnika o e-učenju s obzirom na spol** Kako bi se utvrdile razlike između nastavnika u stavovima o e-učenju s obzirom na spol korišten je Mann-Whitney U test, a rezultati su prikazani u Tablici 2. Tablica 2 Rezultati Mann-Whitney U testa o stavovima nastavnika o e-učenju s obzirom na spol
Izazovi e-učenja Prednosti e-učenja Stav o korištenju računalnih sustava Individualne preferencije
Mann-Whitney U 2314,500 2281,500 2249,500 2282,000
Z -,143 -,324 -,442 -,335
p ,886 ,746 ,658 ,738
Dobivenim rezultatima utvrđeno je da nema statistički značajne razlike u niti jednoj subskali stavova o e-učenju s obzirom na spol (p>0.05). Kako bi se utvrdile razlike u razmišljanjima nastavnika o e-učenju za sve čestice u skali s obzirom na spol, također je korišten Mann-Whitney U test za nezavisne uzorke (Tablica 3). Tablica 3 Rezultati Mann-Whitney U testa za čestice kojima je p<0.05
Prednosti e-učenja \[Radije koristim računalo za pripremu lekcija\] Individualne preferencije \[Volim čitati časopise o novim tehnološkim inovacijama\] Individualne preferencije \[Volim poučavati koristeći računalo\]
Mann-Whitney U 1854,000 1602,000 1732,500
Z -1,987 -2,876 -2,440
p ,047 ,004 ,015
Rezultati pokazuju da se s obzirom na spol pojavljuje statistički značajna razlika u tri čestice i to u subskali Prednosti e-učenja “*Radije koristim računalo za pripremu lekcija”* (p=0.047) i subskali Individualne preference “*Volim čitati časopise o novim tehnološkim inovacijama*” (p=0.004), te *„Volim poučavati koristeći računalo“* (p=0.015). U Tablici 4 prikazani su rezultati srednjeg ranga za navedene tri čestice. Tablica 4 Analiza vrijednosti srednjeg ranga za navedene čestice
Čestica Spol N Srednji rang
Prednosti e-učenja \[Radije koristim računalo za pripremu lekcija\] Žensko 182 101,69
Muško 26 124,19
Ukupno 208
Individualne preferencije \[Volim čitati časopise o novim tehnološkim inovacijama\] Žensko 182 100,30
Muško 26 133,88
Ukupno 208
Individualne preferencije \[Volim poučavati koristeći računalo\] Žensko 182 107,98
Muško 26 80,13
Ukupno 208
Analiza vrijednosti srednjeg ranga za navedene čestice pokazuje da nastavnici u odnosu na nastavnice radije koriste računalo za pripremu lekcija i više vole čitati časopise o novim tehnološkim inovacijama, dok nastavnice radije poučavaju koristeći računalo. **Stavovi nastavnika o e-učenju s obzirom na dob** Kako bi se utvrdile razlike između nastavnika o stavovima u vezi korištenja računalnih sustava s obzirom na dob, korišten je Kruskal-Wallis test za nezavisne uzorke (Tablica 5). Tablica 5 Rezultati Kruskal-Wallis testa za svaku subskalu
Izazovi e-učenja Prednosti e-učenja Stav o korištenju računalnih sustava Individualne preferencije
Kruskal-Wallis H 1,846 2,908 4,850 ,615
df 3 3 3 3
Asymp. Sig. ,605 ,406 ,183 ,893
Dobivenim rezultatima utvrđeno je da nema statistički značajne razlike u niti jednoj subskali stavova o e-učenju s obzirom na spol (p>0.05). Kako bi se utvrdile razlike u razmišljanjima nastavnika o e-učenju za sve čestice u skali s obzirom na dob, također je korišten Kruskal-Wallis test za nezavisne uzorke (Tablica 6). Tablica 6 Rezultati Kruskal-Wallis testa za čestice kojima je p<0.05
Stav o korištenju računalnih sustava \[Bit će mi teško savladati alate e-učenja\] Stav o korištenju računalnih sustava \[Često griješim kada koristim računalo\]
Kruskal-Wallis H 18,125 15,833
df 3 3
p ,000 ,001
Rezultati pokazuju da se s obzirom na godine života pojavljuje statistički značajna razlika u stavovima o korištenju računalnih sustava i to u dvije čestice “*Bit će mi teško savladati alate e-učenja*” (p=0.000) i “*Često griješim kada koristim računalo*” (p=0.001). U Tablici 7 prikazani su rezultati srednjeg ranga za navedene dvije čestice. Tablica 7 Analiza vrijednosti srednjeg ranga za navedene čestice
Čestica Godine života N Srednji rang
Stav o korištenju računalnih sustava \[Bit će mi teško savladati alate e-učenja\] ≤ 35 42 79,26
36 - 45 62 100,17
46 - 55 70 112,01
≥ 56 34 128,10
Total 208
Stav o korištenju računalnih sustava \[Često griješim kada koristim računalo\] ≤ 35 42 78,50
36 - 45 62 109,08
46 - 55 70 107,74
≥ 56 34 121,59
Total 208
Analiza vrijednosti srednjeg ranga za navedene čestice pokazuje da nastavnici mlađe životne dobi imaju pozitivniji stav prema korištenju računala, odnosno, lakše savladavaju alate e-učenja, te manje griješe kod korištenja računala. * * **Stavovi nastavnika o e-učenju s obzirom na predmet kojeg predaju** Kako bi se utvrdile razlike između nastavnika u stavovima o e-učenju s obzirom na predmet kojeg predaju korišten je Kruskal-Wallis test za nezavisne uzorke, a rezultati su prikazani u Tablici 8. Tablica 8 Rezultati Kruskal-Wallis testa o stavovima nastavnika o e-učenju s obzirom na predmet kojeg predaju
Izazovi e-učenja Prednosti e-učenja Stav o korištenju računalnih sustava Individualne preferencije
Kruskal-Wallis H 8,180 1,876 5,267 6,153
df 3 3 3 3
p ,042 ,598 ,153 ,104
Rezultati pokazuju da postoji statistički značajna razlika (p>0.05) u subskali Izazovi e-učenja s obzirom na predmet iz kojeg nastavnik drži nastavu. U Tablici 9. prikazani su rezultati srednjih rangova za navedene subskale. Tablica 9 Analiza vrijednosti srednjeg ranga za subskale
Subskala Predmet N Srednji rang
Izazovi e-učenja kemija 71 106,50
biologija 35 118,11
matematika 74 104,70
fizika 28 78,27
Ukupno 208
Prednosti e-učenja kemija 71 109,62
biologija 35 104,86
matematika 74 97,86
fizika 28 108,61
Ukupno 208
Stav o korištenju računalnih sustava kemija 71 107,91
biologija 35 116,10
matematika 74 103,02
fizika 28 85,27
Ukupno 208
Individualne preferencije kemija 71 101,10
biologija 35 92,39
matematika 74 106,02
fizika 28 124,25
Ukupno 208
Analiza vrijednosti srednjeg ranga za subskale (Tablica 9) pokazuje da nastavnici kemije i biologije e-učenje smatraju izazovnijim, imaju negativniji stav o korištenju računalnih sustava, te manji interes za inovacije e-učenja i korištenja računala u odnosu na nastavnike matematike i fizike. Kako bi se utvrdile razlike između nastavnika u subskali Izazovi e-učenja s obzirom na predmet korišten je Krukal-Wallis test, a rezultati su prikazani u Tablici 10. Tablica 10 Rezultati Kruskal-Wallis testa za čestice kojima je p<0.05
Izazovi e-učenja \[E-učenje povećava društvenu izolaciju učenika\] Izazovi e-učenja \[Interakcija s računalnim sustavom često je frustrirajuća\] Izazovi e-učenja \[Metoda licem u lice više je usmjerena na učenika nego e-učenje\]
Kruskal-Wallis H 8,388 11,762 9,013
df 3 3 3
p ,039 ,008 ,029
Rezultati pokazuju da se s obzirom na predmet koji nastavnik predaje pojavljuje statistički značajna razlika u tri čestice i to „E-učenje povećava društvenu izolaciju učenika“ (p=0.039), „Interakcija s računalnim sustavom često je frustrirajuća“ (p=0.008) i „Metoda licem u lice više je usmjerena na učenika nego e-učenje“ (p=0.029). U Tablici 11 prikazani su rezultati srednjeg ranga za navedene tri čestice. Tablica 11 Analiza vrijednosti srednjeg ranga za navedene čestice
Predmet N Srednji rang
Izazovi e-učenja \[E-učenje povećava društvenu izolaciju učenika\] kemija 71 110,11
biologija 35 110,74
matematika 74 106,86
fizika 28 76,21
Ukupno 208
Izazovi e-učenja \[Interakcija s računalnim sustavom često je frustrirajuća\] kemija 71 102,04
biologija 35 118,97
matematika 74 111,30
fizika 28 74,66
Ukupno 208
Izazovi e-učenja \[Metoda licem u lice više je usmjerena na učenika nego e-učenje\] kemija 71 106,46
biologija 35 100,67
matematika 74 114,42
fizika 28 78,11
Ukupno 208
Analiza vrijednosti srednjeg ranga za navedene čestice pokazuje da nastavnici kemije i biologije e-učenje smatraju izazovnijim u odnosu na nastavnike matematike i fizike. **Stavovi nastavnika o e-učenju s obzirom na ustanovu u kojoj rade** Kako bi se utvrdile razlike u stavovima nastavnika koji drže nastavu u različitim ustanovama o e-učenju, korišten je Kruskal-Wallis test za nezavisne uzorke (Tablica 12). Tablica 12 Rezultati Kruskal-Wallis testa o stavovima nastavnika o e-učenju s obzirom na ustanovu
Izazovi e-učenja Prednosti e-učenja Stav o korištenju računalnih sustava Individualne preferencije
Kruskal-Wallis H 2,558 1,362 ,229 4,009
df 2 2 2 2
p ,278 ,506 ,892 ,135
Dobivenim rezultatima utvrđeno je da nema statistički značajne razlike u niti jednoj subskali stavova o e-učenju s obzirom na ustanovu u kojoj nastavnik radi (p>0.05). Iako Kruskal-Wallis test nije pokazao statistički značajne razlike, analizom vrijednosti srednjeg ranga pruža se dodatni uvid u stavove nastavnika iz pojedinih ustanova (Tablica 13). Dobivenim podacima utvrđeno je da se nastavnici osnovnih škola najviše susreću sa izazovima e-učenja. Nastavnici srednjih škola su najviše skloni individualnim preferencijama. Iako razlike postoje, nisu statistički značajne, ali mogu biti korisne za razumijevanje specifičnih potreba i perspektiva nastavnika u različitim ustanovama. Tablica 13 Analiza vrijednosti srednjeg ranga za subskale
Ustanova N Srednji rang
Izazovi e-učenja osnovna škola 80 111,53
srednja škola 84 100,97
visokoobrazovna ustanova 44 95,91
Ukupno 208
Prednosti e-učenja osnovna škola 80 102,05
srednja škola 84 109,70
visokoobrazovna ustanova 44 99,03
Ukupno 208
Stav o korištenju računalnih sustava osnovna škola 80 102,83
srednja škola 84 104,37
visokoobrazovna ustanova 44 107,78
Ukupno 208
Individualne preferencije osnovna škola 80 95,89
srednja škola 84 112,34
visokoobrazovna ustanova 44 105,18
Ukupno 208
**Rasprava** Ovim istraživanjem ispitani su stavovi o e-učenju nastavnika iz područja prirodnih znanosti s područja cijele Republike Hrvatske koji održavaju nastavu na tri razine obrazovanja. Kako bi se testirala prva hipoteza, proveden je neparametrijski Mann-Whitney U test za nezavisne uzorke koji se odnosio na sve četiri subskale koji je pokazao da nema statistički značajne razlike u pogledu e-učenja u odnosu na spol. Analizom razlika u odgovorima na sve čestice u skali u odnosu na spol, zabilježena je statistički značajna razlika za tri čestice i to „Radije koristim računalo za pripremu lekcija“ i „Volim čitati časopise o novim tehnološkim inovacijama“ i „Volim poučavati koristeći računalo“. Iz rezultata ove analize možemo zaključiti da se prva hipoteza uglavnom prihvaća. Istraživanje je pokazalo da nastavnici i nastavnice imaju slične stavove prema korištenju tehnologije u nastavi (Albert i Johnson, 2011). Ipak, muškarci i dalje imaju malo povoljnije stavove prema tehnologiji općenito (Cai i sur., 2017). Važno je napomenuti da su spolne razlike u samopouzdanju u korištenju tehnologije minimalne (Cai i sur., 2017). Studije o spolnim razlikama u stavovima prema tehnologiji često imaju nedosljedne rezultate, što otežava donošenje čvrstih zaključaka (Šabić i sur., 2022). Prema istraživanju Agboola (2006) rezultati upućuju na zaključak da spol ima značajan utjecaj na percepciju povjerenja u e-učenje, pri čemu muški ispitanici pokazuju veće povjerenje u e-učenje u usporedbi sa ženskim ispitanicama (ispitanici su bili akademski predavači (uglavnom humanističkih i prirodnih znanosti) na Međunarodnom islamskom sveučilištu u Maleziji), dok je prema Ramírez-Correa i sur. (2015), korištenje i namjera korištenja platformi za e-učenje veća među ženama, što ukazuje na smanjenje tradicionalne rodne razlike u usvajanju informacijskih tehnologija (ispitanici su bili studenti marketinga i poslovnog upravljanja iz Španjolske i studenti na tečajevima inženjerstva iz Čilea). Za testiranje druge hipoteze proveden je prvo neparametrijski Kruskal-Wallis test za nezavisne uzorke. Analizom razlika u odgovorima na sve čestice u skali u odnosu na dob, zabilježena je statistički značajna razlika za dvije čestice u subskali Stav o korištenju računalnih sustava, „Bit će mi teško savladati alate e-učenja“ i „Često griješim kada koristim računalo“. Iz rezultata ovih analiza možemo zaključiti da se druga hipoteza djelomično prihvaća. Ovakvi rezultati su u skladu s dosadašnjim istraživanjima (Murphy i Greenwood, 1998; Šabić i sur., 2022) u kojima je utvrđeno da mlađi nastavnici pokazuju znatno višu razinu povjerenja od starijih u korištenje računala u nastavi, a ujedno, da u usporedbi sa svojim studentima, nisu dobro obučeni i adekvatno izloženi IKT alatima. Ujedno pokazalo se značajnim i vrsta škole u kojoj nastavnik radi, percipiranoj tehničkoj i stručnoj podršci za korištenje ICT-a u školi te učestalosti korištenja računalnih programa u nastavi. Kako bi se testirala treća hipoteza, a koja govori o odnosu nastavnika pojedinih predmeta prema njihovim stavovima o e-učenju prvo je proveden neparametrijski Kruskal-Wallis test za nezavisne uzorke koji se odnosio na sve četiri subskale. Ovaj test pokazao je da ima statistički značajne razlike u subskali Izazovi e-učenja, a analizom srednjeg ranga za sve subskale i srednjeg ranga za tri čestice subskale Izazovi e-učenja, može se reći da nastavnici kemije i biologije e-učenje smatraju izazovnijim, imaju negativniji stav o korištenju računalnih sustava, te manji interes za inovacije e-učenja i korištenja računala u odnosu na nastavnike matematike i fizike. Slijedom navedenog Hipoteza 3 se djelomično prihvaća. Ovakvi rezultati nisu u skladu s istraživanjem koje je proveo Bawaneh (2021) čiji uzorak je uključivao 116 studenata prirodoslovnog fakulteta na Sveučilištu Imam Abdulrahman Bin Faisal iz Istočne provincije u Saudijskoj Arabiji, od čega 33 studenta fizike, 38 kemije, 37 biologije i 8 studenata matematike, a kojim je utvrđeno da nema statistički značajne razlike kod stave studenata različitih specijalnosti prema korištenju e-učenja i virtualne nastave. Rezultati istraživanja su pokazali da s obzirom na radnu ustanovu, ne postoje statistički značajne razlike u stavovima nastavnika prema e-učenju, čime se potvrđuje četvrta hipoteza. Iako Kruskal-Wallis test nije pokazao statistički značajne razlike, analizom srednjih rangova može se primijetiti da nastavnici osnovnih škola iskazuju veće poteškoće s izazovima e-učenja, dok nastavnici srednjih škola pokazuju veću naklonost prema individulanim preferencijama. Ovaj nalaz se može objasniti time da osnovne škole mogu imati ograničen pristup tehnologiji i manje resursa za podršku e-učenju. To može otežati implementaciju e-učenja i stvoriti dodatne izazove za nastavnike. Prema istraživanju **Voogta i sur. (2013),** nastavnici u osnovnoj školi često se suočavaju s izazovima u integraciji tehnologije u nastavu zbog nedostatka vremena, resursa i podrške. Učenici u srednjoj školi i visokom obrazovanju obično imaju više iskustva s korištenjem tehnologije i veću samostalnost u učenju. To olakšava implementaciju e-učenja i omogućuje nastavnicima da se fokusiraju na stvaranje interaktivnog i angažirajućeg sadržaja (**Hew i Brush, 2007).** ***Prednosti i ograničenja istraživanja*** Ovo istraživanje ima nekoliko ograničenja. Jedno od njih je vrijeme samog provođenja upitnika. Naime, upitnik se provodio pred kraj nastavne godine kada su svi nastavnici i profesori već svakako dodatno opterećeni radnim obvezama, a sigurno i zasićeni popunjavanjem raznih anketa tijekom godine. Istraživanje je rađeno na nivou Republike Hrvatske, ispitanici su iz gotovo svih županija, ali je zastupljenost pojedinih županija izuzetno niska, te se preporučuje da se budućim istraživanjima dodatno aktiviraju ispitanici iz tih županija. Ovaj mjerni instrument izvorno je napisan na engleskom jeziku, pa je možda u prijevodu došlo do nejasno prevedene čestice za ispitanike, a što je za posljedicu imalo slabiju pouzdanost mjernog instrumenta. Istraživanje se fokusiralo na stavove nastavnika prirodnih znanosti koji rade na tri razine obrazovanja (osnovna, srednja i visoko obrazovanje). U literaturi nema mnogo radova koji se bave ovom specifičnom populacijom, pa ovo istraživanje doprinosi boljem razumijevanju korištenja e-učenja u nastavi prirodnih znanosti. Istraživanje je pokazalo da pozitivni stavovi nastavnika prema e-učenju dovode do češće i kvalitetnije upotrebe e-učenja u nastavi. Ovaj nalaz je važan jer potiče na razvoj strategija za poboljšanje stavova nastavnika prema e-učenju. ** ** **Zaključak** Rezultati ovog istraživanja su pokazali da ispitanici imaju uglavnom pozitivne stavove o e-učenju neovisno o spolu, dobi, predmetu iz kojeg drže nastavu ili ustanovi u kojoj rade. Rezultati su pokazali da nastavnici u odnosu na nastavnice daju nešto veću prednost korištenju računala za pripremu lekcija i radije se informiraju o tehnološkim inovacijama. Što se tiče životne dobi, stariji nastavnici su nešto nesigurniji u korištenju računala u odnosu na mlađe kolege. Nešto veća razlika u stavovima prema e-učenju se pokazala kod nastavnika pojedinih predmeta, odnosno, nastavnici kemije i biologije e-učenje smatraju izazovnijim, imaju negativniji stav o korištenju računalnih sustava, te manji interes za inovacije e-učenja i korištenja računala u odnosu na nastavnike matematike i fizike, ali se svi slažu da e-učenje ima dosta prednosti u odnosu na druge metode poučavanja. Stavovi nastavnika o e-učenju se ne razlikuju u odnosu na ustanovu u kojoj rade. Istraživanja o stavovima nastavnika o e-učenju koji drže nastavu iz prirodnih područja, a pogotovo usporedba stavova nastavnika po pojedinim predmetima, rijetka su i u stranoj literaturi, a pogotovo na nivou Hrvatske. 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[![logo stoo2_1 (no).png](https://hub.ufzg.hr/uploads/images/gallery/2024-09/scaled-1680-/logo-stoo2-1-no.png)](https://hub.ufzg.hr/uploads/images/gallery/2024-09/logo-stoo2-1-no.png) **Teaching (Today for) Tomorrow:** **Bridging the Gap between the Classroom and Reality** 3rd International Scientific and Art Conference Faculty of Teacher Education, University of Zagreb in cooperation with the Croatian Academy of Sciences and Arts
**Teachers' attitudes in the field of natural sciences towards e-learning**
##### **Abstract**
The aim of this paper was to examine and analyse the attitudes of biology, chemistry, mathematics, and physics teachers towards e-learning with respect to gender, age, and the type of school they work in. The research involved teachers from various parts of the Republic of Croatia (N=208). For the purposes of this research, the original E-learning Attitude Scale was used to examine teachers' attitudes towards the challenges of e-learning, the advantages of e-learning, the use of computer systems, and preferences regarding e-learning innovations and computer use in leisure time. The questionnaire was supplemented with general information about the teachers (gender, age, subject taught, and school they work in). The results of the research showed that teachers generally have positive attitudes towards e-learning. Male teachers give slightly more preference to using computers for lesson preparation and prefer to learn about technological innovations. Older teachers are less confident in using computers. Chemistry and biology teachers find e-learning more challenging, have a more negative attitude towards the use of computer systems, and have less interest in e-learning innovations and computer use compared to mathematics and physics teachers, but they all agree that e-learning has advantages over other teaching methods. The results of this research contribute to a better understanding of the acceptance and use of e-learning by teachers. It is important that teachers are ready to adapt to new circumstances and seek opportunities to improve their e-learning skills, which are crucial for creating a higher quality and more modern educational process.
***Key words:***
e-learning; e-learning challenges; teaching; teachers; e-learning advantages
# Stavovi učenika prema umjetnoj inteligenciji i učestalost korištenja Chat GPT-a
[![logo stoo2_1 (no).png](https://hub.ufzg.hr/uploads/images/gallery/2024-09/scaled-1680-/logo-stoo2-1-no.png)](https://hub.ufzg.hr/uploads/images/gallery/2024-09/logo-stoo2-1-no.png) **Odgoj danas za sutra:** **Premošćivanje jaza između učionice i realnosti** 3\. međunarodna znanstvena i umjetnička konferencija Učiteljskoga fakulteta Sveučilišta u Zagrebu Suvremene teme u odgoju i obrazovanju – STOO4 u suradnji s Hrvatskom akademijom znanosti i umjetnosti
##### **Ivan Filipović** *Croatia* *ivanfilipovic1@gmail.com*
**Sekcija - Odgoj i obrazovanje za digitalnu transformaciju****Broj rada: 41****Kategorija: Izvorni znanstveni rad**
##### **Sažetak**
Cilj istraživanja je utvrditi koji prediktori doprinose prevalenciji korištenja chat GPT-a i stavove učenika 8. razreda prema umjetnoj inteligenciji u gradu Zagrebu. Umjetna inteligencija i razni alati poput Chat GPT-a postaju sve prisutniji u životima učenika, a upotreba istih u školama još uvijek nije službeno definirana. Populacija ispitanika definirana je kao populacija učenika 8. razreda četiriju škola Grada Zagreba (N=7670). Korišten je anketni upitnik Student Attitudes Toward AI (SATAI) koji se sastoji od 26 čestica kojima se mjere stavovi učenika prema korištenju umjetne inteligencije. Rezultati provedenog istraživanja ne pokazuju statistički značajne razlike u kognitivnoj i afektivnoj komponenti stava, već samo u ponašajnoj komponenti. Rezultat provedenog istraživanja pokazuje da češće igranje igara u slobodno vrijeme pozitivno pridonosi češćoj upotrebi alata umjetne inteligencije. Varijabla 'poticaj' ima pozitivan i statistički značajan doprinos objašnjenju učestalijeg korištenja Chat GPT-a, što pokazuje da poticaj nastavnika pridonosi učestalosti korištenja ovog alata. Rad pruža bolji uvid u stavove učenika 8. razreda prema Chat GPT-u i faktore koji utječu na učestalost korištenja te ističu važnost poticaja nastavnika u obrazovanju.
***Ključne riječi:***
edukacijska tehnologija; ponašajne komponente; poticaj nastavnika; prediktori korištenja; stavovi prema tehnologiji
# Uvod U studenom 2022. godine kada je *Chat GPT (eng. Generative pre-trained transformer)* otvoren za javnost došlo je do zabrinutost mnogih pedagoga i institucija jer je studentima omogućen pristup softveru koji im potencijalno može pružiti pomoć pri pregledu literature, pomoći u procesu pisanja radova i zadataka, jezičnom pregledu članaka te identificiranju i formatiranju istraživačkih pitanja (Cox, 2021). Stoga je za nastavnike u visokom obrazovanju postao problem to što bi studenti mogli biti koristiti umjetnu inteligenciju za pisanje eseja i drugih zadataka namijenjenih testiranju njihovih sposobnosti i znanja. Znanstvenike stoga primarno zanima što treba podučavati u obrazovanju o umjetnoj inteligenciji (UI) (Ali i sur., 2019; Lee, 2020; Touretzky i sur., 2019). U istraživanju provedenom 2019.godine (Yoo, 2019) je podijeljeno obrazovanje povezano s UI-jem na 40 stavki i ispitana učinkovitost te važnost svake stavke za diplomske studente. Yoo je otkrio da je preduvjet za razvoj drugih elemenata obrazovanja o UI-ju poboljšanje otvorenosti prema učenju o UI-ju. Stavovi prema učenju o UI-ja i znanje o otvorenosti za učenje UI-ja uglavnom su dobiveni prikupljanjem javnog mišljenja (Ikkatai i sur., 2022; Schepman i Rodway, 2020). Iako možemo dobiti neke uvide o percepciji, motivaciji i osjećajima prema UI-ju iz prethodnih studija o stavovima prema različitim oblicima potpomognutog učenja (Cheung i Vogel, 2013; Dunn i Kennedy, 2019) i stavovima prema STEM obrazovanju (Cukurova i sur., 2020; Gaines-Ross, 2016; Gherheș i Obrad, 2018; Manikonda i Kambhampati, 2018; Sit i sur., 2020), nijedna od njih ne odgovara na pitanje o stavu prema obrazovanju o UI-ju. Stavovi predviđaju i utječu na ponašanje i želju za učenjem, što potvrđuju studije povezane s matematikom (Huang i sur., 2016), znanostima (Khine, 2015) i inženjerstvom (Alias i sur., 2018). Prema modelu teorije planiranog ponašanja (Ajzen, 1991) stavovi učenika prema učenju profesionalnih vještina igraju važnu ulogu u tome hoće li ih stvarno steći, dok istodobno pozitivni stavovi prema učenju pozitivno utječu na njihove namjere učenja. Stoga je utvrđeno da na poboljšanje razinu postignuća učenja utječu pozitivni stavovi učenika (Alias i sur., 2018; Cukurova i sur., 2020) i pomažu razvoju nastavnog plana i nastavnicima u optimiranju nastave (Dunlap, 1990; Yu i sur., 2012). To je također povezano s idejom (Schepman i Rodway, 2020) da opći stavovi ljudi prema UI-ju vjerojatno igraju veliku ulogu u njihovom prihvaćanju UI-ja. To je razlog zašto je za uspješnu implementaciju UI učenja, potrebno razumjeti i mjeriti stavove učenika prema UI-ju. Malo je vjerojatno da će učenici savladati profesionalne vještine bez obzira na učinkovitost njihova obrazovanja ako ne razviju pozitivan stav prema učenju istih (Ajzen, 1991; Fredrickson, 2001). Zato mjerenje stavova prema UI može biti važan čimbenik u uspjehu ili neuspjehu obrazovanja o UI-ju. # Metodologija istraživanja ### *Ciljevi istraživanja* Cilj istraživanja je ispitati stavove učenika prema korištenju umjetne inteligencije i namjere vezane za buduće korištenje te istražiti prediktore koji doprinose prevalenciji korištenja chat GPT-a. ### *Problemi istraživanja * Na temelju cilja istraživanja definirani su sljedeći problemi: Problem 1: Ispitati razliku u kognitivnoj komponenti stava prema umjetnoj inteligenciji između dječaka i djevojčica. Problem 2: Ispitati razliku u afektivnoj komponenti stava prema umjetnoj inteligenciji između dječaka i djevojčica. Problem 3: Ispitati razliku u ponašajnoj komponenti stava prema umjetnoj inteligenciji između dječaka i djevojčica. Problem 4: Istražiti prediktore koji doprinose objašnjenju učestalosti korištenja chat GPT-a. ### * Hipoteze* H1: Dječaci imaju značajno pozitivniju kognitivnu komponentu stava prema umjetnoj inteligenciji nego djevojčice. H2: Dječaci imaju značajno pozitivniju afektivnu komponentu stava prema umjetnoj inteligenciji nego djevojčice. H3: Dječaci imaju značajno pozitivniju ponašajnu komponentu stava prema umjetnoj inteligenciji nego djevojčice. H4: Igranje igrica je statistički značajan prediktor prevalencije korištenja Chat GPT-a. Dosadašnja istraživanja pokazuju kako postoji statistički značajna razlika između muškaraca i žena povezana s praksom korištenja umjetne inteligencije. U istraživanju provedenom 2023.godine (Bodani i sur., 2023) su se ispitivali znanje, stavovi i prakse opće populacije prema korištenju Chat GPT-a te je potvrđena statistički značajna razlika (P-vrijednost od ,001). I druge studije su izvijestile o značajnim razlikama u stavovima prema tehnologiji između muškaraca i žena (Padilla-Meléndez i sur., 2013; Teo i sur., 2015; Tezci, 2011) te otkrivaju da su se muškarci značajno razlikovali u tehnološkim sposobnostima i percipiranoj lakoći korištenja tehnologije od žena (Teo, 2014). ### * Uzorak ispitanika* Istraživanje je provedeno na uzorku od 244 učenika 8.razreda (stariji od 14.godina) osnovnih škola na području Grada Zagreba. Istraživanje je provedeno online putem poveznice koja im je bila poslana. Sudjelovanje u istraživanju je dobrovoljno, dok je anonimnost ispitanika osigurana od strane istraživača uklanjanjem podataka koji bi mogli otkriti identitet ispitanika te je isto navedeno u uvodnome dijelu anketnog upitnika. Učenici u svakom trenutku istraživanja mogu odustati. ** ** ### *Postupak* Istraživanje je provedeno tijekom 2024. godine, s ciljem ispitivanja stavova učenika prema umjetnoj inteligenciji i utvrđivanja prediktora učestalosti korištenja Chat GPT-a među učenicima osmih razreda u Zagrebu. Za sakupljanje podataka korišten je Google Forms obrazac, distribuiran putem emaila i platforme Teams. Sakupljeni podaci analizirani su statističkim softverskim paketom SPSS. Za opću deskripciju podataka izračunate su frekvencije, postotci, srednje vrijednosti (M) i standardne devijacije (SD). Statistički značajne razlike među grupama utvrđene su primjenom t-testa, dok su za ispitivanje prediktora učestalosti korištenja UI alata korištene metode linearne regresijske analize. Povezanost nezavisnih varijabli (poput igranja igrica, poticaja nastavnika, te uspjeha u školi) s učestalošću korištenja Chat GPT-a istražena je Pearsonovim koeficijentom korelacije. Rezultati su uspoređeni sa sličnim svjetskim istraživanjima kako bi se osigurala relevantnost i validnost zaključaka. ### *Instrumenti* Upitnik se sastoji od tri dijela. Prvim dijelom prikupljeni su socio-demografski podatci o sudionicima (dob, spol). U drugom dijelu (Prilog 1) koristi se anketni upitnik *Student Attitudes Toward AI (SATAI)* (Suh i Ahn, 2022) koji se sastoji od 26 čestica kojima se mjere stavovi učenika prema korištenju umjetne inteligencije i namjere vezane za buduće korištenje. Čestice su zadržane prema originalnom upitniku. Slaganje sa svakom tvrdnjom sudionici izražavaju na skali Likertovog tipa od 5 stupnjeva (od 1 – uopće se ne slažem, do 5 – u potpunosti se slažem). Četiri čestice su formulirane tako da obuhvaćaju kognitivne aspekte (npr. Mislim da bi se lekcije o umjetnoj inteligenciji trebale učiti u školi), deset čestica obuhvaća afektivne (emocionalne) aspekte (npr. UI proizvodi više dobra nego zla.) i osam čestica obuhvaća komponente ponašanja (npr. Izabrat ću posao u području umjetne inteligencije). Cronbachov α koeficijent pouzdanosti preuzete skale je za bihevioralne komponente (namjera korištenja) ,956, za afektivne (stav) ,924, a za kognitivne ,905 u anketnom upitniku Student Attitudes Toward AI (SATAI) (Suh & Ahn, 2022). U trećem dijelu ispituje se namjera korištenja umjetne inteligencije pitanjem „Koliko često koristiš Chat GPT i druge programe umjetne inteligencije (UI)?“. Odgovor na ovo pitanje sudionici izražavaju odabirom jedne od ponuđenih kategorija na rang skali, koja mjeri učestalost korištenja od najniže razine („nikad“) do najviše razine („nekoliko puta dnevno“). Budući da kategorije imaju hijerarhijski poredak, ali razlike između njih nisu nužno jednako razmaknute, pitanje je oblikovano kao rang varijabla. Ponuđene opcije za odgovor su: „nikad“, „1x mjesečno“, „više puta mjesečno“, „1x tjedno“, „2x tjedno“, „3x tjedno“, „svaki dan“ i „nekoliko puta dnevno“. ### *Deskriptivna statistika* Tablica 1 Deskriptivna statistika i odstupanje od normalne distribucije
Varijabla Kognitivne komponente Afektivne komponente Ponašajne komponente Koliko često koristiš Chat GPT i druge programe umjetne inteligencije (UI)?
Broj ispitanika (N) 244 244 244 244
Raspon 4 4 4 7
Minimum 1 1 1 1
Maksimum 5 5 5 8
Zbroj 755,25 727,60 697,86 732
Srednja vrijednost 3,0953 2,9820 2,8601 3,00
Standardna pogreška 0,05874 0,05359 0,05649 0,132
Standardna devijacija 0,91759 0,83706 0,88235 2,059
Varijanca 0,842 0,701 0,779 4,239
Asimetrija -0,044 0,092 -0,013 0,884
Standardna pogreška asimetrije 0,156 0,156 0,156 0,156
Spljoštenost -0,124 0,247 -0,013 -0,305
Standardna pogreška spljoštenosti 0,310 0,310 0,310 0,310
** ** Četiri varijable su uključene u istraživanje: kognitivna, afektivna i ponašajna komponenta stava te učestalosti korištenja Chat GPT-a. Kognitivna komponenta ima raspon vrijednosti od 1 do 5 i srednjom vrijednošću (M=3,095). Standardna devijacija iznosi 0,918, što ukazuje na relativno konzistentne odgovore među ispitanicima. Vrijednosti koeficijenta asimetrije (-,044) i spljoštenosti (-,124) sugeriraju gotovo simetričnu distribuciju koja ne odstupa značajno od normalne. Afektivna komponenta ima srednju vrijednost od 2,982, što ukazuje na umjereno izražene emocionalne reakcije učenika prema umjetnoj inteligenciji. Standardna devijacija od 0,837 pokazuje sličnu varijabilnost kao kod kognitivnih komponenti. Distribucija ima blago pozitivnu asimetriju (Skewness=,092) i blago povišenu spljoštenost (Kurtosis=,247), što ukazuje na malo izraženiji vrh distribucije. Ponašajna komponenta ima najnižu srednju vrijednost među analiziranim varijablama (M=2,860). Standardna devijacija iznosi 0,882, dok su vrijednosti asimetrije (-,013) i spljoštenosti (-,013) praktički neutralne, što ukazuje na ravnomjernu distribuciju odgovora. Varijabla učestalost korištenja Chat GPT-a ima srednju vrijednost (M=3,00) što ukazuje na umjereno korištenje među ispitanicima, dok standardna devijacija (2,059) sugerira veće razlike u učestalosti korištenja. Pozitivna asimetrija distribucije (Skewness=,884) ukazuje na to da većina ispitanika rjeđe koristi Chat GPT, dok manji broj ispitanika pokazuje veću učestalost korištenja. Vrijednost spljoštenosti (-,305) ukazuje na nešto plosnatiji oblik distribucije. # Rezultati Hipoteze koje proizlaze iz problema su sljedeće: H1: Dječaci imaju značajno pozitivniju kognitivnu komponentu stava prema umjetnoj inteligenciji nego djevojčice. P-vrijednost za t-test (t=1,654; df=242) je veća od ,05 (p=,099). Srednje vrijednosti pokazuju da nema statistički značajne razlike u kognitivnoj komponenti stava između dječaka (M=3,20; Sd=1,034) i djevojčica (M=3,00; Sd=0,793), što znači da rezultati ne podržavaju postavljenu hipotezu. Drugim riječima, hipoteza da dječaci imaju pozitivniju kognitivnu komponentu stava u odnosu na djevojčice nije potvrđena te ju odbacujemo. H2: Dječaci imaju značajno pozitivniju afektivnu komponentu stava prema umjetnoj inteligenciji nego djevojčice. P-vrijednost za t-test (t=1,069; df=242) je veća od ,05 (p=,286). Srednje vrijednosti pokazuju da nema statistički značajne razlike u afektivnoj komponenti stava između dječaka (M=3,04; Sd=0,934) i djevojčica (M=2,93; Sd=0,739), što znači da rezultati ne podržavaju postavljenu hipotezu. Drugim riječima, hipoteza o razlici u afektivnoj komponenti stava između dječaka i djevojčica nije potvrđena te ju također odbacujemo. H3: Dječaci imaju značajno pozitivniju ponašajnu komponentu stava prema umjetnoj inteligenciji nego djevojčice. P-vrijednost za t-test (t=2,310; df=242) je manja od ,05 (p=,022), što znači da je hipoteza potvrđena. Drugim riječima, postoji statistički značajna razlika u srednjoj vrijednosti bihevioralne komponente između dječaka (M=2,99; Sd=0,968) i djevojčica (M=2,74; Sd=0,782). S obzirom da je srednja vrijednost za dječake veća, možemo zaključiti da dječaci u prosjeku češće pokazuju ponašanja povezana s primjenom umjetne inteligencije u usporedbi s djevojčicama. H4: Igranje igrica je statistički značajan prediktor prevalencije korištenja Chat GPT-a. Tablica 2 Rezultati linearne regresijske analize s prediktorima uspjeh u školi, poticaj nastavnika, igranje igrica, pohađanje informatike, obrazovanje oca i majke, te kriterijem učestalosti korištenja Chat GPT-a
Prediktor B Beta t p
Uspjeh -,275 -,087 -1,274 ,204
Poticaj ,670 ,196 3,137 ,002
Igranje igrica ,263 ,192 2,955 ,003
Informatika -,290 -,071 -1,081 ,281
Obrazovanje otac ,187 ,122 1,523 ,129
Obrazovanje majka ,032 ,019 ,238 ,812
a) Zavisna varijabla: "Koliko često koristiš Chat GPT i druge programe umjetne inteligencije (UI)?" Tablica 2 prikazuje rezultate linearne regresijske analize koja ispituje doprinos prediktora školski uspjeh, poticaj nastavnika, igranje igrica, pohađanje informatike i obrazovanje oba roditelja objašnjenju učestalosti korištenja ChatGPT-a i drugih UI alata. Rezultati pokazuju da su nastavnički poticaj (B = ,670, p = ,002) i igranje igrica (B = ,263, p = ,003) statistički značajni i pozitivni prediktori korištenja UI alata, pri čemu oba prediktora pozitivno koreliraju s učestalosti korištenja. Uspjeh u školi (p = ,204), pohađanje informatike (p = ,281), obrazovanje oca (p = ,129) i obrazovanje majke (p = ,812) nisu se pokazali značajnim prediktorima. Tablica 3 *Pearsonov koeficijent korelacije među ispitivanim varijablama *
Koliko često koristiš Chat GPT i…? Uspjeh Poticaj Igranje igrica Informatika Spol
Koliko često koristiš Chat GPT i…? 1,000
Uspjeh -,040 1,000
Poticaj ,204\*\* ,054 1,000
Igranje igrica ,212\*\* -,130\* ,041 1,000
Informatika -,121 -,205\*\* -,084 -,229\*\* 1,000
Spol ,023 ,107\* ,073 -,297\*\* ,204\* 1,000
\*\* p <,01; \*p <,05 Tablica 3 prikazuje Pearsonove koeficijente korelacije između učestalosti korištenja ChatGPT-a i drugih mjerenih varijabli. Rezultati pokazuju da je korištenje ChatGPT-a pozitivno povezano s nastavničkim poticajem (r = ,204, p < ,01) i igranjem igrica (r = ,212, p < ,01), što sugerira da učenici koji primaju veći poticaj od nastavnika i češće igraju igrice, češće koriste i ChatGPT. S druge strane, korelacija učestalosti korištenja ChatGPT-a s uspjehom u školi (r = -,040), pohađanjem informatike (r = -,121) i sa spolom (r = ,023) nisu statistički značajne, što upućuje na to da ove varijable nemaju značajan utjecaj na učestalost korištenja UI alata među učenicima. # Rasprava Cilj istraživanja je ispitati stavove učenika prema korištenju umjetne inteligencije i namjere vezane za buduće korištenje te istražiti prediktore koji doprinose prevalenciji korištenja chat GPT-a. Hipoteza 1 je predviđala da dječaci imaju značajno pozitivniju kognitivnu komponentu stava prema umjetnoj inteligenciji nego djevojčice. Međutim, rezultati t-testa nisu pokazali statistički značajne razlike, čim se nije potvrdila ova hipoteza. Slično tome, Hipoteza 2, koja je istraživala afektivnu komponentu stava prema umjetnoj inteligenciji koja je pozitivnija kod dječaka nego djevojčica, ali također nije potvrđena. Hipoteza 3 sugerira značajno pozitivniju ponašajnu komponentu stava prema umjetnoj inteligenciji za dječake nego djevojčice i ona je potvrđena. Iako postoje neke razlike u ponašanju, istraživanja pokazuju da je više sličnosti nego razlika između dječaka i djevojčica (Zakriski i sur., 2005). Društvene uloge, stereotipi i očekivanja snažno utječu na to kako se dječaci i djevojčice ponašaju (Dietrich, 2016). Djeca često usvajaju ponašanja koja su smatrana prikladnima za njihov spol. Vršnjaci imaju značajan doprinos na razvoj stava i ponašanja. Djeca često usvajaju stavove i ponašanja svojih vršnjaka kako bi se uklopila u skupinu (Witt, 2000). Mediji, uključujući televiziju, filmove i video igre, također mogu utjecati na razvoj spolnih stereotipa i ponašanja (Fernandez i Menon, 2022). Hipoteza 4 je predviđala da je igranje igrica statistički značajan prediktor prevalencije korištenja Chat GPT-a. Ova hipoteza je potvrđena. Rezultati provedenog istraživanja potvrđuju ovu povezanost jer je koeficijent za varijablu "igranje igrica" pozitivan i statistički značajan (p = ,003), kao što je prikazano u Tablici 2. To znači da učenici koji češće igraju igrice u slobodno vrijeme također češće koriste alate umjetne inteligencije, što može ukazivati na veću digitalnu pismenost i sklonost eksperimentiranju s novim tehnologijama. Korelacija između učestalosti igranja igrica i korištenja ChatGPT-a (r = ,212, p < ,01) prikazana u Tablici 3 dodatno potvrđuje ovu povezanost. U novije vrijeme, sve većom dostupnošću interneta i mobitela, igranje igrica postalo je lako dostupno te je počelo izazivati ovisnost i sve više utjecati na način na koji ljudi provode vrijeme. Međutim, postoji i pozitivna strana, koja se očituje u lakšem snalaženju mladih ljudi u svijetu napredne tehnologije, omogućujući im brzo usvajanje novih vještina i veću otvorenost prema promjenama. Istraživanja su pokazala da igranje igrica kao oblik rekreacijske tehnologije može potaknuti interes za tehnologiju (Batcheller i sur., 2007). Osim toga, vrsta znanstvenog razmišljanja koju potiču videoigre te tehnološke sposobnosti potrebne za igranje videoigara rezultiraju većim povjerenjem igrača u računalne sustave i povećanjem zanimanja za računalnu znanost (Sevin & Decamp, 2016). Učinak poticanja nastavnika na određene aktivnosti također ima značajan utjecaj na samoučinkovitost i motivaciju učenika za rad, kao što su pokazala ranija istraživanja (Tuckman, Bruce W., 1991). Rezultati provedenog istraživanja pokazuju da, slično kao i kod igranja igrica, i varijabla "poticaj" ima pozitivan i statistički značajan doprinos (p = ,002), što je vidljivo u Tablici 2. To znači da učenici koji primaju veći nastavnički poticaj češće koriste alate umjetne inteligencije, što može ukazivati na važnost uloge nastavnika u promicanju novih tehnologija u obrazovanju. S druge strane, varijable "obrazovanje oca" (p = ,129) i "obrazovanje majke" (p = ,812) imaju pozitivan doprinos, ali nisu statistički značajne na razini od ,05, kako je prikazano u Tablici 2. To sugerira da postoji tendencija da učenici s obrazovanijim roditeljima češće koriste AI alate, no ta povezanost nije dovoljno snažna da bi se mogla smatrati pouzdanom. Nezavisne varijable "uspjeh" i "informatika", koje označavaju ukupnu srednju ocjenu na kraju sedmog razreda i pohađanje informatike, također su analizirane. Ove dvije varijable imaju negativan doprinos, no nisu statistički značajne, što se vidi iz Tablice 2. Beta koeficijenti za obje varijable su negativni, što može ukazivati na blagu tendenciju da učenici s boljim školskim uspjehom i oni koji pohađaju informatiku nešto rjeđe koriste alate umjetne inteligencije. Ova povezanost dodatno je vidljiva kroz negativne korelacije između uspjeha i igranja igrica (r = -,130, p < ,05) te između informatike i korištenja ChatGPT-a (r = -,121, p < ,05) u Tablici 3. Drugim riječima, učenici koji imaju slabije školske rezultate češće koriste AI alate poput ChatGPT-a kako bi si olakšali rješavanje školskih zadataka. # Zaključak Prethodna istraživanja su pokazivala kako postoji statistički značajna razlika između dječaka i djevojčica u sve tri komponente stava prema umjetnoj inteligenciji. Rezultati provedenog istraživanja ne pokazuju statistički značajne razlike u kognitivnoj i afektivnoj komponenti stava nego samo u ponašajnoj komponenti. Rezultati provedenog istraživanja pokazuju da varijabla "igranje igrica" ima pozitivan i statistički značajan učinak (p = ,003), što znači da češće igranje igrica u slobodno vrijeme pozitivno utječe na češću upotrebu alata umjetne inteligencije. Varijabla "poticaj" ima pozitivan i statistički značajan doprinos što pokazuje da poticaj nastavnika doprinosi učestalosti korištenja Chat GPT-a. S druge strane, obrazovanje oca i majke imaju pozitivan doprinos, ali nisu statistički značajni što pokazuje da postoji tendencija da učenici s obrazovanijim roditeljima češće koriste alate umjetne inteligencije, ali ta razlika nije dovoljno jaka da bismo je s velikom sigurnošću pripisali slučajnosti. Nezavisne varijable „uspjeh“ i „informatika“ imaju negativan doprinos , ali nisu statistički značajne što bi moglo ukazivati na to da postoji blaga tendencija da učenici s većim uspjehom ili većim znanjem iz informatike nešto rjeđe koriste alate umjetne inteligencije. 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A model of junior high school students’ attitudes toward technology. *International Journal of Technology and Design Education*, *22*(4), 423–436. https://doi.org/10.1007/s10798-011-9154-8 Zakriski, A. L., Wright, J. C., & Underwood, M. K. (2005). Gender Similarities and Differences in Children’s Social Behavior: Finding Personality in Contextualized Patterns of Adaptation. *Journal of Personality and Social Psychology*, *88*(5), 844–855. https://doi.org/10.1037/0022-3514.88.5.844 ** **
[![logo stoo2_1 (no).png](https://hub.ufzg.hr/uploads/images/gallery/2024-09/scaled-1680-/logo-stoo2-1-no.png)](https://hub.ufzg.hr/uploads/images/gallery/2024-09/logo-stoo2-1-no.png) **Teaching (Today for) Tomorrow:** **Bridging the Gap between the Classroom and Reality** 3rd International Scientific and Art Conference Faculty of Teacher Education, University of Zagreb in cooperation with the Croatian Academy of Sciences and Arts
**Students' Attitudes Towards Artificial Intelligence and Frequency of Using Chat GPT**
##### **Abstract**
The aim of the study is to determine which predictors contribute to the prevalence of using chat GPT and the attitudes of 8th grade students towards artificial intelligence in the city of Zagreb. Artificial intelligence and various tools such as Chat GPT are becoming increasingly present in the lives of students, but their use in schools has not yet been officially defined. The population of respondents was defined as the population of 8th grade students attending four schools in the City of Zagreb (N=7670). The Student Attitudes Toward AI (SATAI) questionnaire was used, which consists of 26 items that measure students' attitudes towards the use of artificial intelligence. The results of the study do not show statistically significant differences in the cognitive and affective components of the attitude, but only in the behavioral component. The results of the study show that playing games frequently during free time positively contributes to more frequent use of artificial intelligence tools. The variable 'incentive' has a positive and statistically significant contribution to explaining more frequent use of Chat GPT, which shows that teacher encouragement contributes to the frequency of using this tool. The paper provides a better insight into the attitudes of 8th grade students towards Chat GPT and the factors that influence the frequency of use, and highlights the importance of teacher encouragement in education.
***Key words:***
educational technology; behavioral components; teacher encouragement; predictors of use; attitudes towards technology
# Stavovi učitelja i studenata prema korištenju igrifikacije u nastavi
[![logo stoo2_1 (no).png](https://hub.ufzg.hr/uploads/images/gallery/2024-09/scaled-1680-/logo-stoo2-1-no.png)](https://hub.ufzg.hr/uploads/images/gallery/2024-09/logo-stoo2-1-no.png) **Odgoj danas za sutra:** **Premošćivanje jaza između učionice i realnosti** 3\. međunarodna znanstvena i umjetnička konferencija Učiteljskoga fakulteta Sveučilišta u Zagrebu Suvremene teme u odgoju i obrazovanju – STOO4 u suradnji s Hrvatskom akademijom znanosti i umjetnosti
##### **Katarina Širanović** *Sveučilište u Zagrebu, Hrvatska* *siranovic12@gmail.com*
**Sekcija - Odgoj i obrazovanje za digitalnu transformaciju****Broj rada: 42****Kategorija: Izvorni znanstveni rad**
##### **Sažetak**
Igrifikacija se odnosi na primjenu elemenata igre u kontekstu koji nije namijenjen za igru, s ciljem poticanja motivacije i angažmana pojedinca te boljeg razumijevanja sadržaja. Cilj ovog istraživanja bio je ispitati upoznatost učitelja i studenata učiteljskog studija s konceptom igrifikacije, njihove stavove prema korištenju iste u nastavi te utvrditi postoji li povezanost između namjere budućeg korištenja igrifikacije s njihovim stavovima i karakteristikama pojedinih crta ličnosti. U istraživanju je sudjelovalo 111 učitelja razredne nastave zaposlenih na području grada Zagreba i 109 studenata četvrte i pete godine Učiteljskog fakulteta u Zagrebu. Korišten je upitnik o stavu prema igrifikaciji koji se sastoji od 32 tvrdnje i skraćeni upitnik o pet dimenzija ličnosti kojeg čini 15 tvrdnji. Ispitanici su slaganje sa svakom tvrdnjom procjenjivali na skali Likertovog tipa od pet stupnjeva. Dobiveni rezultati pokazuju da su učitelji bolje upoznati s konceptom igrifikacije od studenata, ali obje skupine dijele pozitivan stav prema njezinoj primjeni u nastavi. Učitelji imaju statistički značajno niži prosječan pozitivan stav prema igrifikaciji u nastavi u usporedbi sa studentima. Nadalje, rezultati pokazuju da je jedino pozitivan stav prema igrifikaciji statistički najznačajniji prediktor budućeg korištenja iste u nastavi. Daljnja istraživanja mogla bi se istražiti razliku u stavovima učitelja razredne nastave prema igrifikaciji između trenutačnih korisnika i onih koji ju ne koriste. Također, buduća istraživanja mogla bi obuhvatiti učitelje i studente učiteljskih studija iz cijele Hrvatske s ciljem pružanja sveobuhvatnijeg uvida u percepciju igrifikacije u hrvatskom obrazovnom sustavu.
***Key words:***
elementi igre; implementacija; kvantitativno istraživanje; pozitivan stav; upoznatost
**Uvod** Od početka 2000-tih godina igrifikacija je postupno postala sve popularnija. Javila se u različitim sektorima poput zdravstvenog, sporta, marketinga pa tako i u području obrazovanja. Termin igrifikacija osmislio je Nick Pelling oko 2002. godine, a definira se kao korištenje elemenata igre u neigrajućem kontekstu (Deterding i sur., 2011). Može se definirati i kao proces specifičnog načina razmišljanja karakterističnog za igranje igara (engl. *game-thinking*) i mehanike igranja igara s ciljem motiviranja pojedinca za rješavanje nekog problema (Zichermann i Cunningham, 2011). Igrifikacija se često poistovjećuje s učenjem temeljenom na igranju igara (engl. *game-based learning*), ali navedena dva koncepta nisu ista. Učenje temeljeno na igranju igara koristi igru kao dio procesa učenja, odnosno kao izvor novih znanja, dok igrifikacija preoblikuje proces učenja u igru korištenjem njezinih elemenata (Al-Azawi i sur., 2016). Elementi igre različito se karakteriziraju u literaturi. Najčešće korišteni elementi igre su bodovi, izazovi, značke, ljestvice poredaka i priče (Majuri i ostali, 2018). Do sličnih rezultata došli su i Durin i sur. (2019) analizom provedenih istraživanja navodeći nagrade, povratne informacije, izazove, razine, bodove, avatare, zadatke, vremensko ograničenje, ljestvice poredaka, trake napretka i značke kao najčešće korištene elemente igara. Konceptualni okvir MDA, koji je jedan od najpoznatijih, definira elemente igre kroz njihovu pripadnost jednoj od triju kategorija: mehanika, dinamika i estetika (Zichermann i Cunningham, 2011). Pri tome, mehanika igre opisuje pojedine komponente igre, dinamika opisuje ponašanje za vrijeme izvođenja mehanika, dok estetika obuhvaća poželjne emocionalne reakcije igrača tijekom igranja (Hunicke i sur., 2004). Dobro promišljena implementacija elemenata igrifikacije može poboljšati intrinzičnu motivaciju zadovoljavanjem urođene psihološke potrebe pojedinca za autonomijom, kompetitivnosti i povezanosti (Fuchs i sur., 2014). O navedenim psihološkim potrebama govori teorija samoodređenja (Ryan i Deci, 2000a). Elementi igre koji doprinose zadovoljenju potrebe za kompetitivnosti su bodovi, ljestvice poredaka, značke i drugi koji daju povratnu informaciju pojedincu o njegovom napretku kroz određeni period (Sailer i sur., 2017), dok se zadovoljenje potrebe za autonomijom očituje u mogućnosti donošenja odluka i odabira idućih koraka (Ryan i Deci, 2000b). Uloga elemenata igrifikacije može se promatrati i kroz teoriju zanesenosti koja se odnosi na postojanje optimalnog stanja koje se javlja kada je pojedinac duboko uronjen i angažiran u aktivnost te istu rado izvršava (Csikszentmihalyi, 2014). Da bi se javilo stanje zanesenosti kod pojedinca, aktivnost u kojoj isti sudjeluje treba biti izazovna, ostvariva te pojedinac u njoj treba uživati (Csikszentmihalyi i sur., 2014). Analiza rezultata prethodnih istraživanja o učincima implementacije igrifikacije pokazala je da većina ispitanika pokazuje pozitivan stav prema igrifikaciji u obrazovanju i to u vidu poboljšavanja stava učenika prema učenju, povećanja motivacije učenika (Banfield i Wilkerson, 2014; Cunha i sur., 2018; Treiblmaier i Putz, 2020) i angažiranosti te razumijevanja nastavnog sadržaja (Durin i sur., 2019). Učenici su na nastavi matematike obogaćenoj elementima igrifikacije rješavali više zadataka te su dulje zadržavali fokus na istima (Jagušt i sur., 2017; Türkmen i Soybaş, 2019). Istraživanja su pokazala da korištenje igrifikacije u nastavi stvara poticajno okruženje za učenje (Arkün Kocadere i Çağlar, 2015) te da ima pozitivan i značajan učinak na ishode učenja učenika u formalnom obrazovnom okruženju (Huang i sur., 2020). Bez obzira na prednosti potvrđene brojnim istraživanjima o učincima igrifikacije u nastavi na zadovoljstvo, motivaciju, uspjeh i angažiranost učenika, istraživanja pokazuju da namjera implementacije igrifikacije ovisi o stavovima učitelja prema istoj (Avidov-Ungar i Eshet-Alkalai, 2011; Scherer i sur., 2019). Istraživanja potvrđuju kako su učitelji koji imaju pozitivan stav prema igrifikaciji skloniji korištenju iste u nastavi u budućnosti (Asiri, 2019). Osim stava, crte ličnosti poput otvorenosti prema novim iskustvima i samoučinkovitosti povezane su s razvojem pozitivnog stava prema igrifikaciji (Cramariuc i sur., 2022 ). Kako bi igrifikacija u nastavi bila učinkovita, učitelji moraju imati znanja o njoj, ali je važan i njihov stav prema istoj (Bicen i sur., 2022). Budući učitelji procjenjuju kako nemaju dovoljno teorijskog i praktičnog znanja o načinu implementacije igrifikacije u nastavu u budućnosti (Guerrero Puerta, 2024). Također, istraživanja pokazuju kako ni učitelji zapravo ne poznaju koncept igrifikacije (Brooks i sur., 2019; Mårell-Olsson, 2022). Nedostatak znanja o konceptu igrifikacije dovodi do njezinog uvođenja u nastavni proces bez određenih kriterija ili bez konfiguracije koja ima određenu svrhu (Navarro Mateos i sur., 2021). Bez obzira na porast interesa prema igrifikaciji u obrazovanju, i dalje nedostaju istraživanja koja istražuju stav učitelja prema igrifikaciji i implementaciji iste u obrazovanje budućih učitelja (Guerrero Puerta, 2024). S obzirom na brojne prednosti koje se očituju u primjeni igrifikacije u nastavi, namjera je ovog istraživanja ispitati stavove učitelja i studenata četvrte i pete godine učiteljskog studija prema igrifikaciji, njihovu upoznatost sa samim konceptom, iskustva u korištenju iste i namjeru njezinog budućeg korištenja te istu povezati sa karakteristikama ličnosti. Na temelju pregleda dosadašnjih istraživanja, pokazano je da učitelji i studenti nemaju dovoljno znanja o igrifikaciji, što znači da postoji potreba za edukacijom istih tijekom studijskog obrazovanja prije implementacije igrifikacije u nastavni proces, stoga se važnost ovog istraživanja očituje u utvrđivanju navedene potrebe s ciljem utjecanja na stvaranje kurikuluma ili smjernica kako navedeni pedagoški pristup implementirati u obuku koju provode obrazovne institucije. **Metodologija istraživanja** **Cilj istraživanja** Cilj ovog istraživanja bio je ispitati upoznatost učitelja i studenata Učiteljskog studija s konceptom igrifikacije, njihove stavove prema korištenju igrifikacije u nastavi te utvrditi postoji li povezanost između namjere budućeg korištenja igrifikacije s njihovim stavovima i karakteristikama pojedinih crta ličnosti. **Istraživačka pitanja** Na temelju pregledane literature i postavljenog cilja istraživanja postavljeni su sljedeći problemi istraživanja: 1. Ispitati upoznatost učitelja razredne nastave i studenata učiteljskog studija s konceptom igrifikacije. 2. Ispitati razliku između stavova učitelja razredne nastave i studenata učiteljskog studija prema implementaciji igrifikacije u nastavu. 3. Ispitati razliku između namjere korištenja igrifikacije u budućnosti između učitelja razredne nastave i studenata učiteljskog studija s obzirom na njihov stav prema igrifikaciji i njihove crte ličnosti. **Hipoteze** Na temelju navedenih istraživačkih problema postavljene su sljedeće hipoteze: H1: Ne postoji statistički značajna razlika u upoznatosti učitelja razredne nastave i studenata učiteljskog studija s konceptom igrifikacije. H2: Ne postoji statistički značajna razlika između učitelja razredne nastave i studenata učiteljskog studija u stavovima prema igrifikaciji u nastavi. H3: Pozitivan stav prema igrifikaciji i otvorenost prema novim iskustvima statistički su značajni prediktori korištenja igrifikacije u budućnosti. ** ** **Sudionici ** U istraživanju je sudjelovalo ukupno 220 ispitanika. Od ukupnog broja ispitanika, njih 109 bili su studenti 4. i 5. godine koji pohađaju Učiteljski fakultet u Zagrebu, dok su preostali dio ispitanika činili učitelji razredne nastave koji su zaposleni na području Zagreba. Raspodjela ispitanika prema spolu bila je 210 žena (95,5%) i 10 muškaraca (4,5%). **Postupak i instrumenti ** Ispitanici su zamoljeni da ispune anketni upitnik putem *Google Forms* obrasca koji im je poslan mailom. Prikupljeni podaci analizirani su statističkim softverskim paketom SPSS. Za dobivanje podataka korišten je anketni upitnik *Attitude towards the use of gamification in education* (InnoRenew CoE, Izola, Slovenia i sur., 2022) za provjeru stavova učitelja i studenata prema igrifikaciji i skraćeni Big Five Inventory (BFI-S) (Lang i sur., 2011) za mjerenje pet glavnih dimenzija ljudske osobnosti. Na početku su prikupljeni opći podaci o ispitanicima (spol, dob, godine staža, vrsta obrazovanja). ***Attitude towards the use of gamification in education**** *(InnoRenew CoE, Izola, Slovenia i sur., 2022). Upitnik se sastoji od 32 čestice kojima se mjeri stav prema igrifikaciji. Čestice su zadržane prema originalnom upitniku. Slaganje sa svakom tvrdnjom sudionici su izražavali na skali Likertovog tipa od 5 stupnjeva od 1 (uopće se ne odnosi na mene) do 5 (u potpunosti se odnosi na mene). Čestice su formulirane tako da obuhvaćaju kognitivne aspekte (npr. Uz pomoć igrifikacije u nastavi učenici bi bolje razumjeli sadržaj poučavanja), afektivne aspekte (npr. Zabrinut sam da bi igrifikacija u nastavi potaknula učenički nemir) i one vezane uz namjenu budućeg korištenja (npr. Koristio bih igrifikaciju u nastavi kako bi sadržaj poučavanja bio zanimljiviji učenicima). Inverzno je formulirano 17 čestica s ciljem provjere odgovora ispitanika. Za provjeru faktorske strukture provedena je eksploratorna faktorska analiza metodom glavnih komponenti s Varimax rotacijom (KMO = ,915; Bartlettov test sferičnosti p < ,001) koja je rezultirala s dva faktora koji objašnjavaju 74,37% ukupne varijance. Prvi faktor, koji je nazvan pozitivan stav prema igrifikaciji, objašnjava 27,39% varijance i sastoji se od ukupno 15 čestica. Drugi faktor, koji je nazvan negativan stav prema igrifikaciji, objašnjava 46,98% varijance i sastoji se od ukupno 17 čestica. **Big Five Inventory (BFI-S)** (Lang i sur., 2011). Za potrebe istraživanja korišten je skraćena verzija navedenog upitnika. Upitnik se sastoji od 15 čestica kojima je obuhvaćeno pet dimenzija ličnosti: neuroticizam, ugodnost, esktraverzija, savjesnost i otvorenost prema iskustvu. Slaganje sa svakom tvrdnjom ispitanici su izražavali na skali Likertovog tipa od 5 stupnjeva od 1 (uopće se ne odnosi na mene) do 5 (u potpunosti se odnosi na mene). Na kraju upitnika dano je pitanje otvorenog tipa u kojem su ispitanici mogli dati komentare ili izraziti vlastita iskustva vezana uz korištenje igrifikacije u nastavi. **Rezultati** Prva istraživačka hipoteza, koja predviđa da ne postoji statistički značajna razlika u upoznatosti ispitanika konceptom igrifikacije, ispitana je t-testom. Uočava se da postoji razlika u upoznatosti učitelja razredne nastave i studenata s konceptom igrifikacije, ali je ona mala (M/SD (učitelji) = 3,97/1,084; M/SD (studenti) = 3,48/1,077). Rezultati Leveneova testa o jednakosti varijanci ukazuje na to da su one homogene (F = 1,778; p = ,184). Rezultati prikazani u Tablici 1 prikazuju da je t-test značajan (t = 3,397, p = ,001) te se nul-hipoteza može odbaciti i zaključiti da postoji statistička značajnost u upoznatosti s konceptom igrifikacije, s time da su učitelji bolje upoznati s konceptom igrifikacije nego studenti. Uz provedeni t-test ispitano je i postojanje korelacija između subjektivno procijenjene upoznatosti ispitanika i točne definicije igrifikacije prema literaturi te netočne definicije iste. Pearsonov koeficijent korelacije pokazao je pozitivnu korelaciju srednje jakosti između tvrdnje *Upoznat/a sam s konceptom igrifikacije* i *Korištenje igrifikacije odnosi se na korištenje elemenata igara u nastavi, npr. bodovi, ljestvice poredaka, nagrade i sl.* (*rs* = ,523, *p*<,001) te negativnu korelaciju između tvrdnje *Upoznat/a sam s konceptom igrifikacije* i *Korištenje igrifikacije u nastavi odnosi se na korištenje igara u nastavi ili na učenje kroz igru* (*rs* = ,-015, *p* = ,828). Dodatno je izračunat i Cohenov *d* kojim se izračunava veličina učinka provedenog testa i on iznosi 0,46 što vodi do zaključka da se radi o srednjoj veličini učinka. Drugim riječima, provedeni t-test je ukazao na statistički značajnu razliku, a veličina učinka potvrđene razlike srednje je veličine. Tablica 1 *Razlike u upoznatosti učitelja i studenata s konceptom igrifikacije*
grupa *N* *M* *SD* *t-test* *p*
Upoznatost s konceptom igrifikacije učitelji razredne nastave 109 3,97 1,084 3,397 ,001
studenti 111 3,48 1,077
Drugom hipotezom pretpostavljeno je da ne postoji statistički značajna razlika između učitelja i studenata Učiteljskog studija u stavovima prema igrifikaciji u nastavi. Navedena hipoteza testirana je t-testom. Uočava se da postoji mala razlika u pozitivnom stavu između učitelja razredne nastave i studenata prema igrifikaciji (*M*/*SD* (učitelji) = 3,86/0,73; *M*/*SD* (studenti) = 4,07/0,56) te izrazito mala razlika u negativnom stavu između učitelja razredne nastave i studenata prema igrifikaciji (*M*/*SD* (učitelji) = 2,11/0,49; *M*/*SD* (studenti) = 2,08/0,52). Rezultati Leveneova testa o jednakosti varijanci ukazuje na to da su one homogene (*F* = 2,770/,005; *p* = ,097/,943). Rezultati prikazani u Tablici 3. prikazuju da je t-test značajan, odnosno, da postoji statistički značajna razlika između učitelja razredne nastave i studenata Učiteljskog studija u njihovom pozitivnom stavu prema igrifikaciji te da oni imaju statistički značajno niži prosječan pozitivan stav prema igrifikaciji u nastavi u usporedbi sa studentima. Rezultati t-testa pokazuju da nema statistički značajne razlike između učitelja razredne nastave i studenata u njihovom negativnom stavu prema igrifikaciji. Tablica 2 *Razlike između pozitivnog i negativnog stava učitelja i studenata prema konceptu igrifikacije*
grupa *N* *M* *SD* *t-test* *p*
Pozitivan stav prema igrifikaciji učitelji razredne nastave 109 3,86 ,73 -2.322 ,021
studenti 111 4,07 ,56
Negativan stav prema igrifikaciji učitelji razredne nastave 109 2,12 ,49 ,596 ,552
studenti 111 2,08 ,52
U svrhu analize prediktorske vrijednosti faktora koji utječu na namjeru budućeg korištenja igrifikacije u nastavi i provjere postavljene treće hipoteze, provedena je hijerarhijska regresijska analiza u tri koraka. U prvom koraku uvrštena je vrsta zanimanja (student/ica, učitelj/ica). U drugom koraku je na navedenu varijablu dodano pet crta ličnosti (ekstraverzija, neuroticizam, ugodnost, savjesnost, otvorenost). U trećem koraku dodana je varijabla stav prema igrifikaciji (pozitivan stav, negativan stav). Teorijska pretpostavka za ovakav slijed varijabli jest ukazivanje da određene crte ličnosti imaju utjecaj na buduće korištenje igrifikacije u nastavi (Camadan i ostali, 2018; Denden i ostali, 2018) te da je pozitivan stav prema igrifikaciji značajan prediktor budućeg korištenja iste u nastavi (Asiri, 2019; Turan i ostali, 2022). Pokazalo se kako zanimanje, kao Model 1, nema značajnu prediktivnu vrijednost objašnjenja namjere korištenja igrifikacije u budućnosti te objašnjava samo 0,4% namjere. U drugom koraku analize (Model 2) uvršteni su faktori koji objašnjavaju pet crta ličnosti te se od njih samo ugodnost pokazala značajnom. U drugom koraku povisio se postotak objašnjenja varijance namjere korištenja igrifikacije u budućnosti na 10,7%. U trećem koraku regresijske analize (Model 3) uz varijable zanimanje i pet crta ličnosti, uvrštena je i varijabla stav prema igrifikaciji (pozitivan stav, negativan stav). U posljednjem, trećem koraku, dodavanjem pozitivnog i negativnog stava kao varijable, ugodnost prestaje biti statistički značajan prediktor, dok je namjeru budućeg korištenja igrifikacije značajno predviđa samo stav, i to pozitivni stavovi pozitivno koreliraju, dok negativni stavovi negativno koreliraju te taj model objašnjava ukupno 30,1% varijance namjere korištenja igrifikacije u budućnosti. Tablica 3 *Hijerarhijska regresijska analiza za namjeru korištenja igrifikacije u budućnosti*
Model Faktori *b* *β* *t* *p*
1 zanimanje -,122 -,063 -,908 ,365
*F* (modela) = ,463, *p* (modela) = ,630, *R* = ,067, *R²* = ,004, *ΔR²* = ,004
2 zanimanje -,106 -,055 -,781 ,436
ekstraverzija ,091 ,069 ,919 ,359
neuroticizam -,013 -,010 -,146 ,884
ugodnost ,289 ,182 2,,191 ,018
savjesnost ,147 ,096 1,291 ,198
otvorenost ,143 ,107 1,426 ,155
*F *(modela) = 3,451, *p* (modela) = ,002, *R* = ,327, *R²* = ,107, *ΔR²* = ,102
3 zanimanje -,211 -,109 -1,722 ,087
ekstraverzija ,037 ,028 ,423 ,672
neuroticizam ,006 ,005 ,072 ,942
ugodnost ,191 ,120 1,755 ,081
savjesnost ,095 ,062 ,934 ,351
otvorenost ,070 ,053 ,785 ,434
pozitivan stav prema igrifikaciji ,391 ,258 3,599 ,000
negativan stav prema igrifikaciji -.530 -,279 -4,059 ,000
*F* (modela) = 9,578, *p* (modela) = ,000, *R* = ,549, *R²* = ,301, *ΔR²* = ,194
Devetnaest ispitanika odgovorilo je na otvoreno pitanje u kojem ih se pozivalo da podijele svoje komentare, iskustva ili zabrinutosti vezane uz korištenje igrifikacije u nastavi. Jedan je komentar bio djelomično pozitivan, npr. *vremenski je zahtjevno pripremiti takve aktivnosti, ali se isplati uložiti to vrijeme u svrhu poticanja na učenje, bolje razumijevanje sadržaja i motivaciju učenika*, jedan je komentar neutralan, npr. *smatram da samo ne bi trebalo pretjerati s količinom,* dok su preostali komentari, točnije njih sedamnaest, pozitivni, npr. *itekako bi igrifikacija trebala više koristiti u nastavi jer djeca promišljaju i nesvjesno razvijaju socijalne vještine i stječu znanje* ili *djeci se to jako sviđa, zadovoljni su, zaigrani i željni pobjede koja im je veliki motivator.* Troje ispitanika izrazilo je želju za edukacijom vezanom uz korištenje igrifikacije u nastavi, npr. *zanima me kvalitetnija primjena igrifikacije u nastavi, voljela bih da postoji edukacija za učitelje o igrifikaciji* ili *voljela bih da ponudite opcije za nas koji minimalno koristimo tehnologiju u nastavi; kako napraviti opciju igrifikacije na ploči ili plakatu.* * * **Rasprava i zaključak** Ovim istraživanjem željela se ispitati upoznatost učitelja razredne nastave i studenata učiteljskog studija s konceptom igrifikacije, njihovi stavovi prema korištenju igrifikacije u nastavi te utvrditi postoji li povezanost između namjere budućeg korištenja igrifikacije s njihovim stavovima i karakteristikama pojedinih crta ličnosti. Za razliku od rezultata prethodnih istraživanja (E. Brooks i sur., 2019; Guerrero Puerta, 2024; Mårell-Olsson, 2022; Toda i sur., 2020; Yaşar i sur., 2020), rezultati ovog istraživanja pokazali su kako su postoji statistički značajna razlika između učitelja razredne nastave i studenata učiteljskog studija u njihovoj upoznatosti s konceptom igrifikacije, s time da su učitelji bolje upoznati s konceptom igrifikacije od studenata. U skladu s prethodnim istraživanjima (Asiri, 2019; Martí-Parreño i sur., 2016; Sáez-López i sur., 2022), rezultati provedenog istraživanja pokazuju kako učitelji i studenti imaju pozitivan stav prema igrifikaciji. Druga hipoteza djelomično je potvrđena jer je istraživanjem utvrđeno da postoji statistički značajna razlika između učitelja razredne nastave i studenata Učiteljskog studija u njihovom pozitivnom stavu prema igrifikaciji te da učitelji imaju statistički značajno niži prosječan pozitivan stav prema igrifikaciji u nastavi u usporedbi sa studentima. Također, pokazano je kako nema statistički značajne razlike između učitelja razredne nastave i studenata u njihovom negativnom stavu prema igrifikaciji. Kao što i prethodna istraživanja pokazuju (Asiri, 2019; Turan i sur., 2022), pozitivan stav prema igrifikaciji statistički je značajni prediktor korištenja igrifikacije u budućnosti. Navedeno je dobiveno i u rezultatima ovoga istraživanja čime se djelomično potvrđuje posljednja hipoteza istraživanja, jer za razliku od prethodnih istraživanja (Cramariuc i sur., 2022), otvorenost prema novim iskustvima nije se pokazala kao statistički značajan prediktor za buduće korištenje igrifikacije u nastavi. Postoje neka ograničenja istraživanja koja onemogućuju generalizaciju dobivenih rezultata na cijelu populaciju. Glavno ograničenje je odabrani prigodni uzorak. Vjerojatnije je da će u istraživanju radije sudjelovati oni koji imaju pozitivniji stav prema igrifikaciji. S obzirom na to da se 30% varijance (R² = ,301) u namjeri budućeg korištenja igrifikacije u nastavi može objasniti posjedovanjem pozitivnog stava prema igrifikacija, ostalih 70% neobjašnjene varijance naglašava da postoje još neke varijable koje utječu na namjeru korištenja iste te one zahtijevaju daljnje istraživanje. Buduća istraživanja mogla bi prevladati ove nedostatke korištenjem drugačijeg uzorkovanja ispitanika poput korištenja slučajnih uzoraka. Mogu se koristiti i kvalitativne metode u istraživanju s ciljem dobivanja dubljeg uvida u stavove učitelja, poput fokus grupa ili dubinskih intervjua. U budućim istraživanja mogla bi se istražiti razlika u stavovima učitelja razredne nastave prema igrifikaciji između trenutačnih korisnika i onih koji ju ne koriste. 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How gamification motivates: An experimental study of the effects of specific game design elements on psychological need satisfaction. *Computers in Human Behavior*, *69*, 371–380. https://doi.org/10.1016/j.chb.2016.12.033 Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. *Computers & Education*, *128*, 13–35. https://doi.org/10.1016/j.compedu.2018.09.009 Toda, A., Pereira, F. D., Klock, A. C. T., Rodrigues, L., Palomino, P., Oliveira, W., Oliveira, E. H. T., Gasparini, I., Cristea, A. I., & Isotani, S. (2020). For whom should we gamify? Insights on the users intentions and context towards gamification in education. *Anais do XXXI Simpósio Brasileiro de Informática na Educação (SBIE 2020)*, 471–480. https://doi.org/10.5753/cbie.sbie.2020.471 Treiblmaier, H., & Putz, L.-M. (2020). Gamification as a moderator for the impact of intrinsic motivation: Findings from a multigroup field experiment. *Learning and Motivation*, *71*, 101655. https://doi.org/10.1016/j.lmot.2020.101655 Turan, Z., Küçük, S., & Karabey, S. (2022). Investigating Pre-Service Teachers’ Behavioral Intentions to Use Web 2.0 Gamification Tools. *Participatory Educational Research*, *9*(4), 172–189. https://doi.org/10.17275/per.22.85.9.4 Türkmen, G. P., & Soybaş, D. (2019). The Effect Of Gamification Method On Students’ Achievements and Attitudes Towards Mathematics. *Bartın Üniversitesi Eğitim Fakültesi Dergisi*, *8*(1), 258–298. https://doi.org/10.14686/buefad.424575 Yaşar, H., Kiyici, M., & Karatas, A. (2020). The Views and Adoption Levels of Primary School Teachers on Gamification, Problems and Possible Solutions. *Participatory Educational Research*, *7*(3), 265–279. https://doi.org/10.17275/per.20.46.7.3 Zichermann, G., & Cunningham, C. (2011). *Gamification by design: Implementing game mechanics in web and mobile apps* (1st. ed). O’Reilly Media.
[![logo stoo2_1 (no).png](https://hub.ufzg.hr/uploads/images/gallery/2024-09/scaled-1680-/logo-stoo2-1-no.png)](https://hub.ufzg.hr/uploads/images/gallery/2024-09/logo-stoo2-1-no.png) **Teaching (Today for) Tomorrow:** **Bridging the Gap between the Classroom and Reality** 3rd International Scientific and Art Conference Faculty of Teacher Education, University of Zagreb in cooperation with the Croatian Academy of Sciences and Arts
**Teachers' and Students' Attitudes Towards the Use of Gamification in Teaching**
##### **Abstract**
Gamification refers to the use of game elements in environments that are not necessarily intended for play, with the aim of enhancing individual motivation and engagement as well as facilitating a deeper understanding of specific content. The aim of this study was to examine the familiarity of the teachers and students from the Faculty of Teacher Education with the concept of gamification, their attitudes towards its use in teaching, and to determine whether there is a correlation between the interaction to use gamification in the future and their attitudes and certain personality traits. The study included 111 primary school teachers employed in the city of Zagreb and 109 fourth-and fifth-year students from the Faculty of Teacher Education in Zagreb. A questionnaire on attitudes towards gamification, consisting of 32 statements, and a shortened version of the Big ive personality traits inventory, composed of 15 items, were used. Respondents evaluated their agreement with each statement on a five-point Likert scale. The results show that teachers are more familiar with the concept of gamification than students, but both groups share a positive attitude towards its application in teaching. Teachers have a statistically significantly lower average positive attitude towards gamification in teaching compared to students. Furthermore, the results indicate that a positive attitude towards gamification is the only statistically significant predictor to future use in teaching. Future research could explore the difference in attitudes towards gamification among current users and non-users of gamification in primary education. Additionally, future research could include teachers and students from teacher education programs across Croatia with the aim of providing a more comprehensive understanding of the application and perception of gamification within the Croatian educational system.
***Key words:***
game elements; familiarity; implementation; positive attitude; quantitative research
# Sigurnost na internetu kod adolescenata
[![logo stoo2_1 (no).png](https://hub.ufzg.hr/uploads/images/gallery/2024-09/scaled-1680-/logo-stoo2-1-no.png)](https://hub.ufzg.hr/uploads/images/gallery/2024-09/logo-stoo2-1-no.png) **Odgoj danas za sutra:** **Premošćivanje jaza između učionice i realnosti** 3\. međunarodna znanstvena i umjetnička konferencija Učiteljskoga fakulteta Sveučilišta u Zagrebu Suvremene teme u odgoju i obrazovanju – STOO4 u suradnji s Hrvatskom akademijom znanosti i umjetnosti
##### **Ružica Filipović** *Učiteljski fakultet Sveučilišta u Zagrebu, Hrvatska* *ruzica.filipovic91@gmail.com*
**Sekcija - Odgoj i obrazovanje za digitalnu transformaciju****Broj rada: 43****Kategorija: Izvorni znanstveni rad**
##### **Sažetak**
Korištenje interneta značajno je poraslo u posljednjih dvadesetak godina, no s tim rastom pojavila se zabrinutost oko problematičnog korištenja interneta koje može uzrokovati psihičke poteškoće. Ovo uključuje aktivnosti poput videoigara, društvenih medija, web-streaminga i online kupovine, a posebno su djeca i adolescenti izloženi rizicima. Cilj ovog istraživanja bio je ispitati razlike, s obzirom na vrstu srednje škole, između stvarnog rizičnog ponašanja adolescenata na internetu i njihove samoprocjene te razinu svijesti o informacijskoj sigurnosti. Istraživanje je provedeno u srednjim školama Sisačko-moslavačke županije na uzorku od 167 učenika prosječne dobi od 16,5 godina, koristeći Bihevioralno-kognitivni upitnik internetske sigurnosti (BKUIS). Upitnik mjeri rizična ponašanja i svijest o sigurnosti putem 17 varijabli raspoređenih u četiri subskale. Rezultati su pokazali visoku pouzdanost upitnika (Cronbachova alfa = 0,81), a zbog odstupanja podataka od normalne distribucije, korišten je neparametrijski test Mann-Whitney U. Rezultati su pokazali da nema statistički značajnih razlika u rizičnom ponašanju između različitih vrsta srednjih škola, osim u specifičnim područjima kao što su privola za obradu osobnih podataka i provjera prijenosnih medija. Statistički značajna razlika su uočene u svijesti o sigurnosti na internetu, gdje su učenici gimnazijskog smjera pokazali bolje znanje o održavanju zaštite i sigurnosnim praksama. Zaključno, iako učenici imaju pristup edukativnim programima o sigurnosti na internetu unutar školskog sustava, potrebno je dodatno raditi na podizanju svijesti i smanjenju rizičnih ponašanja kod adolescenata.
***Ključne riječi:***
adolescenti; BKUIS; internet; sigurnost na internetu; rizična ponašanja
**Uvod ** Korištenje interneta u posljednjih dvadesetak godina uvelike je poraslo u cijelom svijetu (Pettorruso i sur., 2020). Pojam sigurnost na internetu obuhvaća pitanja koja se odnose na fizičku i psihološku dobrobit korisnika Interneta. Istodobno, povećano korištenje interneta izazvalo je zabrinutost zbog njegovog problematičnog korištenja, jer se često povezuje s ozbiljnim psihičkim tegobama (Aboujaoude, 2010; Spada, 2014). Jedan od aspekata rizika povezanih s internetom je problematično korištenje interneta. Problematično korištenje interneta definira se kao korištenje interneta koje stvara psihološke, socijalne, školske i/ili radne poteškoće u životu osobe (Beard i Wolf, 2001), a ono može uključivati različite oblike neprimjerenog ponašanja, videoigre, korištenje društvenih medija, web-streaming, gledanje pornografije i kupnju. Unatoč tome, koncept problematično korištenja interneta prkosi jasnoj mjerljivoj definiciji i to može odražavati heterogenost i složenost fenomena. Grant i sur. (2014) navode da ostaje nejasno zadovoljavaju li svi oblici problematičnog korištenja interneta iste fiziološke kriterije, kao što su tolerancija i odvikavanje ili čak je li problematično korištenje interneta samostalan poremećaj ili potiče druge oblike ovisnosti. Alternativno, neka ponašanja problematičnog korištenja interneta mogu dijeliti sličnosti s poremećajima povezanim s opsesivno-kompulzivnim poremećajima (npr. opetovano provjeravanje e-pošte ili društvenih medija) ili socijalnim anksioznim poremećajem (npr. pretjerano korištenje društvenih medija kao izbjegavanje društvenog kontakta licem u lice) (Chamberlain i sur., 2018; Ioannidis i sur., 2016). Budući da problematično korištenje interneta može imati ozbiljne posljedice, važno je razumjeti kako ono utječe na djecu i adolescente, koji su često izloženi specifičnim rizicima tijekom korištenja digitalnih platformi. Núñez-Gómez i sur. (2021) ističu da, s obzirom na to da djeca i adolescenti često konzumiraju sadržaje na internetu i aktivno sudjeluju na društvenim mrežama, potrebno je poznavati rizike kako bi se provela kritička analiza usmjerena na zaštitu i razumijevanje njihove uporabe ovih platformi. Istraživanje koja je provela Livingstone i sur. (2011) u 25 zemalja Europske unije pokazuju kako roditelji podcjenjuju rizike kojima su djeca izložena na internetu te samo 5 % roditelja smatra da je dijete odalo određenu osobnu informaciju, dok u stvarnosti to čini gotovo 50 % djece. Nadalje, samo 7 % roditelja misli kako se njihovo dijete susrelo sa seksualnim komentarima na internetu, a samo 4 % roditelja misli kako je njihovo dijete bilo zlostavljano na internetu dok su izjave djece o navedenim događajima dvostruko su češće. Roditelji najviše podcjenjuju probleme koje doživljava najstarija dobna skupina (Livingstone i Bober, 2006). Prema istraživanju Núñez-Gómez i sur. (2021) provedenom na 1350 djece i adolescenata između 6 i 12 godina koji žive u Španjolskoj, javlja se međugeneracijska napetost između odraslih i djece u korištenju interneta te teškoće u postizanju konsenzusa i kvalitetne podrške pri korištenju interneta. Zaključuju da se djeci moraju dati digitalni alati, kompetencije i sigurnost kako bi ona u potpunosti razvila svoj digitalni identitet. Unatoč tome što roditelji često podcjenjuju rizike s kojima se njihova djeca susreću na internetu, istraživanja pokazuju da je važno pronaći ravnotežu između omogućavanja pristupa digitalnim resursima i zaštite djece od potencijalnih opasnosti. Korištenje interneta pruža brojne prednosti mladima uključujući povećanu društvenu podršku, akademsko obogaćivanje i međukulturalne interakcije širom svijeta, ali postoje i popratni rizici za korištenje interneta (Anderson, 2001; Colley i Maltby, 2008; Goold i sur., 2003; Hunley i sur., 2005; Joiner i sur., 2005). Današnju djecu možemo smatrati djecom „digitalne generacije“ (Despotovic i sur. 2011). Adolescenti također često dijele osobne i identifikacijske informacije o sebi na internetu. Ti detalji mogu uključivati lokaciju njihovog doma, fotografije koje otkrivaju ili opise seksualnog ponašanja i upotrebe supstanci (Back i sur., 2010; Hinduja i Patchin, 2008; Moreno i sur., 2009). Istaknutost i značaj društvenih medija za adolescente, stoga, vjerojatno proizlazi iz sve veće važnosti istraživanja identiteta, samoizražavanja, prijateljstava i prihvaćanja od strane vršnjaka koje se događa tijekom ovog razdoblja (Gerwin, Kaliebe i Daigle, 2018). Razumijevanje odnosa između korištenja društvenih medija i rizičnog ponašanja tijekom adolescencije je ključno (Casey, 2015; Shulman i sur., 2016). Iako internet može donijeti mnoge koristi, kao što su društvena podrška i obogaćivanje obrazovanja, važno je razumjeti rizike povezane s njegovim korištenjem, posebno u kontekstu adolescenata. U tom smislu, digitalne navike mladih ljudi postaju ključne za očuvanje njihove sigurnosti. Naime, djeca koja provode više vremena na internetu često nisu dovoljno svjesna opasnosti poput izlaganja osobnih podataka, što zahtijeva razvoj digitalnih kompetencija i odgovornosti. Posljednjih godina pojavilo se zlostavljanje putem digitalnih uređaja koristeći internet, koje se često zajednički naziva *cyberbullying*. Odgovarajuća definicija je da je to agresivan, namjeran čin koji izvodi grupa ili pojedinac, koristeći elektroničke oblike kontakta, opetovano i tijekom vremena protiv žrtve koja se ne može lako obraniti (Smith i sur., 2008). *Cyberbullying* ili internetsko nasilje utječe na do trećinu mladih i povezuje se s raznim zdravstvenim problemima, od kojih su neki ozbiljni, kao što su suicidalne misli (Agatston i sur., 2007; Hinduja i Patchin, 2010; Ybarra i sur., 2007). Odnosi se na verbalnu agresiju, neprijateljstvo i druge pokušaje nanošenja štete u online komunikaciji i obuhvaća izraze kao što su *flaming*, *outing*, govor mržnje, online drama i *online* uznemiravanje (Calvete i sur., 2010; Pyżalski, 2012). Može uključivati objavljivanje lažnih profila, distribuciju klevetničkih informacija i internetsko uhođenje (Rivers i Noret, 2010). Osim fizičkih prijetnji i prijetnji domu, obitelji i prijateljstvima, opće je poznato da velik dio internetskog nasilja (poput zlostavljanja licem u lice) ima seksualne komponente, uključujući seksualno uznemiravanje te homofobno i seksističko omalovažavanje (Ehman i Gross, 2019). Govor mržnje i predrasuda je također čest (Henry, 2013). Mrežno zlostavljanje, uznemiravanje, agresija i uhođenje također se pojavljuju u kontekstu adolescentskih veza, među školskim vršnjacima i u vezama započetim na internetu (Rivers i Noret, 2010; Stonard i sur., 2014). Dredge i sur. (2014) navode da su posljedice internetskog nasilja štetnije od tradicionalnog vršnjačkog nasilja zbog sveprisutnog javnog objavljivanja uvredljivih komentara i veće publike koja svjedoči nasilju, anonimnosti zlostavljača, trajnosti i snage pisane riječi ili objavljene fotografije, mogućnosti da se žrtvu zlostavlja neprestano tijekom cijelog dana kao i nemogućnost bijega žrtve. Osim *cyberbullyinga*, postoje i drugi oblici digitalnih prijetnji koje mogu utjecati na mentalno zdravlje i sigurnost djece i adolescenata, a njihovo razumijevanje ključno je za izradu učinkovitih strategija zaštite. Sigurnost na internetu vrlo je važna za današnju mladež jer provode i do 10 sati dnevno koristeći različite oblike medija (Jones i sur., 2009; Lenhart i sur., 2010; Rideout i sur., 2010). Prema autorima Puri i Sgarma (2016) i Zeng i sur. (2016) što više vremena djeca i adolescenti provode na internetu, osjećaju se usamljenijima. Autori Šolić i Velki (2019) navode kako se dobna granica prvog pristupanja internetu spušta diljem Europe. Prema istraživanju Livingston i sur. (2011) u Danskoj i Švedskoj je prosječna dob sedam godina, dok je u nekim zemljama osam godina (Norveška, Velika Britanija itd.). U nekim zemljama poput Austrije, Turske ili Portugala, je prosječna dob djece koja prvi put pristupaju internetu, deset godina. „Budući da se sve mlađa i mlađa djeca počinju koristiti internetom, internetske sigurnosne kampanje i inicijative moraju biti usmjerene i prilagođene mlađim dobnim skupinama, a istodobno održavati postojeće napore vezane uz sigurnost starije djece“ (Šolić i Velki, 2019). Pojam sigurnost na internetu se također se naziva i mrežna sigurnost, digitalna sigurnost ili e-sigurnost, a ovaj koncept povezuje se s rizicima s kojima se pojedinci suočavaju na internetu i načinima na koje se mogu zaštititi od tih rizika (Kimpe i sur., 2019). S obzirom na sveprisutnost interneta u životima kod djece i adolescenata, raste i zabrinutost za njihovu sigurnost na internetu. Pružanje sigurnog okruženja zahtijeva dubinsko razumijevanje vrsta i rasprostranjenosti internetskih rizika s kojima se mladi korisnici interneta suočavaju, kao i najučinkovitijih rješenja za ublažavanje tih rizika (Farrukh i sur., 2014). Povijesno gledano, sigurnost na internetu, se uglavnom smatrala tehničkim problemom fokusiranim na hardverska i softverska rješenja (Parsons i sur., 2014), ali mnogi autori smatraju da su mnogi sigurnosni problemi uzrokovani upravo ljudski faktorom (Lukasik, 2011; Orshesky, 2003; Sasse i sur., 2001). S obzirom na to da djeca provode sve više vremena na internetu, istraživanja na nacionalnoj razini pokušavaju utvrditi obrasce digitalnog ponašanja i sigurnosne prijetnje s kojima se mladi susreću u Hrvatskoj. Na razini Republike Hrvatske 2014. godine je provedeno Nacionalno istraživanje o ponašanju korisnika na internetu te znanju o pitanjima sigurnosti i privatnosti na uzorku od 4859 sudionika, od kojih su trećina bili srednjoškolci, trećina studenti i trećina zaposleni odrasli ljudi. Istraživanje je pokazalo kako unatoč znanju, većina ljudi je lakovjerna, ali se i sigurnosno izrazito riskantno ponaša. Većina ljudi je odala svoju lozinku na trik pitanju dobrovoljno (Šolić i Velki, 2019). Prvo hrvatsko istraživanje o digitalnim navikama djece i sigurnosti na internetu provedeno je 2017. godine, a sudjelovalo je 1.017 djece u dobi od 9 do 17 godina te njihovi roditelji, a provedeno je u sklopu projekta *EU (Global) Kids Online*. Utvrđeno je da djeca pristupaju internetu kada žele i trebaju najčešće putem pametnih telefona, ali i dalje više vremena provode družeći se uživo s prijateljima, količina vremena provedenog uz Internet raste s dobi, a djeca koja među svojim Facebook prijateljima imaju svoje roditelje češće posjećuju društvene mreže i komuniciraju s drugima putem aplikacija (Ciboci i sur., 2020). Tijekom osnovnoškolskog i srednjoškolskog obrazovanja, učenici se susreću s temama sigurnosti na internetu kroz nastavne predmete Hrvatski jezik i Informatika propisane predmetnim kurikulumima koji su utemeljeni na nacionalnom okvirnom kurikulumu. Osim škola, u Republici Hrvatskoj postoji i Nacionalni centar za sigurniji Internet koji svojim djelovanjem želi upozoriti djecu i mlade na rizike i opasnosti svakodnevnog korištenja interneta **Cilj i metode istraživanja ** ***Cilj*** S obzirom na mali broj znanstvenih istraživanja o sigurnosti na internetu među djecom i adolescentima u Republici Hrvatskoj, cilj istraživanja je ispitati razlike između stvarnog rizičnog ponašanja adolescenata na internetu i samoprocjene, utvrditi razinu svijesti o informacijskoj sigurnosti i potencijalnim rizicima te analizirati u kojoj se mjeri pridržavaju važnosti sigurnosnih preporuka prilikom korištenja računalnih sustava. Poseban naglasak stavljen je na utjecaj edukacija o sigurnosti na internetu koje učenici pohađaju tijekom svog osnovnoškolskog i srednjoškolskog obrazovanja. ***BKUIS*** U ovom istraživanju korišten je Bihevioralno-kognitivni upitnik internetske sigurnosti (BKUIS), koji su razvili i validirali Velki i Šolić (2020). Upitnik sadrži 17 varijabli raspoređenih u četiri subskale: dvije bihevioralne (samoprocjena rizičnog ponašanja i simulacija rizičnog ponašanja) i dvije kognitivne (kognitivni rizik i kognitivna važnost). Bihevioralne skale mjere rizična ponašanja ispitanika, dok kognitivne skale procjenjuju razinu svijesti o rizicima internetske sigurnosti. Primjeri varijabli uključuju učestalost dijeljenja lozinki i percepciju rizika od krađe novca prilikom korištenja internetskog bankarstva. Rezultati simulacijskih subskala izražavaju se kao zbroj odgovora (0-4), dok se za ostale subskale koristi aritmetička sredina. Rezultati se kreću od 0 (nema rizičnih ponašanja) do 4 (maksimalna rizična ponašanja i visoka svjesnost o rizicima). ***Uzorak*** U istraživanju je sudjelovalo 167 učenika srednjih škola, od čega 57 iz gimnazija i 110 iz strukovnih škola s prosječnom dobi od 16,5 godina. Korišten je izvorni faktorski model, a koeficijent pouzdanosti tipa Cronbachov alpha, za svih 17 varijabli, iznosio je 0,81, što ukazuje na visoku pouzdanost varijabli (KMO=0,787, χ2=1709,383, df=190, p<0,001) (Tablica 1). Tablica 1 *Prikaz KMO i Bralettovog testa zakrivljenosti* * *
**Kaiser-Meyer-Olkin Measure of Sampling Adequacy.** **,787**
**Bartlett’s Test of Sphericity** Approx. Chi-Square 1709,383
** ** df 190
** ** Sig. <,001
Normalnost distribucije podataka provjerena je Kolmogorov-Smirnov testom, koji je pokazao značajno odstupanje od normalne distribucije (Tablica 2) (Steinskog i sur., 2007). Analiza distribucije podataka dodatno potvrđuje ova odstupanja, što je vidljivo iz vrijednosti skewnessa (asimetrije) i kurtosisa (zašiljenosti). Prema kriterijima, ako skewness prelazi ±1, distribucija je značajno asimetrična, dok vrijednosti kurtosisa veće od ±3 ukazuju na odstupanja u zašiljenosti distribucije. U ovom istraživanju, kod više varijabli, poput "Obavijest od suradnika" (skewness = -2,895, kurtosis = 6,460), "Daje lozinku" (skewness = -2,556, kurtosis = 6,009) i "Posuđuje debitnu/kreditnu karticu" (skewness = -4,219, kurtosis = 19,961), jasno je vidljivo značajno odstupanje od normalne distribucije. S obzirom na to da test Kolmogorov-Smirnov potvrđuje ova odstupanja, za daljnju analizu podataka korišten je neparametrijski test Mann-Whitney U, budući da on ne zahtijeva pretpostavku normalnosti distribucije i prikladniji je za analizu ovakvih podataka. Tablica 2 *Deskriptivna statistika i odstupanje od normalne distribucije*
** ** **N** **Min** **Max** **Mean** **Std. Dev.** **Skewness** **Std. Error (Skew.)** **Kurtosis** **Std. Error (Kurt.)**
**Obavijest od suradnika** 167 1 2 1,91 0,287 -2,895 0,188 6,460 0,374
**Besplatni antivirus** 167 1 4 1,86 0,394 -0,585 0,188 5,909 0,374
**Promotivni materijali** 167 1 3 1,89 0,311 -2,553 0,188 4,570 0,374
**Privola za obradu osobnih podataka** 167 1 3 1,39 0,600 1,285 0,188 0,622 0,374
**Posuđuje podatke** 167 2 5 4,44 0,825 -1,358 0,188 0,986 0,374
**Daje lozinku** 167 2 5 4,72 0,676 -2,556 0,188 6,009 0,374
**Razdvaja privatno od službenog** 167 1 5 4,00 1,349 -1,133 0,188 0,006 0,374
**Dozvoljava kolegama** 167 1 5 4,43 0,908 -1,697 0,188 2,644 0,374
**Posuđuje debitnu/kreditnu karticu** 167 3 5 4,81 0,617 -4,219 0,188 19,961 0,374
**Otkriva PIN** 167 3 5 4,83 0,658 -4,542 0,188 21,675 0,374
**Radi sigurnosne kopije** 167 1 5 3,65 1,146 -0,746 0,188 -0,044 0,374
**Održavanje zaštite** 167 1 5 2,43 1,282 0,554 0,188 -0,922 0,374
**Odjavljivanje** 167 1 5 2,72 1,431 0,282 0,188 -1,327 0,374
**Provjeravanje prijenosnih medija** 167 1 5 2,53 1,260 0,537 0,188 -0,765 0,374
**Povremeno mijenjanje lozinke** 167 2 5 2,90 1,228 0,066 0,188 -0,998 0,374
**Krađa identiteta** 167 1 5 2,09 1,330 0,953 0,188 -0,459 0,374
**Krađa novaca** 167 1 5 2,02 1,439 1,148 0,188 -0,207 0,374
**Hakiranje osobnog računa** 167 1 5 2,01 1,329 1,148 0,188 -0,376 0,374
**Gubitak privatnih fotografija** 167 1 5 2,41 1,394 0,566 0,188 -1,036 0,374
**Zlouporaba kreditne ili debitne kartice** 167 1 5 1,93 1,459 1,270 0,188 -0,023 0,374
*** *** **Postupak** Istraživanje je provedeno od ožujka do svibnja 2024. u srednjim školama Sisačko-moslavačke županije, a empirijski podatci su prikupljeni putem anketnog upitnika (Matijević i sur., 2016). Upitnik je izrađen u Microsoft Formsu, uz prethodno pribavljene potrebne suglasnosti za sudjelovanje. Učenici su imali su mogućnost odustati od istraživanja u bilo kojem trenutku. Upitnik je ispunjavan online uz jasne upute, a rezultati su obrađeni statističkim programom IBM SPSS 20 (Brownlow, 2004). ** ** **Rezultati ** U istraživanju su postavljene četiri hipoteze. Prva hipoteza ispituje razliku između vrsta srednjih škola u procjeni rizičnog online učeničkog ponašanja na simulacijskoj skali te predviđa da postoji statistički značajna razlika između gimnazija i strukovnih škola. S obzirom na to da učenici gimnazija, prema svom kurikulumu, imaju više nastavnih predmeta koji ih podučavaju o sigurnosti na internetu, očekuje se da će njihovi rezultati na simulacijskom testu pokazati nižu razinu rizičnog ponašanja u odnosu na učenike strukovnih škola, čiji programi ne uključuju toliko sadržaja vezanih uz digitalnu sigurnost. Tablica 3 *Usporedba dvije grupe na simulacijskoj skali rizičnog online ponašanja*
**Obavijest od suradnika** **Besplatni antivirus** **Promotivni materijali** **Privola za obradu osobnih podataka**
**Mann-Whitney U** 2978,0000 3072,000 3121,000 2486,000
**Wilcoxon W** 4631,000 4725,000 9228,000 4139,000
**Z** -1,070 -,338 -,075 -2,659
**Asymp. Sig. (2-tailed)** ,285 ,735 ,940 ,008
*a. Grouping Variable: Smjer srednje škole* Na osnovu prikazanih rezultata iz tablica za Mann-Whitney U test (Tablica 3), p-vrijednosti (Asymp. Sig.) su sljedeće: obavijest od suradnika (p = 0,285), besplatni antivirus (p = 0,735), promotivni materijali (p = 0,940), privola za obradu osobnih podataka (p = 0,008). U varijablama obavijest od suradnika, besplatni antivirus i promotivni materijali nije utvrđena statistički značajna razlika između gimnazija i strukovnih škola na simulacijskoj skali u procjeni rizičnog ponašanja, čime se hipoteza ne može potvrditi. S druge strane, u varijabli privola za obradu osobnih podataka, gdje je p-vrijednost manja od 0.05, postoji statistički značajna razlika, što upućuje na to da vrsta srednje škole može imati različit utjecaj na pojedine aspekte rizičnog online ponašanja. Međutim, s obzirom na cjelokupne rezultate Mann-Whitney U testa, hipoteza se ne može potvrditi jer nije utvrđena statistički značajna razlika između gimnazija i strukovnih škola u procjeni rizičnog online ponašanja na simulacijskoj skali. Druga hipoteza predviđa postojanje statistički značajna razlike između vrsta srednjih škola u učeničkoj samoprocjeni rizičnog online ponašanja. Prema Panaderu i sur. (2016), samoprocjena se odnosi na širok raspon mehanizama i tehnika pomoću kojih učenici opisuju (tj. procjenjuju) i eventualno dodjeljuju zasluge ili vrijednosti (tj. procjenjuju) kvalitetu vlastitih procesa učenja i proizvoda. Samoevaluacija zahtijeva od učenika da u određenim vremenskim intervalima ocjenjuju vlastito ponašanje (Shapiro i Cole, 1994). Očekuje se da će učenici četverogodišnjih srednjih škola (gimnazija) imati višu razinu samoprocjene rizičnog online ponašanja u usporedbi s učenicima strukovnih škola, pri čemu bi njihova samoprocjena mogla odstupati u odnosu na rezultate simulacijske skale. Jedan od najučinkovitijih pristupa povećanju sigurnosti na internetu među digitalnim građanima jest obrazovanje (Onyancha, 2015; Sharma i sur., 2015; Whittier, 2013). Shui Ng (2020) u svojem pregledu istraživanja ističe važnost edukacije u poboljšanju *online* ponašanja (Dhir i sur., 2016; Hur i sur., 2009; Ncube i Dube, 2016). Tablica 4 Samoprocjena rizičnog ponašanja po vrsti srednje škole
**Posuđuje podatke** **Daje lozinku** **Razdvaja privatno od službenog** **Dozvoljava kolegama** **Posuđuje debitnu/kreditnu karticu** **Otkriva PIN** **Rai sigurnosne kopije**
**Mann-Whitney U** 2969,500 3065,000 2753,000 2954,500 3016,000 3046,500 2461,000
**Wilcoxon W** 9074,500 4718,000 4406,000 9059,500 9121,000 9151,500 4114,000
**Z** -,645 -,354 -1,420 -,718 -,729 -,602 -2,371
**Asymp. Sig. (2-tailed)** ,519 ,724 ,156 ,473 ,466 ,547 ,018
*a. Grouping Variable: Smjer srednje škole* Rezultati Mann-Whitney U testa (Tablica 4) pokazuju da su za većinu varijabli osim *Radi sigurnosne kopije*, p-vrijednosti veće od konvencionalnog praga statističke značajnosti od 0,05 (npr. p = 0,519, p = 0,724, p = 0,156, itd.). To znači da nema dovoljno dokaza koji bi potvrdili hipotezu o postojanju statistički značajne razlike u samoprocjeni rizičnog online ponašanja između učenika gimnazija i strukovnih škola. Slijedom toga, hipoteza nije potvrđena. Iako hipoteza nije potvrđena, obrazovni sustav i dalje treba razvijati strategije za edukaciju učenika o sigurnom *online* ponašanju, neovisno o vrsti srednje škole. Cilj je povećati njihovu svijest i razviti sigurne digitalne navike, čime bi se unaprijedila njihova sposobnost prepoznavanja i izbjegavanja potencijalnih rizika na internetu. Treća hipoteza predviđa postojanje statistički značajne razlike između vrsta srednjih škola u samoprocjeni svijesti učenika o informacijskoj sigurnosti. Škola ima ključnu ulogu u kritičkom digitalnom opismenjavanju učenika i odgovornost ne samo za njihovu edukaciju o sigurnom korištenju interneta već i za informiranje roditelja o dječjem online iskustvu kod kuće. Cilj obrazovanja o sigurnosti na internetu jest osvijestiti korisnike o potencijalnim rizicima povezanim s upotrebom digitalnih alata, uključujući društvene mreže, online igre, e-poštu i razne komunikacijske platforme. Iako postoji značajan broj istraživanja o internetskoj sigurnosti, manje je radova koji se bave konkretnim mjerama koje škole mogu poduzeti za jačanje svijesti o sigurnosti na internetu (Franke i Brynielsson, 2014; Dong i sur., 2015; Kruse i sur., 2017; Mellado i sur., 2010; Rahim i sur., 2015). Uvidom u kurikulume nastavnih predmeta Hrvatski jezik, Informatika ili tehničkih smjerova (poput tehničar za računalstvo), očekuje se da će učenici četverogodišnjih srednjih škola, koji imaju navedene nastavne predmete, imati veće spoznaje o sigurnosti na internetu od učenika strukovnih škola, gdje su ovi predmeti manje zastupljeni ili ih nemaju uopće tijekom srednjoškolskog obrazovanja. Tablica 5 Samoprocjena razine svjesnosti o važnosti korištenja računalnih sustava i interneta
**Krađa identiteta** Krađa novaca** **Hakiranje osobnog računa** **Gubitak privatnih fotografija** **Zloupotreba kreditne ili debitne kartice**
**Mann-Whitney U** 2483,500 2589,500 2656,000 3135,000 2836,500
**Wilcoxon W** 4136,500 4242,500 4279,000 9240,000 4489,500
**Z** -2,354 -2,042 -1,881 ,000 -1,177
**Asymp. Sig. (2-tailed)** ,019 ,041 ,060 1,000 ,239
*a. Grouping Variable: Smjer srednje škole* Na temelju statističkih rezultata Mann-Whitney U testa i Wilcoxon W testa (Tablica 5), može se zaključiti da postoji statistički značajna razlika u percepciji prijetnji informacijske sigurnosti između učenika različitih vrsta srednjih škola, ali samo za određene varijable. Učenici koji pohađaju škole s naglaskom na IKT ili informatiku pokazuju veću svijest o sigurnosti na internetu u usporedbi s učenicima strukovnih škola, gdje su informatički predmeti manje zastupljeni ili ih uopće nema. Z-vrijednosti, koje su negativne za varijable poput Krađe identiteta (Z = -2,354) i Krađe novca (Z = -2,042), ukazuju na statistički značajnu razliku u rangovima između tih skupina. Međutim, za varijable poput Hakiranja osobnog računa, Gubitka privatnih fotografija i Zloupotrebe kreditne ili debitne kartice, p-vrijednosti su veće od 0,05, što znači da te razlike nisu statistički značajne. Dakle, iako se u tekstu navodi postojanje statistički značajnih razlika, ove razlike su statistički značajne samo za varijable u kojima je p-vrijednost manja od 0,05. Ovi nalazi potvrđuju hipotezu da je inkluzija predmeta o sigurnosti na internetu u kurikulume srednjih škola ključna za podizanje svijesti i zaštitu mladih od online prijetnji, s naglaskom na varijable koje su statistički značajne. Ovi rezultati naglašavaju važnost obrazovanja o sigurnosti na internetu u obrazovnom sustavu, no treba imati na umu da nisu sve varijable pokazale značajnu razliku. Četvrta hipoteza predviđa da postoji statistički značajna razlika u vrsti srednje škole u samoprocjeni svjesnosti potencijalnih rizika kod učenika. Internet je imao značajan pozitivan utjecaj na živote ljudi, ali je također donio brojne izazove, uključujući: *cyberbullying*, online prijevare, rasno zlostavljanje, pornografiju i online kockanja. Nedostatak svijesti i digitalne pismenosti često čini korisnike ranjivima na ove prijetnje. Prema istraživanjima, razina svijesti o sigurnosti na internetu i dalje je niska ili umjerena. Stoga je ključno od najranije dobi razvijati znanja i vještine potrebne za sigurno digitalno okruženje (Rahman i sur., 2020). Rezultati provedenog istraživanja pokazuju da postoji statistički značajna razlika u samoprocjeni svjesnosti potencijalnih rizika među učenicima iz različitih srednjih škola za većinu mjerenih varijabli čime se hipoteza potvrđuje. Ovi nalazi dodatno naglašavaju važnost sustavnog obrazovanja o internetskoj sigurnosti, ne samo u adolescentskoj dobi već i kroz rane stupnjeve obrazovanja. Tablica 6 Samoprocjena razine svjesnosti potencijalnih rizika
**Održavanje zaštite** Odjavljivanje** **Provjeravanje prijenosnih medijai** **Povremeno mijenjanje lozinki**
**Mann-Whitney U** 2062,500 2494,500 2365,500 2783,000
**Wilcoxon W** 3715,500 4147,500 4018,500 4436,000
**Z** -3,760 -2,220 -2,686 -1,220
**Asymp. Sig. (2-tailed)** <,001 ,026 ,007 ,223
*. Grouping Variable: Smjer srednje škole* Rezultati Mann-Whitney U testa (Tablica 6) pokazali su statistički značajnu razliku u percepciji važnosti određenih sigurnosnih praksi među učenicima različitih vrsta srednjih škola. Najznačajnija razlika utvrđena je kod varijable održavanje zaštite (Asymp. Sig. < 0,001), što ukazuje na to da učenici iz gimnazija i strukovnih škola imaju različite stavove o važnosti kontinuiranog osiguravanja digitalne sigurnosti. Također, značajna razlika pronađena je i kod varijable provjeravanje prijenosnih medija (Asymp. Sig. = 0,007), što sugerira da učenici iz različitih škola različito percipiraju potrebu za provjerom sigurnosti eksternih uređaja i medija. Statistički značajna razlika zabilježena je i kod varijable odjavljivanje (Asymp. Sig. = 0,026), iako manje izražena u odnosu na prethodne varijable. S druge strane, kod varijable povremeno mijenjanje lozinki nije pronađena statistički značajna razlika (Asymp. Sig. = 0,223), što sugerira da učenici bez obzira na vrstu srednje škole imaju slične stavove o važnosti ove sigurnosne prakse. Dobiveni rezultati potvrđuju da obrazovni pristup, dostupnost tehnoloških resursa te socio-ekonomski i kulturni čimbenici mogu utjecati na svijest učenika o sigurnosti na internetu. Učenici koji pohađaju škole s izraženijim naglaskom na IKT pokazuju veću svijest o sigurnosnim praksama, dok učenici iz škola u kojima su takvi sadržaji manje zastupljeni ili ih nema uopće rjeđe percipiraju važnost određenih sigurnosnih mjera. Ovi nalazi upućuju na potrebu za unaprjeđenjem obrazovnih programa iz područja internetske sigurnosti u svim vrstama srednjih škola, s ciljem razvijanja boljih sigurnosnih navika kod mladih i smanjenja njihove izloženosti digitalnim prijetnjama. **Rasprava ** Rezultati ovog istraživanja ukazuju na značajne razlike u samoprocjeni rizičnog ponašanja adolescenata na internetu među učenicima gimnazija i strukovnih škola, što je u skladu s prethodnim istraživanjima koja sugeriraju da vrsta obrazovne institucije može oblikovati percepciju i ponašanje učenika u digitalnom okruženju (Livingstone i sur., 2011; Núñez-Gómez i sur., 2021). Na primjer, učenici gimnazija, koji su obično izloženi korištenju digitalnih tehnologija kroz informatiku, pokazuju višu razinu svijesti o sigurnosnim rizicima na internetu. S druge strane, učenici strukovnih škola, koji mogu biti manje izloženi temama vezanim uz digitalnu pismenost u školama, često iskazuju nižu svijest o potrebnim sigurnosnim mjerama, poput redovite promjene lozinki ili provjere sigurnosti prijenosnih uređaja. Ovi rezultati potvrđuju teoretske okvire koji problematično korištenje interneta i sigurnost na mreži promatraju kroz bihevioralne i kognitivne dimenzije (Beard i Wolf, 2001; Velki i Šolić, 2020), dok konkretni primjeri ukazuju na to da obrazovni pristupi u različitim vrstama škola mogu značajno oblikovati studentske digitalne navike. Hipoteza 1, koja je istraživala razlike između gimnazija i strukovnih škola u procjeni rizičnog online ponašanja, nije potvrđena, osim za varijablu privole za obradu osobnih podataka. To sugerira da su učenici iz obje vrste škola izjednačeni u samoprocjeni rizičnog ponašanja na internetu, no razlika u percepciji privatnosti i upravljanju osobnim podacima ukazuje na specifične obrazovne razlike u pristupu sigurnosti na internetu. Učenici gimnazija pokazuju višu razinu svijesti o zaštiti svojih podataka, dok učenici strukovnih škola možda nisu dovoljno educirani o važnosti privatnosti, što se očituje u nižoj razini privole za obradu osobnih podataka. Hipoteza 2, koja se bavila razlikama između vrsta srednjih škola u samoprocjeni rizičnog ponašanja, nije potvrđena, osim u slučaju provjere prijenosnih medija, gdje je zabilježena statistički značajna razlika. Učenici gimnazija su češće prijavljivali korištenje antivirusnog softvera i redovito ažuriranje postavki privatnosti na društvenim mrežama, dok učenici strukovnih škola rjeđe poduzimaju ovakve korake. Ovi podaci sugeriraju da se teorijski okvir problematično korištenja interneta može primijeniti i unutar različitih obrazovnih sustava, s naglaskom na specifične razlike u praksama vezanim uz digitalnu sigurnost. Razlike u obrazovnim programima između gimnazija i strukovnih škola mogu igrati ključnu ulogu u oblikovanju tih sigurnosnih navika. Hipoteza 3, koja je istraživala razlike u samoprocjeni svijesti o potencijalnim rizicima između učenika različitih vrsta srednjih škola, potvrđena je. Mann-Whitney test ukazao je na statistički značajne razlike između gimnazija i strukovnih škola za varijable održavanja zaštite i provjere prijenosnih medija. Ovi rezultati jasno naglašavaju utjecaj obrazovnog pristupa i kurikuluma na percepciju sigurnosti na internetu među učenicima. Učenici gimnazija, koji su češće izloženi obrazovnim programima koji uključuju digitalnu pismenost i sigurnost na internetu, pokazuju bolju svijest o rizicima, kao što su održavanje zaštite na uređajima i provjera sigurnosti prijenosnih medija. Nasuprot tome, učenici strukovnih škola pokazuju manju sklonost poduzimanju zaštitnih mjera, što ukazuje na potrebu za jačim obrazovnim intervencijama u tim institucijama. Hipoteza 4, koja je također istraživala razlike u samoprocjeni svjesnosti o potencijalnim rizicima, potvrđena je. Mann-Whitney test ponovno je pokazao statistički značajne razlike između gimnazija i strukovnih škola za varijable održavanja zaštite i provjere prijenosnih medija. Ovi nalazi ponovo podcrtavaju važnost uključivanja edukativnih programa o kibernetičkoj sigurnosti u školski kurikulum kako bi se ojačala svijest i praksa učenika u digitalnom okruženju. Učenici koji su prošli obrazovne programe o sigurnosti na internetu, koji su specifično dizajnirani u gimnazijama, pokazuju veće razumijevanje sigurnosnih praksi, što može značajno smanjiti rizike povezane s nesigurnim ponašanjem na internetu. Ovi rezultati također ukazuju na važnost obrazovnih programa koji se bave sigurnošću na internetu. Rezultati se slažu s prethodnim istraživanjima koja potvrđuju potrebu za integracijom edukacija o sigurnosti na internetu u školski kurikulum (Livingstone i sur., 2011; Velki i Šolić, 2020). Iako su samoprocjene rizičnih ponašanja među učenicima gimnazija i strukovnih škola slične, specifične digitalne navike, kao što su provjera prijenosnih medija i održavanje zaštite, mogu biti pod utjecajem različitih obrazovnih pristupa i iskustava. Na primjer, obrazovni programi u gimnazijama koji uključuju tematiku digitalne sigurnosti i kritičkog razmišljanja o medijima mogu pomoći učenicima u razvijanju većih vještina zaštite svojih podataka, dok bi slični programi u strukovnim školama mogli smanjiti nesklad između samoprocjene i stvarnog ponašanja. Obrazovni programi koji fokusiraju na podizanje svijesti o rizicima internetskog ponašanja mogu znatno smanjiti nesklad između samoprocjene i stvarnog ponašanja učenika. Rezultati također sugeriraju da se svijest o rizicima i digitalnoj sigurnosti razvija kroz formalno obrazovanje. Na primjer, učenici gimnazija koji su redovito izloženi kurikulumu koji uključuje sigurnost na internetu često bolje razumiju važnost zaštite privatnosti, dok učenici strukovnih škola, koji možda nemaju istu količinu obrazovanja o digitalnim prijetnjama, mogu biti skloniji rizičnim ponašanjima. Razlike u kurikulumu između gimnazija i strukovnih škola mogu značajno utjecati na percepciju i ponašanje učenika u digitalnom prostoru, osobito u kontekstu varijabli poput održavanja zaštite i provjere prijenosnih medija. Na primjer, učenici koji su uključeni u obrazovne aktivnosti usmjerene na sigurnost na internetu, poput radionica o zaštiti privatnosti, izvještavanju o prijetnjama i korištenju sigurnosnih postavki na društvenim mrežama, pokazali su značajno veću spremnost na implementaciju tih sigurnosnih mjera u svakodnevnom online ponašanju. Ovi nalazi naglašavaju važnost kontinuiranog obrazovanja o sigurnosti na internetu, koje bi trebalo postati sastavni dio obrazovnih programa, kako bi se smanjio nesklad između samoprocjene i stvarnog ponašanja učenika u digitalnom okruženju (Spada, 2014; Velki i Šolić, 2020). ** ** **Zaključak** Rezultati ovog istraživanja ukazuju na statistički značajnu razliku između učenika gimnazija i strukovnih škola u svijesti o specifičnim aspektima informacijske sigurnosti, kao što su održavanje zaštite i provjera prijenosnih medija. Ovi nalazi sugeriraju da obrazovni pristup i kurikulumi mogu imati ključnu ulogu u oblikovanju percepcije sigurnosnih rizika među učenicima. Stoga, uključivanje edukativnih programa o kibernetičkoj sigurnosti u školske kurikulume može značajno unaprijediti svijest i praksu učenika u digitalnom okruženju, čime bi se smanjio nesklad između samoprocjene i stvarnog ponašanja. Iako nisu sve hipoteze u istraživanju bile potvrđene, rezultati naglašavaju složenost faktora koji utječu na percepciju i svijest o sigurnosti na internetu među srednjoškolcima. Razlike između gimnazija i strukovnih škola ukazuju na to da obrazovni pristupi u različitim vrstama škola oblikuju specifične navike i stavove učenika prema digitalnoj sigurnosti. Potreba za daljnjim istraživanjima, koja će uzeti u obzir širi spektar varijabli i konteksta školovanja, postaje očita. Buduća istraživanja trebaju uključiti čimbenike poput socijalno-ekonomskog statusa, dostupnosti tehnologije kod kuće i kulturnih specifičnosti, koji također mogu značajno utjecati na digitalne navike učenika. Zaključci ovog istraživanja pružaju nove uvide u percepciju sigurnosnih rizika među učenicima srednjih škola, ističući važnost vrste obrazovne institucije i kurikuluma kao ključnih faktora u oblikovanju njihove percepcije i ponašanja u digitalnom okruženju. Korištenje kvantitativne analize, konkretno Mann-Whitney U testa, omogućilo je objektivno ispitivanje razlika u percepciji sigurnosnih rizika. Ovi nalazi pružaju konkretne smjernice za daljnji razvoj i prilagodbu obrazovnih programa o kibernetičkoj sigurnosti u srednjim školama, s ciljem povećanja sigurnosti i smanjenja rizičnih ponašanja među učenicima. Istraživanje doprinosi razumijevanju složenosti percepcije sigurnosnih rizika u digitalnom okruženju među srednjoškolcima. Rezultati ukazuju na potrebu za daljnjim teorijskim i praktičnim smjernicama za razvoj obrazovnih strategija koje će adresirati specifične izazove u oblikovanju digitalnih navika i povećanju svijesti o sigurnosti među mladima. Preporuke za buduće istraživanje uključuju proširenje uzorka na različite geografske regije i tipove škola kako bi se omogućila šira generalizacija rezultata. Također, preporuča se kombiniranje kvantitativnih i kvalitativnih metoda, što bi omogućilo dublje razumijevanje iskustava i stavova učenika. Razvoj sveobuhvatnijih mjera za procjenu percepcije rizika, koje će obuhvatiti različite aspekte digitalne sigurnosti, kao i uključivanje relevantnih kontekstualnih faktora poput socijalno-ekonomskog statusa, moglo bi dodatno obogatiti rezultate. Dodatno, provedba longitudinalnih istraživanja koja bi pratila promjene u percepciji i ponašanju učenika kroz vrijeme mogla bi omogućiti dublje uvide u dinamiku razvoja digitalnih navika. Evaluacija učinkovitosti obrazovnih programa o kibernetičkoj sigurnosti, kroz praćenje njihovog utjecaja na sigurnosnu praksu učenika, također bi pružila dragocjene informacije za usmjeravanje budućih obrazovnih politika i strategija. ** ** **Literatura ** Aboujaoude, E. (2010). Problematic Internet use: An overview. *World Psychiatry*, *9*(2), 85–90. https://doi.org/10.1002/j.2051-5545.2010.tb00278.x Agatston, P. 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[https://hrcak.srce.hr/249659](https://hrcak.srce.hr/249659) Whittier, D. B. (2013). Cyberethics: Envisioning character education in cyberspace. *Peabody Journal* *of Education*, 88(2), 225-242. [https://doi.org/10.1080/0161956X.2013.775882](https://doi.org/10.1080/0161956X.2013.775882) *Zakon o kibernetičkoj sigurnosti operatora ključnih usluga i davatelja digitalnih usluga*. (2018). Pribavljeno Veljača 12, 2024, s [https://narodne-novine.nn.hr/clanci/sluzbeni/2018\_07\_64\_1305.html](https://narodne-novine.nn.hr/clanci/sluzbeni/2018_07_64_1305.html%20Pristupljeno%2012). Zheng, X. i Zhao, W. (2015). Relationship between Internet altruistic behavior and hope of middle-school students: The mediating role of self-efficacy and self-esteem. *Psychological Development and Education*, 31(4), 428–436. ** **
[![logo stoo2_1 (no).png](https://hub.ufzg.hr/uploads/images/gallery/2024-09/scaled-1680-/logo-stoo2-1-no.png)](https://hub.ufzg.hr/uploads/images/gallery/2024-09/logo-stoo2-1-no.png) **Teaching (Today for) Tomorrow:** **Bridging the Gap between the Classroom and Reality** 3rd International Scientific and Art Conference Faculty of Teacher Education, University of Zagreb in cooperation with the Croatian Academy of Sciences and Arts
**Internet safety in adolescents**
##### **Abstract**
Internet use has increased significantly over the past twenty years, but with this growth has come concerns about problematic internet use that can cause psychological problems. This includes activities such as video games, social media, web streaming and online shopping, and children and young people are particularly at risk. The aim of this study was to investigate the differences between young people's actual risk behavior online and their self-assessment and awareness of information security, according to school type. The research was conducted in secondary schools in Sisak-Moslavina County on a sample of 167 students with an average age of 16.5 years, using the Behavioral Cognitive Internet Safety Questionnaire (BKUIS). The questionnaire measures risk behavior and safety awareness using 17 questions divided into four subscales. The results showed a high reliability of the questionnaire (Cronbach's alpha = 0.81), and due to the deviation of the data from the normal distribution, the non-parametric Mann-Whitney U-test was used. The results showed that there are no statistically significant differences in risk behaviors between the different types of high schools, except in certain areas such as consent to process personal data and portable media review. A statistically significant difference was found in online safety awareness, with high school students showing better knowledge of maintaining protection and safety practices. In summary, while students have access to Internet safety education programs within the school system, more needs to be done to raise awareness and reduce risky behavior among youth.
***Key words:***
adolescents; BKUIS; Internet; Internet security; risky behaviors
# Literature learning and digital skills among high school students
[![logo stoo2_1 (no).png](https://hub.ufzg.hr/uploads/images/gallery/2024-09/scaled-1680-/logo-stoo2-1-no.png)](https://hub.ufzg.hr/uploads/images/gallery/2024-09/logo-stoo2-1-no.png) **Teaching (Today for) Tomorrow:** **Bridging the Gap between the Classroom and Reality** 3rd International Scientific and Art Conference Faculty of Teacher Education, University of Zagreb in cooperation with the Croatian Academy of Sciences and Arts
##### **Tonuzi Macaj Edlira** *University of Tirana, Albania* *edliralib@yahoo.com*
**Section - Education for digital transformation****Paper number: 44****Category: Professional paper**
##### **Abstract**
This study examines high school students' attitudes toward integrating technology into literature learning, emphasizing their perspectives on digital tools in literary analysis. It explores how technological advancements influence their engagement with artistic works and how students perceive the effectiveness of different digital models in enhancing literary understanding. The research investigates students' willingness to adopt new reading models incorporating digital skills and their role in shaping these approaches. The central hypothesis suggests that providing students with technology-supported models for engaging with literary works – or opportunities for active participation in their implementation – can enhance their reading comprehension and analytical skills. To assess these attitudes, the study employs the quantitative approach, using a thematic survey to measure how literature classes are conducted in high schools. Throughout this approach, it is possible to capture students' preferences and receptiveness to digital tools in literature learning. The paper offers valuable insights into the intersection of literature and digital tools by describing and interpreting survey data obtained by 117 high school students who anonymously responded to the survey. The study case findings highlight students' openness to innovative approaches and their appreciation for literature when learning experiences incorporate technology, ultimately demonstrating the evolving role of digital methods in literary education.
***Key words:***
digital tools, high school, learning, literature, skills, youth
**Introduction** In today’s rapidly evolving technological landscape, digital advancements’ integration into education presents unprecedented opportunities to enhance student engagement and learning outcomes. This study examines high school students’ attitudes toward incorporating technology into literature learning, exploring how digital tools can enrich their interaction with literary texts. The research focuses on the effectiveness of alternating different instructional models to foster deeper literary analysis and how these approaches can integrate essential digital skills for modern education. It posits that introducing technology-supported frameworks for literary study – alongside opportunities for active student participation in their implementation – can significantly improve reading comprehension and critical thinking skills. To investigate these perspectives, the study employs a concrete approach to examine current literature class structures with students' interaction. By synthesizing situational data from a thematic questionnaire sent to high school students online in various high schools, but mainly the major cities in Albania (Tirana, Durrës), this research provides valuable insights into the intersection of literature education and digital literacy. Ultimately, the findings underscore both students’ and educators’ readiness to embrace innovative methods, highlighting their enthusiasm and openness for technology-enhanced literature learning and the potential for qualitative improvements of students with literary engagement. **Literature review** Technology integration into education has been a subject of extensive research and debate, with numerous studies highlighting its potential to enhance student engagement and learning outcomes. One significant area of focus has been the use of digital tools in the teaching of literature. According to McKnight (McKnight et al., 2016), technology can transform traditional classrooms by providing interactive and engaging platforms for students to explore literary texts. This aligns with the findings of Beers (2003), who emphasized that digital resources can facilitate a deeper understanding of complex literary themes by offering diverse perspectives and interactive elements. Teachers can improve readability at the secondary (6-12) level by choosing practical strategies. Several studies have explored specific technological applications in literature education. For instance, O'Brien and Scharber (2008) discuss the benefits of digital storytelling, which allows students to create multimedia presentations of their interpretations of literary works. This approach enhances their comprehension but also develops their digital literacy skills. Similarly, Herrington, Parker, and Boase-Jelinek (2014) highlight the role of online forums and collaborative platforms in fostering discussions and critical thinking among students, enabling them to engage with literature in a more dynamic and participatory manner. The importance of digital literacy in contemporary education is underscored by scholars such as Gilster (1997), who define it as the ability to understand and use information in multiple formats from various sources when presented via computers. Digital literacy involves analysing, interpreting, and creating content using digital tools in literature education. This is supported by Leu et al. (2004), who argue that digital literacy skills are essential for students to navigate and make sense of the vast information available online. Research has also examined the impact of new pedagogical models on student engagement and learning. For example, Mishra and Koehler's (2006) Technological Pedagogical Content Knowledge (TPACK) framework emphasizes the need for teachers to integrate technology effectively into their teaching practices to enhance student learning. This framework has been widely adopted in studies exploring technology integration in literature education, such as those by Young and Bush (2004), who found that using technology in literature classes can lead to student motivation and engagement. Furthermore, studies have shown that students' active participation in creating and using digital content can significantly enhance their learning experience. According to Jenkins et al. (2009), participatory culture, where students are encouraged to contribute to and collaborate on digital projects, can lead to more meaningful and engaged learning. This is echoed by Kress (2003), who highlights the importance of multimodal literacy, where students learn to interpret and create texts using various modes of communication, including digital media. All these ideas are in the way to promote a better affiliation for young students with literature classes surpassing traditional reading. Critics contend that conventional reading instruction, which frequently emphasizes isolated skills, may fail to promote a comprehensive understanding of texts and might not effectively meet the varied needs of students (Jones, 2021). The existing literature suggests the use of technology in literature classes. It can enhance student engagement, comprehension, and digital literacy skills. Students are less likely to develop a passion for reading or recognize its relevance if the content is not meaningful and engaging (Edmunds & Bauserman, 2006). Adopting new pedagogical models and encouraging active student participation can create more dynamic and effective learning environments. This study builds on these findings by exploring how high school students in two major cities in Albania can use technology to engage with literature more deeply and how new models can improve their reading and learning competencies. This theoretical and practical background at the international level is essential to consider, as it also impacts the educational strategies in Albania. Documents such as the *Education Strategy 2021-2026* (Strategjia, 2021-2026: 12), the *Law on Education* (Law 2012, 2015), and specific guidelines emphasize the significance of digital competence. However, empirical studies on the implementation of this competence remain limited. Even in published cases, aspects such as digital infrastructure and the effectiveness of interventions – if any have been undertaken – still lack measurable assessments (Macaj & Shehri, 2022: 29). For this reason, the present study aims to assess the extent and manner in which high school students engage with literature learning concerning to the opportunities provided by technology. Consequently, students should be guided through concrete models to develop this competence effectively. The data obtained from the questionnaire offers an overview of the current situation and indicates students' tendencies regarding how they perceive and approach literature and the study of literary works. **Methodology ** This paper employs a quantitative research methodology, using a structured questionnaire as the primary data collection instrument. The concrete objective is to examine high school students’ attitudes toward integrating digital tools in literature education and to assess whether these tools enhance their ability to analyse artistic works. A questionnaire was designed to evaluate students’ digital competencies, engagement with literature, and openness to technology-supported learning models. The questionnaire includes a combination of closed-ended, multiple-choice, and Likert-scale questions to measure students’ preferences, perceptions, and experiences regarding the use of technology in literature classes. It was distributed electronically via Google Forms to high school students across various schools in Tirana and other cities in Albania, ensuring broad participation and a diverse sample. The survey maintains respondent anonymity and is structured to capture data on students’ digital literacy, reading habits, and attitudes toward interactive learning methods. This method was chosen because it is convenient for problem statement and searching interest closely related to teaching and learning, specifically literature, as one of the most important subjects in the high school curricula of Albania. Traditional literature teaching in Albania's high schools often relies on conventional teaching methods, such as textbook-based instruction, teacher-led analysis, and memorization of literary concepts. These approaches can limit students' engagement, critical thinking, and ability to interact with literary texts dynamically. The lack of digital tools in literature instruction may limit students' ability to engage with texts independently, enhance their analytical skills, and establish meaningful connections with literature. This study seeks to test the following hypotheses: The integration of digital tools enhances students' comprehension and engagement with literary works by offering interactive and multimedia-based approaches to reading and analysis; Students who use technology in literature education demonstrate improved analytical skills compared to those who rely solely on traditional teaching methods; High school students show a positive attitude toward incorporating technology in literature learning, indicating a willingness to adopt new digital reading models. Although this research examines the structure of literature classes and technology integration, it does not employ a qualitative research approach. The study does not include direct program analysis, in-depth classroom observations, or qualitative interviews with educators. Instead, all findings are based on students' self-reported experiences and perceptions, as gathered through the questionnaire. High school students were the sole assessors of literature education methods, providing insights into their preferences and the effectiveness of digital tools in literary learning. As they are the main actors in perceiving knowledge, the interest is on explaining and interpreting their direct answers. The data collected will contribute to understanding how digital methodologies can support literature education and whether students perceive technology as an effective tool for analysing artistic works. **Data analysis** A survey was conducted among high school students using a structured questionnaire. The survey comprised ten research questions designed to assess the impact of technology and its associated facilities on knowledge acquisition, focused on literature as a school subject. Four optional questions were included, inquiring about the respondent’s city, school, class, and gender. Analysis of these variables suggests that geographical location, institutional affiliation, and year of study play a significant role in shaping educational distribution and academic performance at the national level. The survey sample included students from high schools in Tirana, Albania’s most developed city, and Durrës, the country's second most developed city, facilitating a comparative analysis between students from these urban centres. There were also submitted answers from small towns such as Klos, Mallakastër, and Kukës. However, their representation was so limited that it did not help to build a comparative analysis between urban areas – such as the two major cities – and less developed areas like these small towns. Nevertheless, their responses are still considered in the overall assessment. For research purposes, two large cities are sufficient. Of the 117 students who participated in the survey, 114 provided information about their city of residence, while 115 specified the school they attended. The gender distribution of respondents indicates that 91 students (79.8%) were female, while 23 students (20.2%) were male. Additionally, 25 respondents were from high schools in Durrës, whereas 86 were from institutions in Tirana, indicating that the sample was drawn from Tirana’s high schools. The surveyed students were enrolled across the three years of general secondary education. The collected data offer insights into the research questions, particularly concerning the availability of technological infrastructure in schools. According to the responses, 41.9% (49 students) reported that their schools do not have an active technological infrastructure to support literature classes, whereas 58.1% (68 students) stated that their schools use technological tools to facilitate literature instruction, as illustrated in Figure 1. *Figure **1** Activated tech infrastructure for literature classes* *[![image.png](https://hub.ufzg.hr/uploads/images/gallery/2025-05/scaled-1680-/Tqbimage.png)](https://hub.ufzg.hr/uploads/images/gallery/2025-05/Tqbimage.png)* The second research question discovers the instructional methods employed in literature classes across the surveyed schools. According to the collected data, 53 respondents (45.3%) reported that literature instruction in their schools follows a traditional approach, adhering strictly to the standard curriculum guidelines. A nearly equal proportion – 55 students (47%) – indicated that their literature classes incorporate a combination of instructional methods and techniques. The most effective pedagogical approach seems to be a balanced combination of traditional and technological models, as indicated by 18 students (15.4%), who reported that literature instruction in their schools incorporates both approaches alternately. However, only 5 respondents (4.3%) indicated that literature classes in their schools rely exclusively on technological resources, while an equal number reported the use of other, unspecified methods. The response distribution for this question is presented in Figure 2. *Figure **2** How the literature classes are conducted* [![image.png](https://hub.ufzg.hr/uploads/images/gallery/2025-05/scaled-1680-/rdaimage.png)](https://hub.ufzg.hr/uploads/images/gallery/2025-05/rdaimage.png) The questionnaire aimed to gather students’ perspectives on whether technology facilitates their engagement with reading. The responses indicate that 53 students (45.3%) believe technology can sometimes fulfil this role. Additionally, 25 respondents (25.6%) perceive technology as frequently enhancing their reading engagement. A further 21.4% (25 students) reported that technology consistently supports their reading engagement, while 4 students affirmed this view without explicitly selecting the “always” frequency. Conversely, only 5 respondents (4.3%) rejected the notion that technology contributes to their engagement with reading. The distribution of these responses is illustrated in Figure 3. Figure 3 Technology impact [![image.png](https://hub.ufzg.hr/uploads/images/gallery/2025-05/scaled-1680-/Dvyimage.png)](https://hub.ufzg.hr/uploads/images/gallery/2025-05/Dvyimage.png) Students were asked about their preferred book format for reading, including print books, audiobooks, digital books, or other formats. The responses indicate a strong preference for traditional print books, with 91 students (77.8%) favouring this format. Additionally, 15 students (12.8%) expressed a preference for digital books, while 6 students (5.1%) indicated a preference for audiobooks. A smaller proportion, 5 students (4.3%), selected other unspecified formats. The distribution of these preferences is depicted in Figure 4. *Figure **4** Preferred formats of books* [![image.png](https://hub.ufzg.hr/uploads/images/gallery/2025-05/scaled-1680-/Kmyimage.png)](https://hub.ufzg.hr/uploads/images/gallery/2025-05/Kmyimage.png) To assess students' engagement with literature class projects, they were asked whether they have independently conducted research related to their literature studies using technological resources. This question aimed to determine the extent of their active participation in literary research and projects. The responses indicate that 39 students (33.3%) occasionally utilize technology for literary research and reading, while a slightly higher proportion – 44 students (35.9%) – frequently make use of technological resources in literature classes. Additionally, 19 students (16.2%) reported consistently engaging with technology for this purpose. Conversely, 9 students (7.7%) stated that they have never conducted literary research using technology, while 8 students (6.8%) reported rarely doing so. Overall, the majority of respondents acknowledged having used technological resources for literature classes. The distribution of responses is illustrated in Figure 5. Figure 5 The frequency of independent research [![image.png](https://hub.ufzg.hr/uploads/images/gallery/2025-05/scaled-1680-/HSOimage.png)](https://hub.ufzg.hr/uploads/images/gallery/2025-05/HSOimage.png) Given that students demonstrate both the ability and preference for utilizing technological tools and resources to enhance their literature classes – and acknowledge the role of these methods in improving reading quality – the survey aimed to identify the specific techniques students employ for research projects or literature lessons in their schools. The findings indicate that 40 students (34.2%) reported using diagrams, charts, or tables created with various computer programs. Additionally, 29 students (24.8%) stated that they incorporate PowerPoint presentations into their literature classes, while 12 students (10.3%) have utilized Prezi as an explanatory tool. Moreover, 32 students (27.4%) acknowledged employing diverse technological techniques or applications in their literature studies. A small proportion, 4 students (3.4%), reported engaging in the creation of animated content. The distribution of these responses is illustrated in Figure 6. Figure 6 Used techniques for literature classes [![image.png](https://hub.ufzg.hr/uploads/images/gallery/2025-05/scaled-1680-/aaWimage.png)](https://hub.ufzg.hr/uploads/images/gallery/2025-05/aaWimage.png) Technology has become an integral aspect of the younger generation’s daily life, while traditional schooling continues to uphold its established principles. The survey sought to explore how students navigate the transition toward a learning process increasingly influenced by technology, which introduces new approaches to reading and interpreting literary works. Although the majority acknowledge their connection with technology and actively utilize its tools to enhance their learning experience, a key question in the survey aimed to assess whether this technological engagement genuinely improves reading quality, comprehension skills, communication, critical thinking, and other competencies. The data show that 71 respondents (60.7%) believe technology has a positive impact on the reading process. Meanwhile, 35 students (29.9%) do not perceive a connection between reading and technological tools, whereas only 11 respondents (9.4%) believe that the technology affects negatively on reading. The results are shown in Figure 7. Figure 7 Impact of technology on the quality of Reading [![image.png](https://hub.ufzg.hr/uploads/images/gallery/2025-05/scaled-1680-/Mleimage.png)](https://hub.ufzg.hr/uploads/images/gallery/2025-05/Mleimage.png) Given the strong connection between students and the digital environment, the survey aimed to identify which interactive digital formats – such as a literary chat, an online educational game, a video collage, an animated film, or a digital map – would be most suitable for engaging with artistic works in alternative ways. The findings indicate that 35 students (29.9%) considered an animated film the most appropriate option. Similarly, 30 respondents (25.6%) preferred an online interactive game, while 26 students (22.2%) selected a literary chat as their preferred method of engagement. Additionally, 12 students (10.3%) preferred a video collage, whereas another 14 (12%) favoured creating a digital map or a literary itinerary for a character. The distribution of these preferences is illustrated in Figure 8. Figure 8 Favourite practices to choose for a literary work-study [![image.png](https://hub.ufzg.hr/uploads/images/gallery/2025-05/scaled-1680-/tX1image.png)](https://hub.ufzg.hr/uploads/images/gallery/2025-05/tX1image.png) To further assess whether students are open to engaging with new approaches to literature, the survey explored their willingness to continue interacting with alternative models. The results indicate that 76 students (65%) responded favourably to this idea. In contrast, 9 students (7.7%) disagreed, while 32 students (27.3%) stated that such an approach would be beneficial in certain circumstances. The distribution of these responses is illustrated in Figure 9. Figure 9 interaction between students and technology [![image.png](https://hub.ufzg.hr/uploads/images/gallery/2025-05/scaled-1680-/I5Mimage.png)](https://hub.ufzg.hr/uploads/images/gallery/2025-05/I5Mimage.png) One of the main reasons for integrating technology and the digital environment into students' individual or group assignments and research projects – focused on the reading, analysis, and interpretation of literary works – is the creation of effective models. These models provide new alternatives for studying literary works while fostering the development of students' learning skills and competencies, particularly in digital literacy. Regarding this approach, 49 students (41.9%) agreed that effective models contribute to the improvement of their skills, while 38 students (32.5%) strongly agreed. A further 26 students (22.2%) expressed uncertainty on this matter. Only 4 students (3.4%) disagreed, respectively 3 (2.6%) and only 1 of them (0.8%) strongly disagreed with this approach, as illustrated in Figure 10. Figure 10 impact of samples in improving skills [![image.png](https://hub.ufzg.hr/uploads/images/gallery/2025-05/scaled-1680-/Okgimage.png)](https://hub.ufzg.hr/uploads/images/gallery/2025-05/Okgimage.png) ** ** **Results and discussion** The respondents showed significant results related to literature classes and high school students' engagement. Firstly, there is a notable variation at the level of technological advancement across schools, even inside larger cities. Disparities exist in the equipment of schools with active technological infrastructure, not to mention in rural areas. Secondly, the pedagogical methods employed in teaching literature vary greatly among institutions. The majority of responses indicated that literature classes are primarily taught traditionally. However, schools that have attempted to integrate a blend of traditional and technological methods have shown better results at the national level. Today, students seem to be increasingly connected with technology. Among the 117 respondents to the questionnaire, 112 students (approximately 95.7%) agree that technology aids and facilitates their engagement with reading. Regarding preferences for book formats, it is clear that while the majority still favour traditional print books, there is a noticeable shift toward other formats enabled by technology. Traditional print books account for 77.8% of preferences, while alternative formats represent 22.2%. Although this shift reflects an increasing openness to digital formats, traditional print remains dominant. Despite the widespread belief that technology can enhance engagement with reading, students continue to prefer traditional methods of interacting with books. Regarding the use of technological tools in literature classes, over 90% of high school students report utilizing technology for projects or individual research related to literature. This usage spans various frequencies – rarely, occasionally, frequently, and always – indicating a substantial level of engagement with technological resources in literature education. When it comes to the most commonly used techniques and tools, the data suggest that students are developing new skills through their familiarity with technology. Diagrams, project presentations, and even animated creations have proven to be appealing and useful alternatives, especially in the context of literary topics. These practices contribute to the perception of literature as a subject that can be explored and understood in multiple ways, offering diverse avenues for interpretation and creative expression. More than 60% of students believe that technology has a notable impact on reading. The similar distribution of preferences for different models of engaging with literary works indicates that students are open to working in multiple directions simultaneously. This flexibility enables them to approach a literary piece from various perspectives, leading to a deeper and more nuanced understanding. The overwhelming majority of respondents favor continued interaction with the tools and methods technology offers for studying literary works. The responses regarding students' views on whether well-designed models supported by technology improve various skills – including digital literacy – indicate strong support for this approach. Specifically, based on the answers to the last question, 74.4% of students (strongly agree 32.5% and agree 41.9%) view the integration of technology as optimal for enhancing their learning experience, and they think that under the guidance of good technology-assisted models, students benefit and improve their digital skills. In contrast, 2.6% and 0.9% of respondents disagree and strongly disagree, respectively, while 22.2% remain unsure, with their views likely to change over time. **Conclusion** This study employed a quantitative approach to provide a concrete and comprehensive understanding of how technology can be integrated into high school literature education to enhance student engagement and learning outcomes. The survey was specifically dedicated to exploring young students' affinity for technology in literature classes, and data gathered showed valuable insights into the potential benefits and challenges associated with adopting innovative pedagogical models in literature instruction. The survey’s findings reveal a significant disparity in technological advancement across schools, with rural institutions trailing behind their urban counterparts. While traditional methods of literature instruction remain predominant, schools that incorporate various tools and teaching methods demonstrate improved educational outcomes. Although there is a clear preference for printed books among students, many recognize the potential of technology to enhance their engagement with reading. The widespread use of technological tools in literature classes indicates considerable integration of digital resources into the learning process. The data also suggest that students are increasingly attracted to new, technology-supported methods – such as project presentations and interactive diagrams – that facilitate a multidimensional approach to understanding literature. The consensus among students is that technology enhances their reading experience and also aids in the development of essential skills. This growing trend toward integrating technology into literature education reflects a broader shift toward modern, technology-enhanced learning models, with many students expressing a positive outlook on these innovative approaches. **References** Beers, Kylene. (2003). *When Kids Can’t Read: What Teachers Can Do. A Guide for Teachers 6-12*. London: Heinemann. Edmunds, K. M., & Bauserman, K. L. (2006). “What teachers can learn about reading motivation through conversations with children.” *The Reading Teacher*, 59(5), 414-424. Gilster, Paul. (1997), *Digital Literacy*. New York: Wiley Computer Pub. Herrington, Jan, Parker, Jenny, and Boase-Jelinek, Daniel. (2014). “Connected authentic learning: Reflection and intentional learning” *Australian Journal of Education* 58(1):23-35 Jenkins, Henry, et al. (2009). *Confronting the Challenges of Participatory Culture: Media Education for the 21st Century.* Cambridge: The MIT Press. Jones, L. (2021). “Challenges in Traditional Reading Instruction: A Critical Review”. *Journal of Educational Research,* 58(4), 312-329. Kress, Gunther. (2003). *Literacy in the New Media Age*. London: Routledge. Leu, D. J., Jr., Kinzer, C. K., Coiro, J., Cammack, D. (2004). Toward a theory of new literacies emerging from the Internet and other ICT. In R.B. Ruddell & N. Unrau (Eds.), *Theoretical Models and Processes of Reading,* Fifth Edition (1568-1611). Newark, DE: International Reading Association. Ligji Nr. 56/2015. (2015, May 28). *Për disa ndryshime në sistemin arsimor parauniversitar në Republikën e Shqipërisë.* Fletorja Zyrtare e Ligjeve të RSH. Ligji Nr. 69/2012. (2012). *Për sistemin arsimor parauniversitar në Republikën e Shqipërisë.* Fletorja Zyrtare e Ligjeve të RSH. (Amended by Ligji Nr. 56/2015, May 28, 2015). Macaj, E., & Shehri, D. (2022). *Letërsi, teknologji, edukim* (E. Çali, Ed.). Mediaprint. McKnight, Katherine; O'Malley, Kimberly; Ruzic, Roxanne; Horsley, Maria Kelly; Franey, John J.; Bassett, Katherine. (2016). “Teaching in a Digital Age: How Educators Use Technology to Improve Student Learning”. *Journal of Research on Technology in Education*, 48 (3): 194-211 2016 Mishra, P., & Koehler, M. J. (2006). “Technological Pedagogical Content Knowledge: A Framework for Teacher Knowledge.” *Teachers College Record*, 108(6), 1017-1054. [https://doi.org/10.1111/j.1467-9620.2006.00684.x](https://doi.org/10.1111/j.1467-9620.2006.00684.x) O'Brien, D. & Scharber, C. (2008). “Digital Literacies Go to School: Potholes and Possibilities.” *Journal of Adolescent & Adult Literacy*, 52(1), 66-68. Young, C.A. & Bush, J. (2004). “Teaching the English Language Arts With Technology: A Critical Approach and Pedagogical Framework.” *Contemporary Issues in Technology and Teacher Education*, 4(1), 1-22. Waynesville, NC USA: Society for Information Technology & Teacher Education. [https://www.learntechlib.org/primary/p/21903/](https://www.learntechlib.org/primary/p/21903/). **The questionnaire used for the study purpose:** *This questionnaire aims to evaluate the digital competence and engagement of high school students concerning the practical use of technology to improve the quality of literature education. The questionnaire ensures and respects the anonymity of each respondent. We thank you for your participation!* **City: \_\_\_** **School: \_\_\_** **Class: \_\_\_** **Gender: M/F** **1. ****Does your school have technological infrastructure used during literature lessons? **a) Yes b) No **2. ****How is literature taught in your school? **a) Traditionally b) Using technological tools c) With alternative models d) With a mix of methods and techniques e) Other **3. ****Do you think technology helps students engage more with reading? **a) Yes, always b) No, never c) Sometimes d) Often e) Always **4. ****Which formats do you prefer for reading literary works? **a) Printed book b) Audiobook c) Digital book d) Other format **5. ****Have you successfully conducted independent research on a literature topic using technology?** a) Always b) Never c) Sometimes d) Often e) Rarely **6. ****Which techniques do you use for literature project topics? **a) Diagrams/graphs/tables b) Animations c) PowerPoint (PPT) d) Prezi e) Other **7. ****Do you think that using technology improves the quality of reading? **a) Yes b) No c) It’s unrelated **8. ****In your opinion, which of the following suggestions would be most effective for you to use (or approach differently) when working with a literary work?** a) Literary chat b) Creating an educational video game c) Video collage d) An animated film e) A digital map (literary itinerary) f) Book trailer g) Other, specify **9. ****Would you like to engage with new models to approach literature differently? **a) Yes b) No c) Maybe **10. ****Do you think that under the guidance of good technology-assisted models, students benefit and improve their digital skills? **a) I strongly agree b) I agree c) Maybe d) I disagree e) I strongly disagree # Istraživanje AI pismenosti studenata
[![logo stoo2_1 (no).png](https://hub.ufzg.hr/uploads/images/gallery/2024-09/scaled-1680-/logo-stoo2-1-no.png)](https://hub.ufzg.hr/uploads/images/gallery/2024-09/logo-stoo2-1-no.png) **Odgoj danas za sutra:** **Premošćivanje jaza između učionice i realnosti** 3\. međunarodna znanstvena i umjetnička konferencija Učiteljskoga fakulteta Sveučilišta u Zagrebu Suvremene teme u odgoju i obrazovanju – STOO4 u suradnji s Hrvatskom akademijom znanosti i umjetnosti
##### **Ružica Jurčević** *Filozofski fakultet Sveučilišta u Zagrebu, Hrvatska* *rjurcevi@ffzg.unizg.hr*
**Sekcija - Odgoj i obrazovanje za digitalnu transformaciju****Broj rada: 45****Kategorija: Izvorni znanstveni rad**
##### **Sažetak**
U ovom radu predstavljeni su rezultati istraživanja provedenog 2024. godine, s ciljem ispitivanja znanja, stavova i navika studenata u korištenju umjetne inteligencije, odnosno ispitivanja stupnja njihove *AI* pismenosti. *AI* pismenost, koja se prepoznaje kao dodatak digitalnoj pismenosti, obuhvaća sposobnost odgovorne i učinkovite primjene alata umjetne inteligencije uz razumijevanje etičkih implikacija njihova korištenja. Istraživanje je provedeno na uzorku od 210 studenata prijediplomskih i diplomskih studija Sveučilišta u Zagrebu, a podaci su prikupljeni putem *online* anketnog upitnika. Centralni fokus istraživanja bio je ispitati razumijevanje studenata o *AI* tehnologiji, njezinom utjecaju na proces učenja, učestalosti korištenja *AI* alata u učenju te razinu njihove etičke svijesti o *AI* tehnologiji. Rezultati pokazuju da studenti nisu u potpunosti integrirali *AI* alate u sve aspekte svog učenja, već ih koriste u ograničenom opsegu, prepoznajući njihovu korisnost za specifične zadatke poput pomoći u pisanju, analizi podataka i rješavanju matematičkih problema. Iako prepoznaju prednosti koje *AI* alati mogu pružiti, razina svijesti o njihovoj etičkoj uporabi ostaje relativno niska, što upućuje na potrebu za razvojem obrazovnih programa koji će se usmjeriti na odgovornu i etičnu primjenu *AI* tehnologije.
***Ključne riječi:***
AI alati; etika; suvremeno obrazovanje; obrazovni programi; umjetna inteligencija
**Uvod ** Umjetna inteligencija (eng. *artificial intelligence*; *AI*) sve se više integrira u suvremeno društvo, donoseći transformativne promjene u brojnim područjima ljudskog djelovanja, poput gospodarstva, obrazovanja, zdravstva i sigurnosti. Ove promjene otvaraju nove prilike, ali i nameću potrebu za pažljivim promišljanjem o njenom dugoročnom utjecaju na društvo. Povećanje broja međunarodnih dokumenata koji se bave regulacijom umjetne inteligencije jasno ukazuje na sve veću važnost *AI* tehnologije u današnjem svijetu. Primjeri uključuju smjernice OECD-a (2019) o transparentnom i odgovornom korištenju alata umjetne inteligencije (*AI* alata), izvještaj Europske komisije, JRC-a i OECD-a (2021) u kojem se naglašava važnost važnost etičkog pristupa u razvoju i upotrebi *AI* tehnologije te smjernice Ujedinjenih naroda (2022) u kojima se ističe potreba za oblikovanjem globalnih standarda u razvoju *AI* tehnologije. Uz to, Europski parlament je 2024. godine izglasao *Europski zakon o umjetnoj inteligenciji *koji za cilj ima razviti mehanizme za odgovorno upravljanje i primjene *AI* tehnologije. Ovi dokumenti odražavaju zajednički napor međunarodne zajednice u oblikovanju okvira za sigurnu i odgovornu uporabu umjetne inteligencije u različitim kontekstima. U području odgoja i obrazovanja, potencijal umjetne inteligencije za transformaciju procesa poučavanja i učenja postaje sve izraženiji. Posljednjih se godina, uz smjernice za donositelje politika za prilagodbu odgoja i obrazovanja novim digitalnim tehnologijama (Miao, Holmes, Ronghuai i Hui, 2021; UNESCO, 2022), intenziviraju istraživanja o ulozi i utjecaju *AI* tehnologije na obrazovanje i odgoj (npr. Holmes, Bialik i Fadel, 2019; Yeh, Tsai, Tsai i Chang, 2019; Holmes, 2020; Lai, 2021; Chai, Lin, Jong, Dai, Chiu i Qin, 2021; Chocarro, Cortiñas i Marcos-Matás, 2021; Vadakkemulanjanal, Athira, Thomas, Jose, Roy i Prasad, 2024). Istovremeno, sve se veći naglasak stavlja na etičke aspekte primjene umjetne inteligencije u odgoju i obrazovanju, uključujući ključna pitanja privatnosti, sigurnosti, pristranosti, transparentnosti i odgovornosti (Goldsmith i Burton, 2017; Jobin, Ienca i Vayena, 2019; Hagendorff, 2020; Kuipers, 2020; Garrett, Beard i Fiesler, 2020; Borenstein i Howard, 2021; Green, 2021; Ashok, Madan, Joha i Sivarajah, 2022). Pekinški konsenzus o umjetnoj inteligenciji i obrazovanju (eng. *Beijing Consensus on Artificial Intelligence and Education*) (UNESCO, 2019) jedan je od ključnih dokumenata koji razmatra ulogu umjetne inteligencije u transformaciji odgoja i obrazovanja. U dokumentu se naglašava važnost razvoja *AI* tehnologije u skladu s temeljnim ljudskim pravima, dostojanstvom i univerzalnim vrijednostima. Poseban naglasak stavlja se na primjenu *AI* tehnologije za personalizaciju obrazovnih iskustava, prilagodbu procesa učenja individualnim potrebama učenika te osnaživanje nastavnika u njihovoj profesionalnoj ulozi. Također, istaknuta je potreba za razvojem i primjenom AI tehnologija na etičan, transparentan i odgovoran način, pri čemu se posebna pažnja posvećuje pitanjima privatnosti, pristranosti i sigurnosti. Međutim, ključni aspekt dokumenta je promicanje razvoja novih kompetencija unutar šireg ovira digitalne pismenosti, koje su usmjerene na odgovornu uporabu *AI* alata. Te se kompetencije oblikuju u konceptu *AI* pismenosti, koji uključuje razumijevanje osnovnih principa umjetne inteligencije, njezinih mogućnosti, izazova i etičkih implikacija. Long i Magerko (2020) definiraju *AI* pismenost kao skup kompetencija koji omogućuje pojedincima da kritički procjenjuju *AI* tehnologiju, učinkovito komuniciraju s njom i koriste je kao alat za rješavanje zadataka. Za Yija (2021) ona označava sposobnost pojedinca ne samo da koristi umjetnu inteligenciju, već i da kritički razmatra njezinu promjenjivost i utjecaj na društvo. Prema Kongu i Zhangu (2021), *AI* pismenost obuhvaća tri dimenzije: kognitivnu, afektivnu i sociokulturnu, koje zajedno čine osnovu za odgovorno korištenje *AI* tehnologija u svakodnevnom životu i profesionalnim okruženjima. Kognitivna dimenzija odnosi se na razumijevanje osnovnih koncepata umjetne inteligencije i njezino korištenje za procjenu i razumijevanje stvarnog svijeta. Afektivna dimenzija uključuje kritičko razumijevanje uloge i utjecaja umjetne inteligencije na društvo, kao i vlastite kompetencije u radu s *AI* tehnologijom. Sociokulturna dimenzija usmjerena je na etičku uporabu umjetne inteligencije. Kao i svaki oblik pismenosti, *AI* pismenost se oslanja na temelje klasične pismenosti, a uključuje i razvoj specifičnih znanja, stavova te navika i ponašanja potrebnih za kritičko, odgovorno i učinkovito korištenje umjetne inteligencije. U skladu s navedenim, dimenzije *AI* pismenosti za potrebe ovog rada strukturirane su na sljedeći način: a) *Razumijevanje umjetne inteligencije i etičkih aspekata*. Ova kategorija obuhvaća razumijevanje koncepta umjetne inteligencije, kao i svijest o etičkim aspektima njezina korištenja. b) *Učinkovita i odgovorna primjena umjetne inteligencije*. Ova kategorija obuhvaća sposobnost odgovorne, učinkovite i etičke integracije *AI* tehnologije u proces učenja. c) *Kritičko promišljanje o umjetnoj inteligenciji*. Ova kategorija obuhvaća kritičko razmišljanje i o ulozi i utjecaju umjetne inteligencije na društvo u cjelini, kao i njezinim dugoročnim učincima. Navedene dimenzije korištene su kao okvir za istraživanje *AI* pismenosti studenata prikazanog u nastavku rada. ** ** **Metodologija** Cilj istraživanja bio je ispitati stupanj *AI* pismenosti studenata prijediplomskih i diplomskih studija Sveučilišta u Zagrebu. *AI* pismenost, koja se danas sve više prepoznaje kao sastavni dio šireg okvira digitalne pismenosti, podrazumijeva sposobnost odgovorne i učinkovite upotrebe *AI* alata uz svijest o etičkim obilježjima njihove primjene. Središnji fokus istraživanja bio je utvrditi u kojoj mjeri studenti razumiju koncept umjetne inteligencije i etičke implikacije njezinog korištenja, u kojoj mjeri koriste *AI* alate u učenju te koji su njihovi stavovi i promišljanja o umjetnoj inteligenciji. Na temelju toga postavljena su tri glavna istraživačka pitanja: 1. U kojoj mjeri su studenti Sveučilišta u Zagrebu upoznati s umjetnom inteligencijom i etičkim aspektima njezina korištenja? 2. Koriste li i na koji način studenti Sveučilišta u Zagrebu alate umjetne inteligencije u svojem učenju? 3. Koji su stavovi studenata Sveučilišta u Zagrebu prema umjetnoj inteligenciji? Za potrebe istraživanja primijenjena je kvantitativna metodologija, pri čemu je kao istraživački instrument korišten *online* anketni upitnik. Upitnik je sadržavao 20 pitanja koja su uključivala kombinaciju otvorenih pitanja, Likertovih skala i višestrukih izbora. Upitnik je bio strukturiran u nekoliko dijelova, od kojih je svaki bio usmjeren na određeni aspekt *AI* pismenosti, slijedeći postavljena istraživačka pitanja. Prvi dio obuhvatio je pitanja o socio-demografskim karakteristikama ispitanika, uključujući spol i dobnu skupinu. Drugi dio bio je posvećen ispitivanju razine upoznatosti ispitanika s umjetnom inteligencijom i etičkim pitanjima povezanim s njezinom upotrebom. U trećem dijelu istraživala se učestalost korištenja *AI* alata u učenju, a u posljednjem dijelu stavovi i promišljanja ispitanika o umjetnoj inteligenciji. Istraživanje je provedeno od veljače do srpnja 2024. godine. Na službene adrese svih fakulteta Sveučilišta u Zagrebu poslana je molba za diseminaciju poveznice na upitnik studentima kako bi se osiguralo pridržavanje etičkih načela istraživanja, odnosno izbjeglo njihovo kršenje. U uvodnom dijelu upitnika ispitanicima je objašnjen cilj istraživanja, uz naglasak na poštivanje načela anonimnosti i dobrovoljnosti njihovog sudjelovanja. Obrada podataka izvršena je pomoću statističkoga programa SPSS (verzija 23). Za utvrđivanje unutarnje konzistentnosti (pouzdanosti) upitnika korišten je *Cronbach alfa* koeficijent, koji iznosi 0,752, što ukazuje na zadovoljavajuću razinu pouzdanosti upitnika. **Rezultati** U istraživanju je sudjelovalo 210 studenata prijediplomskih i diplomskih studija Sveučilišta u Zagrebu. Od tog broja, 131 (62%) bio je ženskog spola, dok je 79 (38%) bilo muškog spola. Što se tiče dobne strukture, 135 studenata (64%) pripadalo je dobnoj skupini od 19 do 22 godine, dok je 75 studenata (36%) bilo u dobnoj skupini od 23 godine i više. Rezultati istraživanja prikazani su opisno, tablično i grafički, u skladu s postavljenim istraživačkim pitanjima. ***Upoznatost s umjetnom inteligencijom i etičkim aspektima korištenja *** Kako bi se odgovorilo na prvo istraživačko pitanje, postavljena su pitanja koja su obuhvatila samoprocjenu ispitanika o njihovoj upoznatosti s umjetnom inteligencijom i etičkim aspektima korištenja *AI* alata u njihovom učenju. Iz Grafikona 1 vidljivo je da gotovo polovica studenata (45%) navodi da poznaje osnovne koncepte umjetne inteligencije, dok se 36% smatra dobro upućenima. Njih 13% izjavljuje da zna vrlo malo, dok samo 6% ispitanika smatra da zna vrlo mnogo. Grafikon 1 Samoprocjena ispitanika o njihovoj upoznatosti s umjetnom inteligencijom (%) [![image.png](https://hub.ufzg.hr/uploads/images/gallery/2025-05/scaled-1680-/kndimage.png)](https://hub.ufzg.hr/uploads/images/gallery/2025-05/kndimage.png) U drugom pitanju ispitanici su bili zamoljeni da pokušaju sami definirati umjetnu inteligenciju. Njihovi odgovori mogu se podijeliti u nekoliko kategorija (Tablica 1). Dok je neki opisuju kao naprednu tehnologiju, sustav ili softver sposoban oponašati ljudsku inteligenciju, drugi je doživljavaju kao računalni program ili algoritam koji omogućuje pronalaženje odgovora na postavljena pitanja. Ipak, većina ispitanika vidi umjetnu inteligenciju kao koristan alat koji pomaže u olakšavanju svakodnevnih aktivnosti i zadataka, i to ako se koristi na ispravan (etičan) način. Tablica 1 Kategorije definicija umjetne inteligencije prema ispitanicima
**Umjetna inteligencija kao...** **Odgovori ispitanika**
.. tehnologija *Umjetna inteligencija je oblik života tehnologije koji ima neke karakteristike čovjeka. * *Tehnologija koja razmišlja poput ljudi te velik dio misaonog procesa preuzima na sebe.* *Umjetna inteligencija je tehnologija koja omogućuje računalima da uče, razmišljaju i donose odluke slično kao ljudi. * *Umjetna inteligencija se odnosi na tehnologiju stvorenu tako da imitira ljudski mozak i zamjenjuje ljude u obavljanju nekih radnji.*
... program *Umjetna inteligencija je tip programa koji je razvijen kako bi mogao lakše odgovarati na pitanja koja ljudi postavljaju.* *Umjetna inteligencija program koji ima mogućnost korištenja velike količine znanja i pronalaženjem korelacije među opširnom količinom informacija.* *Umjetna inteligencija su razni programi koji posjeduju gotovo znanje. * *Umjetna inteligencija je kompjuterski program koja kao da imitira neuronsku mrežu čovjeka, odnosno imitira čovjekovo razmišljanje. *
... algoritam *Umjetna inteligencija je skup optimiziranih matematičkih algoritama namijenjenih za specifičnu stvar.* *Umjetna inteligencija su računalni algoritmi koji prepoznavanje uzoraka ili tema mogu rekreirati slike, videe ili tekstove.* *Umjetna inteligencija je sposobnost mehaničkog stroja odnosno algoritma da s velikom učinkovitošću obavlja određene zadatke.* *Sustavi koji prema početnim podacima i zadanim algoritmima pronalaze rješenja na pitanja koja im se postave po prvi put. *
.. alat *Umjetna inteligencija je za mene alat koji bi nam mogao olakšati svakodnevni život ako se koristi na ispravan način (bez zloupotrebe).* *Umjetna inteligencija alat je koji nam može služiti kao potpora našem radu ako ga koristimo na ispravan način i u ispravne svrhe. * *Umjetna inteligencija je digitalni alat koji je ima mogućnosti širokog spektra korištenja te je njegova prvotna funkcija asistiranja u radu i izvršavanju nekih zadataka. * *Umjetna inteligencija je alat pomaže ljudima da posao obave brže, preciznije i bez naprezanja mozga.*
*Napomena: Prikazan je samo dio odgovora ispitanika.* Na pitanje o tome u kojoj mjeri smatraju da su upoznati s pravilima ili etikom korištenja *AI* alata, 35% ispitanika izjavljuje da poznaje osnovne koncepte, dok 29% smatra da posjeduje vrlo malo znanja o ovoj temi (Grafikon 2). Otprilike jedna četvrtina studenata ocjenjuje se upućenim. Samo 3% ispitanika vjeruje da su u znatnoj mjeri upoznati s etičkim pravilima korištenja alata, dok 5% priznaje da je potpuno neupućeno. Grafikon 2 Samoprocjena ispitanika o njihovoj upoznatosti s etikom korištenja *AI* alata (%) [![image.png](https://hub.ufzg.hr/uploads/images/gallery/2025-05/scaled-1680-/lUdimage.png)](https://hub.ufzg.hr/uploads/images/gallery/2025-05/lUdimage.png) Slijedom toga, ispitanicima je postavljeno otvoreno pitanje s ciljem da prema vlastitom nahođenju navedu primjere ispravne i pogrešne upotrebe *AI* alata. Kao primjere ispravne upotrebe navode primjenu tih alata u medicini za bržu dijagnosticiranje bolesti, zatim kao pomoć u učenju i rješavanju zadataka, za ispravljanje gramatičkih i pravopisnih grešaka, za provjeru plagijata, itd. Također, istaknuli su korisnost tih alata u optimizaciji vremena, primjerice kroz sažimanje informacija, automatizaciju repetitivnih zadataka, rješavanje složenijih matematičkih problema te objašnjavanje određenih koncepata. Kao primjere pogrešne upotrebe *AI* alata, ispitanici su, osim varanja na ispitima i objave tekstova generiranih pomoću umjetne inteligencije kao vlastitih, naveli sve one koji nanose štetu drugom ljudskom biću ili ugrožavaju njegovo dostojanstvo i privatnost. Primjeri toga uključuju stvaranje lažnih informacija i fotografija, falsificiranje snimki i audio zapisa i sl. Na pitanje o tome tko bi trebao biti odgovoran za osiguranje etične i pravedne uporabe *AI* alata, odgovori ispitanika bili su podijeljeni (Tablica 2). Najveći broj ispitanika smatra da bi odgovornost trebali preuzeti pružatelji *AI* alata, odnosno tvrtke koje razvijaju te tehnologije. Veći dio ispitanika također smatra da bi odgovornost trebali preuzeti odgojno-obrazovne institucije, kao i svaki pojedinac zasebno, dok u nešto manjoj mjeri navode da bi odgovornost trebala snositi vlada. Zanimljivo je da jedna petina ispitanika smatra da bi neke druge institucije trebale biti odgovorne, ali nisu precizirali koja bi to institucija bila. Desetina ispitanika izrazila je nesigurnost u vezi ovog pitanja. Tablica 2 Percepcija odgovornosti za osiguranje etične uporabe *AI* alata (%)
**Odgovornost trebaju snositi: ** **Postotak**
Pružatelji AI alata (tvrtke) 60%
Odgojno-obrazovne institucije 45%
Svaki pojedinac 44%
Vlada 36%
Neka druga institucija 21%
Nisam siguran/a 9%
*Napomena: Zbroj postotaka ne iznosi 100% zbog mogućnosti višestrukog odabira* ***Korištenje AI alata u učenju *** Drugim dijelom upitnika nastojala se ispitati učestalost korištenja *AI* alata među studentima, kao i vrste alata koje najčešće koriste u učenju. Od ukupnog broja ispitanih, 37% studenata navodi da rijetko koristi *AI* alate u učenju, 31% ih koristi povremeno, a 15% jednom tjedno. Isti postotak ističe da ih nikada ne koristi, dok svega 2% koristi *AI* alate svakodnevno. Ovi rezultati koreliraju s rezultatima o motivaciji studenata za korištenje *AI* tehnologije. Na ljestvici od 1 do 5, značajniji udio čine oni s niskom razinom motiviranosti (41%) u usporedbi s onima koji pokazuju visoku razinu (33%). Među studentima koji koriste *AI* alate, najčešće korišteni su oni za kreiranje i oblikovanje pisanog teksta, pomoć pri domaćim zadaćama, rješavanje matematičkih zadataka i izradu prezentacijskih slajdova (Tablica 3). Također su popularni i alati za učenje stranih jezika te analizu podataka. Tablica 3 Odgovori ispitanika o vrsti korištenih *AI* alata u učenju (%)
**Vrste *AI* alata ** **Postotak**
AI alati za poboljšavanje vještina pisanja teksta 43%
AI alati za kreiranje sadržaja (npr. esej ili drugi pisani tekst) 39%
AI alati za pomoć pri domaćim zadaćama 29%
AI alati za pomoć pri rješavanju matematičkih zadataka 21%
AI alati za izradu prezentacijskih slajdova 21%
AI alati za učenje stranog jezika 20%
AI alati za provođenje analize podataka 20%
AI alati za kodiranje 15%
AI alati za uređivanje sadržaja 14%
AI alati za ilustriranje teksta 9%
AI alati za mentalni trening (vježbanje mozga) 6%
AI alati za pretvaranje tekstualnog sadržaja u audio ili obrnuto 6%
*Napomena: Zbroj postotaka ne iznosi 100% zbog mogućnosti višestrukog odabira* Na pitanje „Jesi li naišao/la na određene poteškoće ili izazove prilikom korištenja *AI* alata u svom učenju?“ gotovo polovica ispitanika (47%) odgovara potvrdno. Najčešće spomenute poteškoće su tzv. *AI* halucinacije, odnosno generiranje informacija koje su netočne, izmišljene ili neodgovarajuće. Primjeri takvih odgovora su sljedeći: O1: *Kada se od AI traži da izdvoji literaturu koja se bavi određenom temom, ponekad zna izmisliti autore i radove koji zapravo ne postoje* O2: *Često zna spojit dvije ne spojive stvari ili izvući informacije iz neke fiktivne priče“; „**Ako pitate Chat-GPT da vam izdvoji frazalne glagole iz teksta, AI neće razlikovati frazalne glagole od glagola s prijedlozima, a ponekad niti glagole od imenica* O3: *Krivo navođenje literature, davanje iznimno opširnog odgovora za temu koja me zanimala bez da je išta objašnjeno ili bez da su navedeni izvori (kada sam pitala program da pojasni neki pojam ili tvrdnju te pokušala doći do odgovora, nisam dobila od programa ono što sam tražila).* S druge strane, 32% studenata izjavljuje da nije naišlo na poteškoće tijekom korištenja *AI* alata, a 21% ne može procijeniti. Ispitanici su zatim zamoljeni da na ljestvici od 1 do 5 ocijene koliko su sigurni u svoju sposobnost učinkovite upotrebe *AI* alata u učenju. Rezultati pokazuju da se 47% studenata osjeća prilično sigurnima (ocjene 4 i 5), 21% izražava izraženu nesigurnost (ocjene 1 i 2), dok 32% se osjeća umjereno sigurnim (ocjena 3). ***Stavovi i kritičko promišljanje o umjetnoj inteligenciji*** Zadnjim dijelom upitnika nastojalo se ispitati mišljenje ispitanika o umjetnoj inteligenciji, kao i njihovo kritičko promišljanje o potencijalnim prednostima i rizicima primjene *AI* tehnologije u procesu učenja. Na pitanje „Kako se osjećaš u vezi korištenja *AI* alata u svojem učenju?“, jedna petina studenata izražava negativan stav (3% vrlo negativno, 18% djelomično negativno mišljenje), a oko 30% pozitivan stav (18% djelomično pozitivno, 14% vrlo pozitivno mišljenje). Najveći postotak, 44%, nema izraženo mišljenje, a 3% nije sigurno. Odgovori ispitanika o njihovoj spremnosti da preuzmu odgovornost za odgovore koje pruži *AI* alat ukazuju na umjereni oprez kada je riječ o korištenju tih odgovora u, primjerice, pisanom tekstu ili raspravi (Grafikon 3). Naime, većina ispitanika (30%) zauzima neutralan stav prema preuzimanju odgovornosti. Gotovo jednak postotak spremno je preuzeti odgovornost u određenoj mjeri (22%) ili u potpunosti (21%). Manji dio ispitanika (11%) izražava visoku razinu spremnosti, dok 16% ispitanika nije spremno preuzeti odgovornost za takve odgovore. Grafikon 3 Spremnost ispitanika za preuzimanje odgovornosti za odgovore *AI* alata (%) [![image.png](https://hub.ufzg.hr/uploads/images/gallery/2025-05/scaled-1680-/9qtimage.png)](https://hub.ufzg.hr/uploads/images/gallery/2025-05/9qtimage.png) Nadalje, gotovo polovica ispitanika (44%) izjavila je da se brine zbog privatnosti i sigurnosnih implikacija korištenja *AI* alata u učenju. S druge strane, dok četvrtina ispitanika (25%) ne brine, 13% o tome uopće nije razmišljalo, a 18% nije sigurno. Slično je i kod pitanja o važnosti *AI* alata za budući posao, gdje je odgovorima prisutna velika disperzija. Naime, gotovo polovica smatra da su *AI* alati važni, dok ostatak odgovora pokazuje različite stupnjeve nesigurnosti ili suprotnog mišljenja (Grafikon 4). Grafikon 4 Samoprocjena ispitanika o važnosti *AI* alata za budući posao (%) [![image.png](https://hub.ufzg.hr/uploads/images/gallery/2025-05/scaled-1680-/eMCimage.png)](https://hub.ufzg.hr/uploads/images/gallery/2025-05/eMCimage.png) Zadnja dva pitanja u upitniku odnosila su se na procjenu motivacije ispitanika za daljnjim učenjem o *AI* alatima. Dok četvrtina ispitanika navodi da nije zainteresirana, više od polovice (54%) izražava želju naučiti više o *AI* tehnologiji, njezinim funkcionalnostima, potencijalima i mogućnostima, kao i načinima na koje je moguće koristiti je kao koristan alat. Neki od odgovora na posljednje pitanje otvorenog tipa, „Ako si zainteresiran/a za učenje o *AI*-u, što je to što te čini znatiželjnim/nom?“, su sljedeći: O4: *Činjenica da je to nešto što nam je budućnost (a čak već i sadašnjost) i to da oni koji ne budu znali koristiti alate umjetne inteligencije vrlo brzo neće biti kompetentni. * O5: *Ako ću jednom raditi u nekoj od odgojno-obrazovnih ustanova, ja moram moći prepoznati korištenje AI aplikacija, a i sama ih moram dovoljno dobro znati kako bih možda mogla izvući pedagoški potencijal.* O6: *Ako ne budem učio o AI budem zastario i neću biti u toku s vremenom.* *Zanima me kako potaknuti mlade osobe na odgovorno i učinkovito korištenje alata u svrhu podupiranja njihova učenja. * 07: *A kao budući nastavnik trebam biti upoznata sa svim prednostima, nedostatcima i načinima za ispravno korištenje AI u nastavi, ali i da mogu podučavati učenike o ispravnom korištenju.* * * **Rasprava** Rezultati istraživanja prikazani u ovom radu pružaju uvid u stavove, znanje i iskustva studenata Sveučilišta u Zagrebu u vezi s umjetnom inteligencijom i njezinom primjenom u procesu učenja. Istraživanje je pokazalo da studenti uglavnom posjeduju osnovno ili površno razumijevanje umjetne inteligencije i etičkih smjernica vezanih uz njezinu upotrebu. Ipak, većina prepoznaje umjetnu inteligenciju kao koristan alat koji može značajno olakšati svakodnevne aktivnosti i zadatke, ističući potrebu da se koristi na ispravan, etički način. Ovi rezultati ukazuju na to da su studenti svjesni etičkih izazova i potencijalnih rizika povezanih s zloupotrebom *AI* alata, što se očituje u njihovoj opreznosti u pristupu ovoj tehnologiji. Naime, velik broj studenata izjavljuje da rijetko ili povremeno koristi *AI* alate, što može biti povezano s njihovim nešto negativnijim stavovima prema korištenju *AI* alata u učenju i umjetnoj inteligenciji općenito. Primjerice, dio njih ističe sljedeće: O8: *AI je zapravo vrlo opasan za društvo. Ne samo u ekonomskom smislu zauzimanja poslova, već prije svega za ljudske sposobnosti i samostalnost generalno. AI je nova razina tehnologije, koja će nas učiniti još nesposobnijima i hendikepiranima nego što već jesmo da se sami snalazimo, da sami tražimo.* O9: *Ako se previše naslonimo na umjetnu inteligenciju svijet postaje automatiziran i maksimalno efikasan te smatram da tako polagano umire kreativnost ljudi.* O10: *Utjecat će negativno. Otuđenjem čovjeka od njegove ljudske biti.* O11: *Svako korištenje AI-a je pogrešno. Ni sami ne znamo u kojem smjeru ide razvoj niti kako ga se želi usmjeriti. To je igranje s oružjem samouništenja. Tak i tak je već pametnija od nas, samo toga još nije svjesna.* Navedeni stavovi odražavaju duboku zabrinutost i skepticizam studenata prema umjetnoj inteligenciji. No, ta promišljanja o potencijalno štetnim utjecajima na pojedinca i društvo pokazuju da posjeduju određenu razinu etičke svijesti, što im omogućuje da prepoznaju i analiziraju moguće negativne posljedice primjene *AI* tehnologije te promišljaju o njeznoj odgovornoj uporabi. Nadalje, studenti prepoznaju korisnost *AI* alata u specifičnim zadacima poput pisanja i analize podataka, što sugerira da ih koriste na način koji je usklađen s njihovim obrazovnim potrebama i ciljevima. Ipak, iako mnogi studenti pokazuju visok stupanj sigurnosti u svojoj sposobnosti učinkovite upotrebe *AI* alata, još uvijek postoji značajan broj onih koji izražavaju nesigurnost. To ukazuje na potrebu za dodatnom podrškom koja bi povećala povjerenje studenata i uklonila strahove povezane s upotrebom *AI* alata, čime bi se omogućio njihov učinkovitiji angažman u vlastitom procesu učenja. Kada je riječ o preuzimanju odgovornosti u korištenju umjetne inteligencije i odgovora koji se generiraju određenim *AI* alatima, studenti su također oprezni u svojim odgovorima. Većina ispitanika smatra da tvrtke koje razvijaju *AI* alate trebaju preuzeti vodeću ulogu u kontroliranju i osiguravanju etičkih standarda. Značajan broj studenata također smatra da bi odgojno-obrazovni sustav trebao imati ključnu ulogu u obrazovanju o odgovornoj primjeni *AI*-a. Pozitivno je istaknuti da su studenti prepoznali i osobnu odgovornost za etičku upotrebu *AI* alata. To se jasno vidi u odgovorima na pitanju o ispravnim i pogrešnim primjerima upotrebe *AI* alata. U tom su pitanju, osim varanja na ispitima i prezentiranja tekstova generiranih pomoću umjetne inteligencije kao vlastitih, naveli i sve situacije u kojima *AI* alati nanose štetu drugim ljudima ili ugrožavaju njihovo dostojanstvo i privatnost. Zanimljivo je istaknuti da dio studenata vjeruje da bi odgovornost trebale imati i druge, neimenovane institucije, što otvara pitanje o tome tko još može ili treba preuzeti ulogu: međunarodne organizacije, nevladine organizacije ili možda određena profesionalna udruženja. Nadalje, veći broj studenata izražava zabrinutost zbog privatnosti i sigurnosnih aspekata *AI* tehnologija, dok manji broj nije razmišljao o tim pitanjima ili nema jasno izraženo mišljenje. Ovo sugerira da studenti prepoznaju rizike povezane s korištenjem *AI* alata, ali da im je potrebna dodatna podrška kako bi se AI tehnologija mogla primjeniti na siguran i odgovoran način. To im je potrebno omogućiti i zbog njihovog prepoznavanja važnosti *AI* tehnologije za buduću karijeru, kako bi mogli sigurno i učinkovito integrirati ovu tehnologiju u svoje buduće profesionalno okruženje. Međutim, potrebno je istaknuti da na pitanje o ulozi umjetne inteligencije u njihovoj karijeri postoji određena disperzija odgovora, koja vjerojatno proizlazi iz nesigurnosti studenata o tome kako će *AI* oblikovati budućnost koja je pred njima, što se može vidjeti u stavovima izraženim u odgovorima na pitanja otvorenog tipa. Zaključno, rezultati ovog istraživanja naglašavaju potrebu za sustavnom edukacijom koja bi obuhvatila ne samo tehničke aspekte korištenja alata umjetne inteligencije već i njezine etičke dimenzije, s ciljem osnaživanja studenata za odgovorno i svjesno korištenje ove tehnologije. To bi se moglo potaknuti uvođenjem određenih programa *AI* pismenosti i/ili radionica koji bi studentima omogućili dublje razumijevanje tehničkih i etičkih dimenzija umjetne inteligencije, ali i razviti smjernice za sigurno korištenje *AI* alata u odgoju i obrazovanju. ** ** **Zaključak ** Kako se umjetna inteligencija sve intenzivnije integrira u odgojno-obrazovne procese i šire društvene okvire, ključno je razvijati i promicati odgovornu i etičnu uporabu *AI* alata, odnosno razvijati *AI* pismenost. Time se omogućuje ne samo maksimiziranje prednosti koje umjetna inteligencija nudi, već i minimiziranje potencijalnih rizika povezanih s njezinim sve većim utjecajem na obrazovanje, društvo i svakodnevni život. Iako rezultati istraživanja prikazanih u radu pokazuju da je korištenje umjetne inteligencije kod studenata još uvijek u svojim začecima, ovo je ključan trenutak za usmjeravanje njezine primjene ne samo kao alata za povećanje efikasnosti, već i kao sredstva za unapređenje kvalitete života, očuvanje ljudskih vrijednosti i doprinos rješavanju globalnih izazova. U tom kontekstu, ključno je poticati razvoj *AI* pismenosti koja će omogućiti odgovorno i etično korištenje ove tehnologije, osiguravajući njezinu integraciju u društvo na način koji promovira zajedničko dobro. Razvijanjem *AI* pismenosti, mlade generacije bit će bolje pripremljene za suočavanje s izazovima i iskorištavanje mogućnosti koje umjetna inteligencija donosi, ne samo u odgojno-obrazovnom sustavu, već i u budućim profesionalnim i društvenim kontekstima. Zaključno, razvijanjem *AI* pismenosti može se osigurati ravnoteža između čovjeka i tehnologije kako bi umjetna inteligencija ostala alat koji služi ljudskim potrebama i vrijednostima, a ne faktor koji ih narušava. ** ** **Literatura** Ashok, M., Madan, R., Joha, A. & Sivarajah, U. (2022). Ethical framework for artificial intelligence and digital technologies. *International Journal of Information Management*, *62.* [https://doi.org/10.1016/j.ijinfomgt.2021.102433](https://doi.org/10.1016/j.ijinfomgt.2021.102433) Borenstein, J. & Howard, A. (2021). Emerging challenges in AI and the need for AI ethics education. *AI and Ethics, 1*(1), 61-65. [https://doi.org/10.1007/s43681-020-00002-7](https://doi.org/10.1007/s43681-020-00002-7) Chai, C. S., Lin P. Y., Jong, M., Dai, Y., Chiu, T. & Qin, J. (2021). Perceptions of and behavioral intentions towards learning artificial intelligence in primary school students. *Educational Technology & Society, 24*(3), 89-101. Chocarro, R., Cortiñas, M. & Marcos-Matás, G. (2021). Teachers’ attitudes towards chatbots in education: a technology acceptance model approach considering the effect of social language, bot proactiveness, and users’ characteristics. *Educational Studies,* 1-19. [https://doi:10.1080/03055698.2020.1850426](https://doi:10.1080) European Commission, Joint Research Centre (JRC) & Organisation for Economic Co-operation and Development (OECD) (2021). *AI watch, national strategies on artificial intelligence: A European perspective.* [https://doi.org/10.2760/069178](https://doi.org/10.2760/069178) European Parliament (2024). *Artificial Intelligence Act*. Pribavljeno Kolovoz 30, 2024, s [https://www.europarl.europa.eu/doceo/document/TA-9-2024-03-13-TOC\_EN.html](https://www.europarl.europa.eu/doceo/document/TA-9-2024-03-13-TOC_EN.html) Garrett, N., Beard, N. & Fiesler, C. (2020). More than “If time allows”: The role of ethics in AI education. 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Tatnall (Ed.), *Encyclopedia of education and information technologies* (pp. 88-103). Springer International Publishing. [https://doi.org/10.1007/978-3-030-10576-1\_107](https://doi.org/10.1007/978-3-030-10576-1_107) Holmes W., Bialik M. and Fadel C. (2019). *Artificial intelligence in education: promises and implications for teaching and learning*. Center for Curriculum Redesign. Jobin, A., Ienca, M. & Vayena, E. (2019). The global landscape of AI ethics guidelines. *Nature Machine Intelligence, 1*(9), 389-399. [https://doi.org/10.1038/s42256-019-0088-2](https://doi.org/10.1038/s42256-019-0088-2) Kuipers, B. (2020). Perspectives on ethics of AI: Computer science. In M. D. Dubber, F. Pasquale, & S. Das (Eds.), *The Oxford Handbook of Ethics of AI* (pp. 419-441). Oxford University Press. [https://doi.org/10.1093/oxfordhb/9780190067397.013.27](https://doi.org/10.1093/oxfordhb/9780190067397.013.27) Lai, C. L. (2021). Exploring university students’ preferences for AI-assisted learning environment. *Educational Technology & Society, 24*(4), 1-15. Pribavljeno Kolovoz 29, 2024, s [https://www.jstor.org/stable/48629241](https://www.jstor.org/stable/48629241) Long, D. & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In *Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems* (pp. 1-16). [https://doi.org/10.1145/3313831.3376727](https://doi.org/10.1145/3313831.3376727) Miao F., Holmes W., Ronghuai H. & Hui, Z. (2021). *AI and education: guidance for policy-makers*. [https://doi.org/10.54675/PCSP7350](https://doi.org/10.54675/PCSP7350) Organisation for Economic Co-operation and Development (OECD) (2019). *Recommendation of the Council on Artificial Intelligence*. Pribavljeno Kolovoz 30, 2024, s [https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449](https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449) UNESCO (2019). *Beijing consensus on artificial intelligence and education*. Pribavljeno Kolovoz 29, 2024, s [https://unesdoc.unesco.org/ark:/48223/pf0000368303](https://unesdoc.unesco.org/ark:/48223/pf0000368303) UNESCO (2022). K-*12 AI curricula: a mapping of government-endorsed AI curricula*. Pribavljeno Kolovoz 30, 2024, s [https://unesdoc.unesco.org/ark:/48223/pf0000380602](https://unesdoc.unesco.org/ark:/48223/pf0000380602) United Nations (2022). *Principles for the ethical use of artificial intelligence in the United Nations system*. Pribavljeno Kolovoz 30, 2024, s [https://unsceb.org/principles-ethical-use-artificial-intelligence-united-nations-system](https://unsceb.org/principles-ethical-use-artificial-intelligence-united-nations-system) Vadakkemulanjanal, G.J., Athira, P., Thomas, A. M., Jose, D., Roy T. V. & Prasad, M. (2024). Impact of digital literacy, use of ai tools and peer collaboration on AI assisted learning: perceptions of the university students. *Digital Education Review, 45,* 43-49. https://doi.org/10.1344/der.2024.45.43-49 Yeh, H. Y., Tsai, Y. H., Tsai, C. C. & Chang, H. Y. (2019). Investigating students’ conceptions of technology-assisted science learning: A Drawing analysis. *Journal of Science Education and Technology*, *28*(4), 329-340. [https://doi:10.1007/s10956-019-9769-1](https://doi:10.1007) Yi, Y. (2021). Establishing the concept of AI literacy: Focusing on competence and purpose. *Jahr - European Journal of Bioethics*, *12*(2), 353-368. [https://doi.org/10.21860/j.12.2.8](https://doi.org/10.21860/j.12.2.8)
[![logo stoo2_1 (no).png](https://hub.ufzg.hr/uploads/images/gallery/2024-09/scaled-1680-/logo-stoo2-1-no.png)](https://hub.ufzg.hr/uploads/images/gallery/2024-09/logo-stoo2-1-no.png) **Teaching (Today for) Tomorrow:** **Bridging the Gap between the Classroom and Reality** 3rd International Scientific and Art Conference Faculty of Teacher Education, University of Zagreb in cooperation with the Croatian Academy of Sciences and Arts
**Research on AI literacy of students **
##### **Abstract**
This paper presents the results of a study conducted in 2024, aimed at examining students' knowledge, attitudes, and habits regarding the use of artificial intelligence, specifically assessing their AI literacy. AI literacy, recognized as an extension of digital literacy, encompasses the ability to responsibly and effectively use AI tools while understanding the ethical implications of their use. The study was conducted with a sample of 210 undergraduate and graduate students from the University of Zagreb, and data were collected through an online survey questionnaire. The central focus of the research was to assess students' understanding of AI technology, its impact on the learning process, the frequency of AI tool use in learning, and their level of ethical awareness regarding AI technology. The results show that students have not fully integrated AI tools into all aspects of their learning; instead, they use them in a limited scope, recognizing their usefulness for specific tasks such as writing assistance, data analysis, and solving mathematical problems. While students acknowledge the potential benefits that AI tools can offer, their awareness of the ethical use of these tools remains relatively low, highlighting the need for the development of educational programs focused on the responsible and ethical application of AI technology.
***Key words:***
AI tools; artificial intelligence; contemporary education; educational programs; ethics
# Threshold concepts in Computer Science teaching
[![logo stoo2_1 (no).png](https://hub.ufzg.hr/uploads/images/gallery/2024-09/scaled-1680-/logo-stoo2-1-no.png)](https://hub.ufzg.hr/uploads/images/gallery/2024-09/logo-stoo2-1-no.png) **Teaching (Today for) Tomorrow:** **Bridging the Gap between the Classroom and Reality** 3rd International Scientific and Art Conference Faculty of Teacher Education, University of Zagreb in cooperation with the Croatian Academy of Sciences and Arts
##### **Gabrijela Jakovac, Martina Holenko Dlab ** *University of Rijeka, Faculty of Informatics and Digital Technology* *gabrijela.jakovac@student.uniri.hr*
**Section - Education for digital transformation****Paper number: 46****Category: Original scientific paper**
##### **Abstract**
Fundamental concepts underlie every scientific field. Among them, there are concepts that represent a turning point in the understanding of the field and whose understanding is a significant challenge for students. Such concepts are called threshold concepts. The aim of this paper is to provide an overview of the characteristics of threshold concepts that distinguish them from fundamental concepts, to identify threshold concepts in the field of computer science, and to emphasize the need for selecting appropriate teaching strategies and approaches for teaching threshold concepts using digital technology. In addition to the list of threshold concepts in computer science derived from the literature review, a list of threshold concepts derived from research with computer science teachers is presented. The nominal group technique, which provides a structured approach to idea exchange within the group, was used to identify threshold concepts. Participants (N=53) first proposed the threshold concepts individually by writing explanations and then presented them to the group. The group discussed and voted to reach a consensus. In identifying threshold concepts, the focus was on recognizing transformative and integrative features to identify concepts whose understanding triggers a significant shift in the understanding of the subject area and makes connections that were previously hidden. Identifying threshold concepts can help guide learning and teaching. With a better understanding of the difficulties students face, teachers can provide personalized support to help students master these concepts using technology. Further research will focus on analyzing the possibilities of applying approaches for teaching threshold concepts, especially game-based learning approaches.
***Key words:***
computer science, personalization, STEM, teaching, threshold concept.
**Introduction** In today's digital society, in which we have grown up and are active members, it is clear that the integration of information technology within the educational system is becoming increasingly significant (Bognar, 2016). In the context of computer science, teachers face the dual challenge of teaching foundational knowledge and enabling students to overcome key barriers to understanding. These obstacles include threshold concepts— core ideas that represent a transformative points in learning. These concepts are not only fundamental, but also serve as a gateway to deeper understanding that often requires a significant shift in perspective to master. Meyer and Land (2003) have identified the threshold concept in the field of education as a set of ideas that, once understood, become transformative but are initially challenging and unfamiliar. Regardless of whether we adopt a constructivist approach or another learning theory, threshold concepts represent points at which students are likely to encounter learning difficulties. To further define threshold concepts, Meyer and Land state that they are integrative, as they show previously unknown ways of linking ideas; irreversible, as the new way of thinking becomes part of the learner once they have truly understood it; and boundary markers, as they define the boundaries of part (or all) of a set of ideas. An entire subject area may have its boundary marked by a single threshold concept, mastery of which indicates competence in that area (Meyer & Land, 2003). Dr. Tucker highlights several characteristics of threshold concepts and emphasizes five main characteristics: transformative, irreversible, integrative, troublesome, and bounded (SJSU School of Information, 2013). Research on threshold concepts in computer science has highlighted certain concepts as transformative and challenging for students as they often require significant cognitive change. Among these concepts, object-oriented programming stands out due to its complexity and potential to enable deeper understanding and application in different areas of computer science (Boustedt et al., 2007). However, the concepts taught in primary school have not been the focus of such research, leaving a gap in the understanding of threshold concepts that younger students should overcome. This paper examines the threshold concepts in computer science education, with a focus on primary school. The research aims to identify concepts that are particularly challenging for students and to distinguish those that can be considered threshold concepts based on their characteristics. Using the nominal group technique, primary school teachers were involved in a structured research process and threshold concepts were proposed on the basis of shared insights. By addressing this topic, this research aims to improve teaching practices and support learning and teaching, as by understanding these concepts, teachers can better address the obstacles students face and guide them towards understanding the key concepts. * * **Related work** **Characteristics of threshold concepts in education ** In this section, the following characteristics of threshold concepts are described according to Meyer & Land (2003): *transformativeness, irreversibility, integrativeness, troublesomness, and boundedness*. *Transformative *is described and associated with events that leave a lasting impression and are unforgettable, such as passing a driving test. The transformation of attitudes, values or understanding often represents a decisive point in our lives. This change not only shapes our identity, but also has a profound impact on our daily lives. Through this development, the new understanding gradually integrates into our biography and becomes an inseparable part of who we are. This process does not happen immediately, but unfolds gradually and permeates all aspects of our lives. It is not just a matter of adopting a new attitude or a new value. Rather, this transformation becomes part of our inner being and shapes the way we perceive the world around us. Through this integration, the new understanding becomes a fundamental element of our identity and influences our thoughts, feelings and actions. This change does not occur in isolation, but has a profound impact on our relationships, our work and our life choices. The importance of such transformations lies in their ability to promote growth and development as individuals. They encourage us to look at things from different angles, which gives us a broader view of life. Furthermore, these changes often coincide with personal growth and make us stronger and more resilient to life's challenges. *Irreversibility*, in the context of knowledge, stands for a profound level of learning, where what we have mastered becomes an integral part of our intellectual repertoire. It is like riding a bike or swimming, where a learnt skill becomes inherent and indelible. Through the process of irreversibility, knowledge becomes imprinted in our memory in a way that resists forgetting, even in challenging situations. This phenomenon can be compared to riding a bike. As soon as we master a technique, it becomes part of our muscle memory. No matter how long we have not ridden a bike, when we do it again, the process naturally emerges from our subconscious. Similarly, irreversibility in learning means that once acquired, knowledge becomes a permanent skill that is activated regardless of a prolonged period of disuse. *Integrative learning *means that what was previously hidden or not fully understood is made accessible in its context. This quality of learning has the power to link separate concepts together so that they are brought together into a holistic understanding. Ideas that were once separate are now connected, creating a broader understanding that enriches individual perspectives. This integration process can be likened to putting together pieces of a jigsaw puzzle. Each individual piece represents a particular concept, and through integration, these individual pieces become an integral part of a larger, complete landing. Furthermore, integrativeness is not just about putting different concepts together, it goes a step further by creating an expanded understanding that enriches our perception of the world around us. This dimension of integrativeness significantly influences the development of individual understanding. Ideas that have previously isolated now become part of a wider network of connections, leading to a richer and deeper experience of knowledge. *Troublesomeness*, in learning can be associated and described with certain concepts that may seem counter-intuitive or unpleasant. However, it is essential to face these challenges in order to understand them. Often these concepts are associated with situations that cause discomfort or are counter-intuitive, and this discomfort may stem from misconceptions. Especially when solving problems in physics, beginners are often confronted with various misconceptions and contradictions. However, when they dedicate themselves to solving these challenges, they not only overcome their preconceptions but also reach a new level of understanding. Wrestling with counterintuitive ideas becomes a path to deeper understanding, and this process allows individuals to reach new heights in their learning eliminating underestimation in the process. *Boundedness* implies the presence of definitive boundaries. These boundaries serve as transitions between different conceptual areas, defining boundaries to other thresholds and introducing us to new areas of understanding. In a particular subject area, specialized terminology takes on a new meaning defined precisely by these boundaries. Boundedness implies not only the presence of endpoints, but also the possibility of exploring and expanding these boundaries to deepen our understanding and discover new meanings that emerge within these defined boundaries (Mayer and Land, 2003), (SJSU School of Information, 2013). ** ** **Threshold concepts in computer science ** The research on threshold concepts in computer science conducted by Boustedt et al. (2007) focused on identifying terms that could correspond to threshold concepts and validating them with students, followed by checking whether the criteria for threshold concepts are met. At the Conference on Innovations in Computer Science Education in June 2005, 33 computer science experts from nine countries were surveyed to select terms that met the criteria for threshold concepts. In November 2005, a similar study was conducted at a conference on computer science in Finland, the results of which focused on the „hard to learn” aspects of threshold concepts (McCartney and Sanders, 2005). Subsequent studies at different universities in several countries showed that students identified „control structures”, „sequential thinking”, „parameters”, „objects” and „memory models” as threshold concepts. Object-oriented technologies and pointers were selected for in-depth analysis as they fully met all the criteria for threshold concepts (Boustedt et al., 2007). *Object-oriented programming* (OOP) is based on the idea that a program consists of objects that represent interconnected parts of a solution, in contrast to the classic procedural model, which views a program as a sequence of instructions. OOP enables more efficient code organization and simplifies the maintenance and scaling of large programs. It is used in languages such as Java and Python, while the procedural model is suitable for languages such as C and Pascal (Jovanović, 2012). Although OOP offers numerous advantages, students often report difficulties in learning it, especially with basic concepts such as classes and objects. Research among first-year university students shows that many have experienced OOP as a challenge that requires rethinking. Nevertheless, most students emphasize that mastering OOP has helped them to understand more complex programming concepts and enabled them to transfer the skills learned to other areas, such as software engineering, demonstrating the transformative nature of OOP. Such experiences show that although the learning process is long and complex, it provides long-term benefits and enables students to apply their knowledge in different contexts (Boustedt et al., 2007). *Pointers* have been identified as a threshold concept in computer science because their understanding is often challenging for students, especially when they are used as parameters in programs. Students reported difficulty connecting abstract theory to the practical application of pointers. One student described having difficulty understanding pointers until he realized that they simply represent a specific memory location, which made the concept clearer. Having mastered pointers, students began to apply this knowledge in broader contexts such as hardware and operating systems by using pointers in practical work, for example in assembly language. Understanding pointers enabled them to apply objects and references more successfully in programming, leading to greater confidence in tackling more complex computing problems (Boustedt et al., 2007). Recent research by Kallia and Sentance (2020) provides additional insights in the context of functions. Their study emphasises the transformative and integrative nature of concepts such as „parameters”, „parameter passing”, „return values”. These concepts were found to be challenging for secondary school students and their mastery facilitates deeper understanding and integration of knowledge across programming topics. The authors identified „procedural decomposition” as a potential threshold skill requiring extensive practice to bridge theoretical understanding and application. The study conducted by McSkimming, Mackay & Decker (2023) identified two threshold concepts for intermediate computer science students: „algorithmic runtime” and „memory management”, emphasizing the challenges and transformative understanding associated with these concepts. The research by Govender & Olugbara (2022) reflected on threshold concepts for developing programming skills in first-year information technology students, providing insights into supporting teaching strategies and identified procedures and functions and programming constructs (such as selection, iteration, and variable manipulation) as critical threshold concepts for teaching computer programming to first-year IT students. These studies highlight the ongoing interest in understanding and addressing threshold concepts to enhance computer science education. **Methodology ** The main aim of this paper is to propose threshold concepts for computer science taught in primary school. The basic methodology of the research was the nominal group technique (NGT), which is suitable for gaining a deeper insight into the participants' perception and understanding of threshold concepts. The nominal group technique (NGT) was found to be a very effective method for promoting critical thinking through discussions that involve a small number of participants, provide clear and focused instructions and allow for constructive feedback. NGT fulfils all these criteria while ensuring the full participation of all group members, which is especially useful in an educational context. NGT is widely used in various disciplines, e.g. medicine, information technology, politics, management and education, where it serves as a method for evaluated discussions (Macphail, 2001). In education, NGT is used in the design and evaluation of curricula and as a pedagogical method that encourages active participation (Chapple & Murphy, 1996). Studies have shown that NGT increases participant’s productivity and problem-solving skills through structured discussions (Madar, 1982). **Procedure ** The application process of NGT consisted of six steps, beginning with an oral *presentation* that covered the definitions and examples provided. A presentation was prepared, which included precise definitions of threshold concepts, along with examples of threshold concepts with explanations according to their characteristics. For demonstrating threshold concepts, „looping” and subprograms were selected as examples that fully align with the threshold concept. Simultaneously, as an example of a demanding concept, recursion was analyzed and identified as a concept that requires additional understanding but is not a necessary prerequisite for further learning in programming. The second step, *silent idea generation,* required participants to individually reflect on and write down their thoughts regarding threshold concepts. They completed a questionnaire that required them to try to identify concepts and answer whether these concepts are fundamental, demanding, and whether they meet the characteristics of threshold concepts. The third step, *idea discussion*, took place after individual reflections. Participants were divided into smaller groups with 4 to 7 participants to further deepen their understanding and discuss the proposed threshold concepts. Through constructive discussion, participants contributed to a deeper understanding of the concepts and their application in computer science education. In this group decision-making phase, each group selected one or more concepts that they believed met all the characteristics of threshold concepts. To obtain clear results and rank the proposed threshold concepts, *voting* was conducted via the online platform Padlet. After the voting and reviewing of the results, *conclusions* were drawn, and potential threshold concepts were identified. Lastly, the *report* with a summary of the procedure, decisions, and final results was written. **Participants** The participants of the research were primary school teachers of computer science (N=53) who participated in the activities organized by the Professional Council of Computer Science Teachers of Primorje-Gorski Kotar County at Vežica Primary School in Rijeka in February 2024. In addition to the questions on threshold concepts, participants were asked to provide demographic data, including their educational profile, professional experience and the subjects they teach. Table 1 shows the age and gender of the participants. The participants in the survey have a wide age range, from 24 to 63 years, with an average age of 39 years. This age diversity suggests that participants are at different stages in their careers, which could influence their responses given their varied teaching experience. Table 1 Age of the participants
Age Number of participants F M Others
24-34 21 15 6 0
35-44 10 7 3 0
45-54 19 15 3 1
55+ 3 2 1 0
The majority of participants were women, a smaller number were men (Chart 1). One respondent did not specify their gender. Chart 1 Distribution of participants by gender ![](https://hub.ufzg.hr/uploads/images/gallery/2025-05/embedded-image-6kd88ubx.png) All participants have a high level of education and work as computer science teachers at primary schools (Table 2). The average length of time spent in the teaching profession is around 10 years. A total of 33 participants have between 0 and 9 years of professional experience, 7 participants have between 10 and 19 years of professional experience, 11 participants have between 20 and 29 years of professional experience, and 2 participants have more than 30 years of professional experience. Table 2 Participants‘ work experience
Work experience Number of participants F M Others
0-9 33 23 10 0
10-19 7 6 1 0
20-29 11 9 1 1
30+ 2 1 1 0
Participants vary in the combination of subjects they teach, including computer science, mathematics, English, physics and technical education what can be seen in Table 3. Table 3 Number of participants by job position
Job Position Number of Participants
Teacher of Computer Science 39
Teacher of Computer Science and Mathematics 11
Teacher of Computer Science and Technical Culture 2
Teacher of Computer Science and English Language 1
** ** **Results ** The results collected through the survey provide insight into opinions of computer science teachers regarding the threshold concepts. Within the stage of *silent idea generation,* the participants highlighted several key fundamental concepts in computer science education, including „Branching”, „Functions”, „HTML”, „File Storage”, „Addressing” and „Logical Conditions”, which were recognized as essential for understanding computer science. „Branching Algorithm” was marked as a fundamental concept by 15 participants, while „Functions” and „Logical Conditions” received 13 votes. „HTML” and „File Storage” were also identified as fundamental by 11 participants, and „Addressing” was also ranked highly. Challenging concepts that represent learning obstacles include „Branching”, „Database Relations” and „File Storage”, which require additional attention and resources for adequate understanding. The interconnection of concepts was also important, and the participants recognized that „Branching”, „Functions”, „Logical Conditions”, „HTML” and „File Storage” are connected, meaning that mastering these concepts facilitates understanding of other aspects of computer science. The transformative nature of concepts, which indicates their potential to lead to deeper understanding, was also highlighted for „Branching”, „Functions”, „HTML” and „Logical Conditions”, while the irreversibility of these concepts was considered high, meaning that students rarely forget these terms once they have mastered them. Concepts such as „Database Relations” and „Branching“ were marked as bounded, meaning that their understanding may vary among students, requiring continuous repetition. Some concepts, such as „Attributes”, „Database”, „File”, „Elements in Canva”, „Internet”, „Output Devices”, „Copying” and „References in Word”, were not recognized as fundamental or challenging by most participants, indicating their lower importance in basic computer science education. During the *idea discussion* phase, the participants concluded that some concepts, such as „Branching” and „Logical Conditions”, could be combined because they are interconnected, and that „Data Organization” and „File Storage” could also be merged, as data organization is key to understanding the process of file storage. After the *discussion* phase, we move on to the *voting* phase. In order to obtain clear results and ranking of the proposed threshold concepts, a voting was conducted through the online platform Padlet. In the following table, the proposed threshold concepts and the voting results for each proposed concept can be seen. Because some teachers were undecided on certain concepts, the total number of votes differs per concept and is less than 53. Table 4 Proposed threshold concepts
PROPOSED CONCEPTS N YES NO
*Logical conditions* *43* *42* *1*
*Cell addressing (in spreadsheet)* *42* *40* *2*
*Variables* *43* *39* *4*
*Data organization (storage)* *45* *39* *6*
Flowchart 41 35 6
Relations (databases) 42 31 11
HTML and similar languages 39 29 10
User account 42 24 18
Personal data protection 42 15 27
By analyzing the voting results, the list of threshold concepts was proposed based on input from participants, highlighting those that clearly received higher support as threshold concepts in computer science education. „Logical conditions” received an exceptionally high number of votes, with 42 votes „YES” and only one vote „NO”. „Cell addressing (in spreadsheet)” also received a large number of votes, with 40 votes „YES” and only two votes „NO”. „Flowchart” received 35 votes „YES” and only 6 votes „NO.” „Data organization(storage)” received 39 votes „YES” and 6 votes „NO”. „Variables”, although not receiving unanimous support, can be considered a threshold concept given the predominant support. **Discussion ** The results of the survey provide several important insights into primary school IT teachers’ views on threshold concepts. Concepts such as „logical conditions”, „cell addressing”, „variables”, and „data organization” received strong support, indicating their importance for computer science teaching. The high consensus on „logical conditions” (42 out of 43 participants who voted for this concept) may reflect its fundamental role in programming, particularly in helping students understand decision-making in algorithms. This strong support suggests that teachers view it as a prerequisite for grasping more advanced programming concepts. In contrast, the slightly lower support for the concept of „flowchart” (35 votes) may indicate variability in how teachers integrate visual algorithm representation into their teaching practices. While flowcharts are valuable for structuring and planning code, some teachers may rely on alternative methods, such as pseudocode, to introduce these skills. The strong support for „cell addressing” (40 votes) highlights its relevance not only for spreadsheet data management but also for broader applications like data analysis. This result underscores the increasing emphasis on data literacy in primary education, aligning with the growing importance of data science skills. However, it also raises questions about whether teachers feel adequately equipped to teach these skills effectively. These findings suggest that while core programming concepts such as „logical conditions” and „variables” are well-established in the curriculum, there is room to explore how concepts like „data organization” and „flowcharts” can be better integrated. Future research might investigate how these concepts are taught in practice and the challenges teachers face in developing student’s understanding of them. **Conclusions** The topic of threshold concepts represents not only a challenge, but also an opportunity for innovation in computer science teaching. Clearly defined threshold concepts provide a foundation for high-quality education and promote the development of the skills required in the digital age. Methodologies like the nominal group technique allow structured dialogue among teachers, creating a space for exchanging experiences and finding common solutions to teaching challenges. Based on the research conducted using the nominal group technique with primary school teachers the following threshold concepts in computer science education have been proposed: logical conditions, cell addressing (in spreadsheet), flowcharts, data organization, and variables. These concepts have been highlighted as transformative points that enable deeper understanding of the subject matter and progress for students. In the future work, these proposals will be validated through further research involving subject-matter experts, who will contribute to developing a solid rationale confirming that these concepts have the defining characteristics of threshold concepts. The integration of educational games has considerable potential to support students in mastering complex topics while increasing their engagement and motivation in the learning process. Following this validation, game-based learning activities will therefore be designed to facilitate student’s acquisition of these concepts. **Acknowledgment** The research has been funded by the Erasmus+ Programme of the European Union, KA220-SCH - Cooperation partnerships in school education, under the project “Science&Math educational games from preschool to university” (023- 1-HR01-KA220-SCH-000165485). **Literature** ** ** Bognar, B. (2016). Teorijska polazišta e-učenja. 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The Expert Searcher and Threshold Concepts. Accessed December 10, 2023, Available online: https://youtu.be/4I1Ue0vpcMw. Vinner, S., & Dreyfus, T. (1989). Images and definitions for the concept of function. Journal for research in mathematics education, 20(4), 356-366. # The opinions and attitudes of prospective primary school teachers on the use AI applications in education
[![logo stoo2_1 (no).png](https://hub.ufzg.hr/uploads/images/gallery/2024-09/scaled-1680-/logo-stoo2-1-no.png)](https://hub.ufzg.hr/uploads/images/gallery/2024-09/logo-stoo2-1-no.png) **Teaching (Today for) Tomorrow:** **Bridging the Gap between the Classroom and Reality** 3rd International Scientific and Art Conference Faculty of Teacher Education, University of Zagreb in cooperation with the Croatian Academy of Sciences and Arts
##### **Maja Homen** *Faculty of Teacher Education, University of Zagreb* *maja.homen@ufzg.hr*
**Section - Education for digital transformation****Paper number: 47****Category: Original scientific paper**
##### **Abstract**
The growing presence of Artificial Intelligence (AI) in education has drawn attention to its potential to enhance teaching and learning processes. The study examines prospective primary school teachers' perceptions and attitudes toward the adoption of AI applications in education, concerning their readiness, concerns, and expectations. The research was conducted through an online questionnaire distributed among students at the Faculty of Teacher Education, University of Zagreb. The findings provide insights into students' familiarity with AI technologies, perceived benefits, and challenges associated with their use in primary education. The study reveals that while students recognize that AI could be used to customize the learning experience and relieve teachers of some administrative tasks, they also seem to care more about ethical issues, concerning data privacy, etc. Also, critical thinking skills can be placed in danger because of AI. These points will be contributing to the future ongoing discussions in taking the responsibility in which AI is used in education and showing that teacher training on the opportunities and risks of adoption needs further development. Results will have importance not only for policymakers but also for educational institutions in establishing quite meaningful AI education policies in primary schools as per pedagogical plans but also ethical considerations.
***Key words:***
AI applications, Artificial Intelligence, education, ICT, prospective teachers, teacher perceptions
**Introduction ** Artificial Intelligence (AI) has recently experienced unprecedented growth in many industries, and education is among the most promising fields for its implementation. Although the potential of AI has been recognized for quite a long time, it is only in the last couple of years that research has shifted more and more towards the effects of AI in the educational field. This increase in interest has occurred alongside the developments in AI technologies like machine learning and natural language processing, which revolutionize education (UNESCO, 2023; World Economic Forum, 2023). In education, the capability of AI to improve teaching and learning cannot be overemphasized since service delivery has been personalized and made more accessible. In this context, AI refers to systems that can acquire and process large amounts of information, identify learning patterns, and provide individual learning processes. Learners can also progress through their learning curves and at their learning rate, effectively producing results (Alneyadi et al., 2023; HolonIQ, 2023). In addition, ITS, chatbot, and auto-grading solutions can decrease the teachers’ workload and make them focus more on the invention and improvement of the creation of meaningful interactions with students and on the development of thinking skills (World Economic Forum, 2023). AI technologies can be used in learning as recommendations in potential development areas and optimization of tasks based on a student’s capability. This leads to a situation where even the poor performers and the bright students receive the assistance they require to be productive. Personalized learning platforms have been shown to engage students more effectively, boosting motivation and improving academic outcomes and retention rates (Gningue et al., 2022; Ibrahim et al., 2022). AI can notice that a student requires assistance by analyzing the student’s performance profile. Instead of providing general assistance, it assists where the student is most likely to require help (Alarabi & Wardat, 2021). AI systems can analyze student performance data to identify areas that may require special attention. These systems can help detect struggling learners early and provide support before issues get out of hand. For instance, using big data can track the student’s activity, attendance, and performance to identify those at risk and suggest the appropriate intervention or change in the learning approach (Niall McNulty, 2023). Moreover, other AI systems, such as Mindspark, assess the students’ responses and recommend the right exercises and materials to fill the students’ knowledge gaps and provide each learner with the right level of challenge and support (Calibraint, 2023). AI systems in education can also control the speed of learning, thus allowing fast learners to progress faster while slow learners can progress at their own pace. This learning approach makes it possible for the learner to learn at his or her own pace, depending on his or her ability, making the learning process very effective. For instance, a study reveals that AI can use the student’s performance data to offer feedback and recommend learning resources suitable for the student (Benzakour et al., 2022). They also assist educators by pointing out students who may require extra attention and assistance. In this case, AI makes it easier to provide targeted support, guaranteeing that every learner gets the help required to succeed (Kurilovas, 2018). In addition, adaptive learning platforms have been integrated into different educational contexts. They have helped students learn at their own pace and enhanced both the interest and the drop-out rates (Chiu et al., 2023). Harry & Sayudin (2023) highlight that there is a potential for artificial intelligence to change the educational scenario; however, there are specific barriers that require attention for this to happen. It is beneficial as it can personalize lessons for students, reduce administrative tasks for staff, and provide real-time learning updates during class. Still, they have to calculate the benefits and risks, protect information gleaned from students, and control prejudices from AI systems to make integrative authorities feasible. When this comes together with the limitations, AI could make you more efficient and deliver educational help to all-inclusive students. Integrating AI in education is rewarding with several benefits but presents core concerns that have to be strategically resolved. The challenges of data privacy, bias in algorithms, and the feasibility of rolling out AI systems have been factors of great concern. Training affected the AI systems, proving adverse as they replicated the biases in the training data and created gaps in areas such as provision for healthcare services and allocation of resources, including special education for various groups of students (Sutaria 2022). With this, privacy concerns come into play when AI categories together and utilize students' information without consent, raising concerns about surveillance and data usage governance in educational settings (GUJJULA et al., 2023; McNulty, 2023). Other important factors are ethical considerations such as fairness and accessibility of the content. AI should be developed and deployed in a way that does not reinforce prejudices and provides equal opportunities for all learners. This includes explaining how AI algorithms work and use data (UNESCO, 2023). Constant supervision and assessment are needed to ensure the process is fair and does not perpetuate societal educational inequalities. For example, Baker & Hawn (2022) note that caution is needed when implementing AI in education since it can lead to algorithmic bias that is unfavorable to some students. They recommend the use of diverse data in order to reduce bias in the AI models. Likewise, Chaudhuri & Mohanty (2023) also talk about the need for bias-aware algorithms to capture and address biases that might be inherent in historical data patterns. In this way, institutions can ensure that AI systems are trained on a diverse set of data to reflect the diverse student body. Malik (2024) highlighted the importance of continuously using diverse and representative data and monitoring the AI systems’ performance to create efficient and ethical AI systems. Furthermore, the UK’s Information Commissioner’s Office (ICO) also notes that fairness in AI is not only a technical concept that refers to algorithmic fairness. It needs a more extensive and context-based approach that takes into account the social and legal consequences of data processing, so that AI does not enhance the existing unfair distribution of resources (ICO, 2023). By implementing these principles, AI systems can be more explainable, non-discriminatory, and inclusive to all students with equal opportunities to access educational materials. There is a great deal of interest and controversy regarding the use of AI in education, as the presence of AI in the educational process increases. Since AI technologies like intelligent learning platforms, automated grading tools, and intelligent tutoring systems are being incorporated into classes, teachers' perceptions of such technologies give insight into the future adoption of AI technologies in education. These perspectives influence the integration and application of AI tools and raise the question of the role of technology in learning contexts. The aim of this paper is to identify the perception of students from the Faculty of Teacher Education at the University of Zagreb towards the application of Artificial Intelligence (AI) in learning. The research aims to identify the positive and negative attitudes students have towards AI, which subject they think should be supported by AI, and which AI applications are most beneficial for teachers and students. The study, conducted through an online questionnaire, aims to identify prospective primary school teachers’ attitudes toward the benefits and possible drawbacks of AI in the context of teaching practices, as well as their preferences regarding the use of AI tools and technologies in the process of learning and teaching. This study provides a systematic discussion of the possibilities and concerns of AI in the learning environment, and its purpose is to inform the future directions for teacher preparation and policies in integrating AI into primary education responsibly. *Teachers' attitudes toward the use of AI in education* AI's integration into education has brought together a range of feelings and opinions from educators, which span from optimism to caution. Teachers broadly believe in the benefits of AI, primarily in augmenting personalized learning while, at the same time, lightening administrative work. For example, AI can provide services that relieve teachers of the burden by automatically providing feedback and monitoring students' progress and automating tasks such as grading, thereby giving the teacher ample time to teach and offer more personalized services. This, according to Kim (2023) and Wang et al. (2023), can go a long way in minimizing teacher burdens. Moreover, a lot of educators believe that AI has the potential to create customized learning experiences for students, allow for differences in learning speed, and even promote emotional well-being through the use of AI-powered chatbots (Kim, 2023; Pejnović, 2024). In addition, AI enhances the possibility of real-time monitoring and analysis of student performance, thus enabling the teacher to intervene on time when some students struggle (Kostrić, 2021). For example, intelligent tutoring systems make individual feedback available through student performance analysis and help students in their weak areas without waiting for conventional testing periods. Most educators believe that with differentiated instruction, AI will significantly help close achievement gaps and ensure that all students reach their full potential. Some subjects, like mathematics, science, and language learning, were found more effective with ITSs, which give domain-specific feedback in detail (Kurni et al., 2023; AIWS, 2023). As AI continues to evolve, the integration of natural language processing and emotional recognition in ITSs promises to further enhance personalized learning by responding not only to cognitive but also affective states of learners (Fernández-Herrero, 2023). However, there are quite a few fears among teachers concerning the potential problems of artificial intelligence. One of the most severe concerns are the data privacy issue and the ethical implications of using AI in schools. Many teachers fear that the extensive data collection for AI-enabled personalization can be problematic for student privacy, especially if sensitive information is not handled properly (Felix, 2020; Beović, 2023). Furthermore, concerns have been raised regarding the potential loss of teacher autonomy and the humanizing of educational processes due to artificial intelligence. According to educators, AI should not replace the vital human aspects of teaching, such as emotional support and the development of critical thinking skills, which are crucial for student development (Beović, 2023; Guilherme, 2017; Wang et al., 2023). Teachers seem indifferent regarding their preparedness for the implementation of AI in schools. While some teachers believe that they can use AI with ease, others observe that this cannot be achieved without deeper training that will equip them with the relevant skills in applying AI (Kim 2023). Conversely, other studies have established that preparation to use AI differs depending on prior use and resource experience (Wang et al. 2023). Teachers advocate for specialized training initiatives that encompass both the technological components of artificial intelligence and the associated ethical implications, thereby guaranteeing their preparedness to utilize AI responsibly within the educational setting (Felix, 2020; Alharbi, 2024). One major factor affecting teachers' attitudes towards AI is their readiness to integrate AI into their teaching practices. However, surveys and studies show that most teachers feel unprepared and ill-equipped for the integration of AI tools—often due to a lack of training or resources required for use in classrooms (Beović, 2023; Wang et al., 2023). Teachers who feel unconfident about using AI systems might not use these resources as intensely, widening the gap between those who benefit from AI-supported learning and those who do not (Kim, 2023). Professional development, therefore, becomes necessary to empower the teachers with knowledge of AI technologies and imbue them with the confidence to apply these tools in their pedagogical approaches. Educators are asking for more training that would highlight ethical AI use, data privacy, and the potential effect AI can have on the relationships within the classroom (Kostrić, 2021; Felix, 2020). These initiatives would assist educators in managing the intricacies of artificial intelligence, ensuring that its deployment is consistent with educational objectives and principles (Wang et al., 2023). Professional development of teachers in an effective way to overcome this gap in AI-supported learning needs to be geared toward both the technical knowledge of AI and deeper understandings of ethical issues thrown up by AI-data privacy concerns, with its potential consequences for classroom relationships. This would imply that such professional development should align with emphases within the DigCompEdu framework on empowering teachers across multiple dimensions:. In developing their own competencies with digital resource development, pedagogical methodologies, and evaluative techniques, educators might become more confident in exercising responsible use of AI tools. Furthermore, such professional development would allow instructors to better nurture students' digital literacy in ways that help them grapple with the ethical and practical problems linked to AI. This comprehensive strategy guarantees the incorporation of artificial intelligence in a manner that improves educational results while preserving the human-centric elements inherent in pedagogy (Kostrić, 2021; Kim, 2023; Wang et al., 2023). This balanced approach is based on the understanding that AI is not a substitution for teachers but rather their augmentation that helps with grading and lesson planning so that the teacher can spend more time interacting with the students (Alharbi, 2024; AIWS, 2023). ** ** *Applications of AI in education* Ng et al. (2023) performed a systematic review of 49 studies over a period of two decades, with the result being a much-needed insight into the growth of AI education. The review highlights three key research questions: types of learners involved in AI education, tools used, and teaching approaches applied. In the early days, AI education focused on computer science education at the higher education sector level. By 2021, however, AI literacy had gained momentum in K-12 due to the development and emergence of age-appropriate teaching tools. The pedagogical strategies identified in the article are dominated by collaborative project-based learning and the use of game elements. Such methodologies encourage problem-solving, creativity, and engagement in students. Teaching tools leveraged for scaffolding AI concepts in students ranged from robotics to software platforms and intelligent agents. Nevertheless, challenges were noted, including the lack of suitable resources for young learners and non-computer science majors and the complexity of some AI concepts. The review highlights the growing importance of AI literacy and shows the need for educators to adapt their teaching practices to integrate interdisciplinary and interactive tools that would make AI concepts understandable to all students. *Tools designed to support teachers* Intelligent Tutoring Systems are among the most prominent AI tools used in education. These systems simulate one-on-one human tutoring by providing personalized learning experiences to students based on their individual needs. They use algorithms to track student progress, analyze areas of difficulty, and provide tailored feedback. For instance, platforms like ASSISTments in the United States help students, particularly in mathematics, by offering tailored instruction based on their performance in practice exercises (Holmes & Tuomi, 2022). ITSs have shown particular efficacy in subjects like mathematics, science, and language learning. In mathematics, ITS platforms can guide students through problem-solving processes by offering hints, identifying misconceptions, and providing real-time feedback (Luckin et al., 2016). For example, in physics and chemistry, AI-driven platforms like Knewton and ALEKS adapt content delivery based on students’ responses, helping them grasp complex concepts through gradual mastery of smaller components (Su & Yang, 2023). In language learning, AI-powered tutors like Duolingo offer personalized exercises based on a student's proficiency level, accelerating language acquisition through repetitive and targeted practice. Systems like Carnegie Learning utilize AI algorithms to evaluate a student's comprehension and modify lesson plans, ensuring that each concept is mastered before moving on to more advanced topics (Cope et al., 2020). Such systems are particularly effective in subjects like mathematics and science, where understanding builds on previous knowledge. A significant benefit of adaptive learning platforms is their capacity to customize the learning experience for students with different skill levels. For example, students who find fractions or algebra difficult may receive extra practice exercises, while more advanced students face more challenging problems (Perrotta & Selwyn, 2019). This method guarantees that all students, no matter their learning speed, can achieve a solid understanding of the subject. Furthermore, the use of AI platforms enables students to visualize the results of experiments that are hard to conduct in real life, such as ecosystems and chemical reactions. This also helps students to grasp concepts that are abstract in nature and also enables learning through experimentation (Holmes & Tuomi, 2022). Another learning application of AI is in the area of tests and evaluations , where students can be given results and feedback immediately. Software such as Gradescope and AutoGradr employ machine learning to self-grade students’ assignments and tests, thus freeing teachers’ time while giving students timely and uniform feedback. Assignments in subjects such as computer science and mathematics entail code or problem-solving, and AI tools can check the correctness and effectiveness of the student’s responses (Cope et al., 2020). Feedback systems that are facilitated by AI also facilitate self learning since students can go through their mistakes and enhance their comprehension before the final submission of their work. In writing and composition courses, technologies such as Grammarly give immediate feedback on grammar, syntax, and style, which can be used to improve the students’ writing skills through repeated practice (Su & Yang, 2023). ClassDojo is an example of an AI-based application that helps teachers to maintain discipline and control the level of students’ participation. ClassDojo enables teachers to monitor student behavior in the classroom in real time, communicate with parents, and differentiate lessons according to students’ needs. By so doing, this tool helps instill a more organized and responsive classroom management system and thus, allows the teachers to spend more time teaching rather than managing the class (Carnegie Learning, 2024). Otter.ai and ModMath are great examples of AI technologies that are rather helpful for students with disabilities. Otter. ai allows students with hearing impairments to follow lectures and discussions by providing real-time speech transcription. ModMath, on the other hand, is intended for students with dyslexia or motor skill challenges, enabling them to solve mathematical problems on a computer without writing by hand. These tools enhance more participation and access to classroom content and materials so that all students can participate irrespective of their physical or learning disabilities (Khan Academy, 2023). *Tools designed to support students* In addition to being helpful for teachers, AI solutions offer several tools that are very helpful for students, including individual learning, improving cooperation, and creativity. One of the most engaging ways AI supports student learning is through educational games, such as Minecraft: Education Edition and DragonBox, two of the best applications for kids and parents. In Minecraft, AI builds dynamic educational experiences and makes programming, mathematics, science, and history come alive for students. This way, the game is designed to fit the student’s answers, and thus, every learner is engaged at his or her level of understanding, promoting deep learning while exploring and solving problems (Alawajee & Delafield-Butt, 2021). Minecraft in education benefits learning and social engagement. *International Journal of Game-Based Learning (IJGBL)*, *11* (4), 19-56.). Likewise, DragonBox employs the use of artificial intelligence to tailor math problems to the learner’s level and thus is especially beneficial in teaching the basics of math (Holmes & Tuomi, 2022). Smart applications like Socratic by Google, and Photomath change the way students solve their assignments. Socratic uses artificial intelligence to provide students with answers to questions they pose on diverse topics and in the process provides individualized explanations that are suited to the student’s learning ability and speed (D'Mello & Graesser 2023). It is most beneficial for homework and study purposes and offers extensive answers to the questions in academic areas such as mathematics, science, and humanities. Likewise, Photomath is an AI-based app to solve mathematical problems by capturing the problems and providing solutions along with the explanations to help students to learn the concepts behind the solutions (Zain et al., 2023). These tools are very useful in assisting students in dealing with complex topics since they are given real-time feedback and instructions. The use of AI has also improved language learning through the development of applications such as Duolingo and Babbel. Duolingo adapts the lessons depending on the learner’s performance, which means that the tasks given to the students are always challenging, but not beyond the learners’ capabilities. The use of an AI-driven system makes it easier to track the mistakes that the student makes and adjust the subsequent lessons to correct the mistakes. Likewise, Babbel incorporates AI to assist the student in monitoring their progress in learning the vocabulary and grammar in the course (Kessler et al., 2023). These language learning tools help in making it easier for the students, especially those in primary and middle schools, to learn languages by making it a unique experience. Another aspect where the AI tools such as Miro and Padlet have brought a change in education is collaboration. Miro is an AI-based collaboration tool that allows students to form groups, discuss concepts, and participate in visual planning. This tool promotes effective collaboration in real-time as students can create a board that they can all work on, add notes, and build projects together (Karsen et al., 2022). Padlet, on the other hand, supports collaborative learning by allowing students to post and display their ideas, projects and notes in an attractive digital canvas. It also has AI components that assist in managing and categorizing the content to enhance the students’ teamwork. Such platforms encourage engagement and collaboration, both important competencies in today’s work settings. In science education, tools such as Labster and Phet Interactive Simulations offer students virtual labs and interactive experiments, allowing them to delve into complex scientific concepts without needing physical lab equipment. Labster features AI-driven simulations that enable students to perform biology, chemistry, and physics experiments in a virtual environment, allowing them to test hypotheses and investigate scientific principles safely (Tsirulnikov et al., 2023). Similarly, Phet Interactive Simulations offers AI-powered simulations across various scientific disciplines, giving students hands-on experience with experiments that may be otherwise out of reach due to limited resources. These tools are especially valuable for students who lack regular access to fully equipped labs, helping them engage more deeply with scientific inquiry. To encourage creativity, platforms like Canva and Animoto enable students to showcase their ideas through visual design and video production. Canva is a design tool powered by AI that assists students in crafting visually striking projects, including posters, presentations, and infographics, by offering customizable templates and design suggestions generated by AI (Belda-Medina & Goddard, 2024). Animoto is a video creation tool that leverages AI to simplify the video-making process. It provides ready-made templates and enhanced effects that make it easy for even younger students to create videos. AI also assists students in staying organized through tools such as Todoist and MyStudyLife. Todoist is a task management app that utilizes AI to help students monitor their assignments, exams, and activities by sending reminders and allowing for customizable to-do lists. Similarly, MyStudyLife enables students to oversee their school schedules, deadlines, and exam dates, aiding them in managing their time more effectively. These tools are particularly beneficial for students who find time management challenging, providing AI-driven solutions to help them keep up with their academic obligations (Bouchrika, 2024). Mental health plays a vital role in the well-being of students, and AI tools such as Woebot and Moodpath offer valuable emotional support for those facing anxiety, stress, or other emotional difficulties. Woebot is an AI-powered chatbot designed to assist students in managing their mental health by providing real-time support and coping strategies (Fitzpatrick et al., 2017). Moodpath monitors students' emotional states and delivers insights and resources to help them better comprehend and handle their feelings. These AI tools introduce a new dimension of emotional support, especially for students who may lack immediate access to mental health professionals. AI applications in education provide numerous advantages for students, including personalized learning experiences, improved problem-solving tools, collaborative platforms, and emotional support. It is believed that with the improvement of artificial intelligence technology, its applications in education will also become more and more powerful, creating opportunities for students to study in progressively interactive, personalized, and supportive environments. **Methodology ** The aim of this paper is to explore the opinions and attitudes of prospective teachers from the Faculty of Teacher Education at the University of Zagreb regarding the use of Artificial Intelligence (AI) in education. The study aims to find out the students’ perceptions of AI, positive and negative attitudes towards AI, the courses they think will benefit most from incorporating AI, and the specific AI applications that are most useful for teachers and students. This quantitative study is based on an online questionnaire and aims to identify the perceptions of prospective primary school teachers on the opportunities and limitations of AI in teaching practices, as well as their preferences on the use of AI tools as learners and teachers. The paper formulates three hypotheses: **Hypothesis 1**: There are no statistically significant differences in opinions and attitudes toward using AI in teaching based on the students' year of study. **Hypothesis 2**: There are no statistically significant differences in opinions and attitudes toward using AI in teaching based on the students' study module. **Hypothesis 3**: There are no statistically significant differences between the average rating of positive and the average rating of negative aspects of AI in teaching. The survey included 56 students from the University of Zagreb, Faculty of Teacher Education, including all five years of academic studies and different modules: Croatian Language, Art Culture, Informatics, Educational Sciences, and English and German Language programs. The questionnaire was voluntary and anonymous and consisted of three sections. The first part was used to collect data on the study year and the module or program students were studying. Following the previous section, the participants were asked to answer some questions regarding their opinions of artificial intelligence in the educational sector. Participants were given statements and were then asked to indicate their level of agreement or disagreement with the presented statement. The instrument utilized a Likert-type scale between 1 and 5, with each number corresponding to the following levels of consensus: 1 - completely disagree (lowest level of agreement), 2 - mostly disagree (partial disagreement), 3 - neutral (neither agree nor disagree), 4 - mostly agree (partial agreement) 5 - completely agree (highest level of agreement). The third part of the questionnaire consisted of questions about the possible application of AI technologies in learning. Students were asked which subjects they think would be most helped by AI, which AI tools are helpful for teachers, and which AI tools are helpful for students in primary education. Further, they were asked how often they would incorporate AI tools in their teaching in the future. At the end of the questionnaire, they responded to an open-ended question: „What changes do you think AI will bring to primary education in the next 10 years?“ The answers were coded and given categories, which will be discussed in the next section of the paper, and the statistically significant differences were analyzed with the help of the appropriate statistical tests. **Results and discussion** The study included 56 students from the Faculty of Teacher Education, University of Zagreb. In Chart 1, students' responses regarding their year of study are displayed. The distribution is as follows: **12%** of students are in their first year of study, **29%** in second year, **36%** in third year, **9%** in fourth year and **14%** in fifth year. Chart 1 *Distribution of students by year of study* **![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXfYxmojEbyJnawaVSp86-QSCaow5t0cv7vWiwN9ZMFjpa3a_pG9CGTOwlB3H-fjqb5k35Xir-eBARvc0jez8aFZKhkTwZtix0JJtNKqlBt6Ffvm6bUzoWQhTTGGHhYVMn9WS25ZtuTBYw6UMczejWg?key=oAm5UjhbIbALMWYU9GN06Q)** * * When it comes to their study module or study program, Chart 2 shows the distribution of students in each module: **21%** Croatian Language module, **12%** Art Culture module, **32%** Informatics module, **11%** Educational Sciences module, **20%** English Language program, **4%** German Language program. Chart 2 *Distriburion of studenty by study module/program* **![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXdT3otjBDSuWeDmEeEyCzwt_hNwKkx27TX3o2I5v59qMFcGuaNSImWQ8lXuH84thOmGegbqV8n_YyxoUOb8tkHzYu2GxM-mTgQLKRrX_CXS4hGWEL4idBcyXehlMVzaksHqengxmM1q93PGACUnwn8?key=oAm5UjhbIbALMWYU9GN06Q)** In the questionnaire, after students provided their year of study and study module/program, they were asked to respond to whether, during their studies so far, they had engaged with topics related to artificial intelligence (AI) or educational technology in any course. **64.3%** of the students asnwered yes and **35.7%** answered no. The following question was: **„How would you rate your familiarity with artificial intelligence technologies?“** Students answered on a scale from **1 to 5**, where **1** indicated „not familiar at all“ and **5** indicated „very familiar.“ The answers are displayed in Chart 3, where it can be observed that most students answered 3 (somewhat familiar) **39.3%** and 4 (familiar) **35.7%**. Only one student answered nor familiar at all and only five students rated their familiarty as very familiar. Chart 3 *Familiarity with atificial intelligence techologies* [![image.png](https://hub.ufzg.hr/uploads/images/gallery/2025-05/scaled-1680-/bpLimage.png)](https://hub.ufzg.hr/uploads/images/gallery/2025-05/bpLimage.png) To determine the method for analyzing the collected research results, the Kolmogorov-Smirnov test was conducted. The test indicated that the distributions of the measured dependent variables significantly deviate from the normal Gaussian distribution. Given this result and the sample size, non-parametric statistical indicators and results from non-parametric statistical tests will be taken into consideration. The data shown in Table 1 provides an analysis of student perceptions regarding the positive aspects of AI in education. The results show that students have a somewhat favorable view of AI's role in enhancing educational experiences. For instance, AI's potential to **improve access to education for students with special needs** received one of the highest average scores (M = 3.53), indicating that students see value in AI's ability to create more inclusive learning environments. Similarly, the potential of AI to **facilitate lesson planning and organization for teachers** was rated relatively highly (M = 3.54), suggesting that students recognize the practical benefits AI can offer to teachers. However, the perceived ability of AI to **allow teachers to focus more on individualized support** received a lower average score (M = 2.89), highlighting some skepticism about AI's capacity to enable more personalized teacher-student interactions. Despite this, the overall positive mean score of **3.20** for AI-related aspects suggests that students generally view AI as a useful tool in enhancing both learning and teaching processes in primary education. Table 1 *Students’ perceptions regarding the positive aspects of AI*
Positive aspects Mean Median Mode Std. Deviation
AI can be useful in primary education. 3.13 3 3 1.04
AI can help personalize educational experiences for students. 3.16 3 4 1.09
AI can improve learning efficiency by providing students with immediate feedback. 3.21 3 3 1.17
AI technologies can increase student engagement through interactive learning methods. 3.05 3 4 1,22
**AI can improve access to education for students with special needs.** **3.53** **4** **4** **1.20**
AI can allow teachers to focus more on providing individualized support to students. 2.89 3 3 1.12
**AI can facilitate lesson planning and organization for teachers.** **3.54** **4** **4** **1.15**
**Average rating of positive AI aspects.** **3.20** **3.42** **3.57a** **.94**
The data shown in Table 2 suggests that students perceive several significant negative aspects of AI in education, with the highest concern being that **AI may reduce students' critical thinking** by providing ready-made solutions (M = 4.59). This indicates a strong belief that relying on AI could prevent independent problem-solving and creativity in the learning process. The concern that **AI technologies could lead to an over-reliance on technology instead of creative learning methods** (M = 4.41) reflects a strong belief among respondents that there is a potential downside to integrating AI into education. This high mean score indicates that many students fear that AI might shift the focus from fostering creativity to overusing technology. Additionally, many students expressed concerns about the **ethical implications** of AI, such as privacy violations and algorithmic bias (M = 4.14), highlighting the importance of addressing these issues in educational AI deployment. Students are also concerned that AI **may not yet be advanced enough to fully understand individual student needs** (M = 4.18), reflecting skepticism about AI's ability to provide the same level of personalized support that teachers can. Interestingly, the complexity of AI technology and its potential to complicate teachers' work received a lower score (M = 3.04), suggesting that while students see AI as a challenge, they are more worried about its effects on teaching quality and creativity than its difficulty. Overall, the average rating of negative attitudes towards AI in education (M = 3.97), reflects a relatively high level of concern among students. This suggests that, overall, students are more apprehensive than optimistic about the potential drawbacks of AI in educational settings. Table 2 *Students’ perceptions regarding the negative aspects of AI*
Negative aspects Mean Median Mode Std. Deviation
AI technologies in education may be too complex to use and could complicate teachers' everyday work. 3.04 3 2 1.15
The use of AI in education may diminish the importance of the teacher's role. 3.45 4 5 1.43
**AI technologies could lead to an over-reliance on technology instead of creative learning methods.** **4.41** **5** **5** **.93**
AI is not yet advanced enough to fully understand students' individual needs. 4.18 4 5 .93
**AI may reduce students' critical thinking by providing ready-made solutions.** **4.59** **5** **5** **.82**
I am concerned about the ethical implications of using AI in education, such as student privacy violations, algorithmic bias, and reducing the human element in teaching. 4.14 5 5 1.15
**Average rating of negative aspects of AI.** **3.96** **4.08** **4.67** **.77**
This strong concern highlights the necessity for careful and responsible implementation of AI technologies, ensuring that they complement, rather than replace, traditional pedagogical methods. It also suggests a demand for more transparency and discussion around how AI will impact teaching and learning, student engagement, and creativity. The Kruskal-Wallis test was conducted to determine whether there are differences in opinions on the use of AI in teaching based on the year of study. The test showed a statistically significant difference regarding the year of study for two statements (Table 3). However, when comparing the arithmetic mean ranks for each year, it is not possible to conclude that students change their opinion consistently based on their year of study (for example, second year students are less likely to believe that AI can help students with special needs, but fifth year students believe this even less than second year students). While there is a statistically significant difference between certain groups of respondents depending on their year of study, the results are irregular across categories. Therefore, **Hypothesis 1** is partially confirmed. Table 3 *Differences in attitudes toward the use of AI in teaching based on the year of study*
Differences in attitudes toward the use of AI in teaching based on the year of study. Chi-Square df Asymp. Sig.
Perceived familiarity with AI technologies. 5.629 4 .229
AI can be useful in primary education. 3.753 4 .440
AI can help personalize educational experiences for students. 3.806 4 .433
AI can improve learning efficiency by providing students with immediate feedback. 1.843 4 .765
AI technologies can increase student engagement through interactive learning methods. 5.712 4 .222
AI can improve access to education for students with special needs. 11.444 4 .022
AI can allow teachers to focus more on providing individualized support to students. 3.446 4 .486
AI can facilitate teachers' planning and organization of lessons. 9.723 4 .045
AI technologies in education may be too complex to use and could complicate teachers' everyday work. 2.031 4 .730
The use of AI in education may diminish the importance of the teacher's role. 8.100 4 .088
AI technologies could lead to an over-reliance on technology instead of creative learning methods. 6.157 4 .188
AI is not yet advanced enough to fully understand students' individual needs. 8.637 4 .071
AI may reduce students' critical thinking by providing ready-made solutions. 3.872 4 .424
I am concerned about the ethical implications of using AI in education, such as student privacy violations, algorithmic bias, and reducing the human element in teaching. 8.690 4 .069
**Average rating of positive AI aspects** 5.332 4 .255
**Average rating of negative aspects of AI** 8.776 4 .067
To determine whether there are differences in opinions on the use of AI in teaching based on the study module, the Kruskal-Wallis test was conducted for each statement and for the average rating of positive aspects. The test identified statistically significant differences based on the module for three statements shown in the Table 4, and for the average rating of positive aspects of AI. Table 4 *Differences in attitudes toward the use of AI in teaching based on the study module*
Statement Module / program Mean Rank
AI can help personalize educational experiences for students. English Language 25.50
German Language 28.75
Croatian Language 20.38
Art Culture 17.64
Educational Sciences 32.67
Informatics 38.56
AI can improve learning efficiency by providing students with immediate feedback. English Language 25.41
German Language 38.25
Croatian Language 24.79
Art Culture 14.50
Educational Sciences 39.17
Informatics 33.67
AI can improve access to education for students with special needs. English Language 22.05
German Language 33.25
Croatian Language 22.08
Art Culture 14.00
Educational Sciences 36.08
Informatics 38.32
Average rating of positive AI aspects. English Language 24.55
German Language 28.00
Croatian Language 22.00
Art Culture 15.93
Educational Sciences 36.58
Informatics 36.41
When observing the arithmetic means of ranks, they indicate that students from the Informatics module generally express more positive opinions on three statements compared to other modules. For the average rating of positive aspects, a post-hoc analysis was conducted to determine between which modules the differences were statistically significant. Post-hoc tests revealed that students from the Informatics module express significantly more positive attitudes toward the use of AI in teaching compared to students from the English Language, Croatian Langugage, and Art Culture modules. However, no significant difference was found between students from the Informatics module and those from the German Language or educational sciences modules. Therefore, it cannot be concluded that Informatics module students have more positive attitudes toward AI compared to other modules and **Hypothesis 2** is partially confirmed. The Wilcoxon Signed Ranks test was conducted to determine whether there is a statistically significant difference between the average ratings of positive and negative aspects of AI in teaching. The result indicated that there is a statistically significant difference (Table 5). Table 5 *Differences in attitudes toward the use of AI in teaching based on the study module*
Wilcoxon Signed Ranks Test Average rating of negative aspects of AI Average rating of positive aspects of AI
Z -3.846b
Asymp. Sig. (2-tailed) .000
By comparing the medians, it can be concluded that there is statistically significantly greater agreement with the negative aspects than with the positive aspects of using AI in teaching (Chart 4) so here it can be concluded that the **Hypothesis 3** is refuted. Chart 4 *Average rating of positive and negative aspects of using AI in teaching* ****![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXduRfXx-uFY4KgIqT3J9wvJ2HyeEIQ3ORCscFp6sFI1DxlbBuBiY4LFv6YWJcAmMontLY-X0SvudjB1yRspPz1b-lfOhE5rNW08MUufIN2GxsEiCB1CfwYeedDtY8OXn9VpQiFW76gV4kP7vPDeiDw?key=oAm5UjhbIbALMWYU9GN06Q)**** In the next section of the questionnaire, students were presented with a list of applications that could be useful for teachers, and they were asked to select which ones they considered the most useful using a multiple-choice format. They were also able to write applications that they consider useful. The applications were as follows: **Intelligent Tutoring Systems:** Carnegie Learning's MATHia – This system uses artificial intelligence to adapt math lessons to students based on their responses, providing personalized instructions and explanations. **Automated Grading and Feedback Systems:** Grammarly – An AI tool that automatically reviews and evaluates written text's grammatical and stylistic aspects, offering feedback to help students improve their writing assignments. Turnitin – A platform that uses AI to analyze student papers, detect plagiarism, and provide feedback on the originality of the content. **Adaptive Learning Platforms:** Khan Academy – An online educational platform that uses AI to personalize lessons and track student progress, adjusting the material according to their needs. DreamBox – An adaptive math learning platform that uses AI to tailor instructional content in real time based on student responses and progress. **AI for Data Analysis and Student Assessment:** Civitas Learning – A platform that uses AI to analyze large amounts of student data to predict their success and provide interventions to improve outcomes. **Edmentum** – A system that leverages data analytics and AI to assess student progress and generate reports for teachers to understand student needs better. **AI Tools for Classroom Organization and Management:** ClassDojo – A platform incorporating AI to help teachers manage student behavior and engagement. Teachers can track student progress, communicate with parents, and customize student activities. **AI Tools for Supporting Students with Special Needs:** Otter.ai – An AI-powered transcription tool that assists students with hearing or writing difficulties by providing real-time transcription of lectures. ModMath – An AI application designed for students with dyslexia or motor disabilities, allowing them to solve math problems on a digital platform without the need for handwriting. As shown in Chart 5, most students (N=30) see the potential of AI tools for supporting students with special needs which is closely followed by Automated grading and feedback systems (N = 26). Two students answered none. Chart 5 *Usefulness of AI application designed to support teachers* ![](https://hub.ufzg.hr/uploads/images/gallery/2025-05/embedded-image-brpibvl1.png) When it comes to applications designed to support students, the list was as follows. **AI-driven educational games**: like Minecraft: Education Edition, use AI to create dynamic educational scenarios where students can learn programming, math, science, and other subjects through interactive gameplay. DragonBox is an educational game that utilizes AI to tailor math challenges to the student’s knowledge level, especially helping with learning basic mathematical concepts. **AI tools for learning and problem-solving**: Socratic by Google, an AI app that answers students' questions and provides problem solutions in various subjects with tailored explanations. Photomath is an AI tool that allows students to scan math problems and receive step-by-step solutions and explanations to better understand the material. **Language learning:** Duolingo is an AI tool that personalizes lessons according to the student’s progress, adjusting the difficulty of tasks for more effective language learning. Babbel is another AI language-learning app that adapts lessons to the student’s needs and tracks progress in vocabulary and grammar. **AI tools for collaboration and teamwork**: Miro provides an AI-powered platform that enables students to work in teams, brainstorm together, and visually plan projects. Padlet is an online tool using AI to facilitate collaboration, allowing students to share ideas, work, and projects in one place. **AI tools for scientific experiments and simulations**: Labster, offer virtual labs and scientific experiment simulations, enabling students to explore biology, chemistry, physics, and other scientific fields. Phet Interactive Simulations uses AI-driven simulations that allow students to explore various scientific concepts and experiments in an interactive environment. **Creativity and design: **Canva is an AI design tool that helps students create visually appealing projects, such as posters, presentations, and infographics, with customizable templates and elements. Animoto is an AI-powered video creation tool that allows students to make video content using templates and AI-driven effects easily. **Creative writing development:** Storybird is an AI platform that guides students through creating and writing their own stories, providing ideas and visual support. **Research and information gathering:** Kiddle is an AI-powered search engine tailored for children, ensuring safe and relevant results for younger students when searching for topics for their projects. Elicit is an AI tool that assists students in researching various topics by suggesting relevant articles and sources and providing summaries. **AI tools for virtual and augmented reality**: Google Expeditions, an AI-powered AR and VR platform that allows students to take virtual trips and explore historical, scientific, and cultural locations. **Time management and organization:** Todoist is an AI tool that helps students organize their tasks and activities by providing reminders and customizable to-do lists. MyStudyLife is an AI app that helps students track school assignments, deadlines, and exam dates, assisting them in better managing their time. **Mental health and emotional support:** Woebot is an AI chatbot that provides emotional support to students, helping them manage anxiety, stress, and emotional challenges. Moodpath is an AI app that monitors students' emotional states and provides tools to better understand and manage their emotions. As shown in Chart 6, most students (N=29) see the potential of AI tools used for language learning and scientific experiments and simulations (N=29). This is closely followed by AI tools for Creativity and design (N=28). On the low end of the score, students don’t really see the benefits of using AI tools for research and information (N=12) and creative writing (N=6), also, one student answered „None.“ Chart 6 *Usefulness of applications designed to support students* **![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeSW4YvXJKid-AUDeYWCxqcnZtd_fV351hoSmwxWvn8pCCpDEYyZiJR9ATQYMXzq0Rg-rXy5LWgQ8TN8Phb0wcdCemw58q8gBpVD9zaT_dfOTJ8dD7A6A7ailq5BurvIisNt24i-Kb565OXs1sIs6s?key=oAm5UjhbIbALMWYU9GN06Q)** Regarding the subjects they think should be supported by AI (Chart 7) the subject Informatics was mentioned most often (N=40). Chart 7 *Usefulness of applications in various subjects* **![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXcZZBEDX1vGt_xglkraJK4BJxjESItaN1DXbASdjoLhM_8ocE80xnQAa0zGHM-0ggdHbAbfPVMEFWY3IbHOQxh4XvlEm8mCKLPtCA7mNXS9RHaCiAX-bx_t1Rhdu2TLEa3J2OFbMzisrOKqifjK4m4?key=oAm5UjhbIbALMWYU9GN06Q)** Here is a categorization of the provided text into a few key categories: **1. Positive Impacts of AI on Education:** Ease of Use for Teachers: AI will likely facilitate the preparation of materials and assignments, reducing the workload for teachers. Teachers will be able to track and evaluate student progress more easily and make their classes more engaging. Enhanced Learning Tools: More tools will make learning more interesting both in class and independently. AI will provide personalized learning, adapting to each student's pace, interests, and needs, helping students with different abilities to progress better. Administrative Automation: AI will automate administrative tasks like grading and progress tracking, reducing the burden on teachers. Interactive Learning for Abstract Concepts: AI will help explain complex topics visually, especially in subjects like natural sciences. Increased Efficiency: AI can make teaching more efficient and lessen the burden on teachers, making classes more effective and fun. **2. Negative Impacts of AI on Education:** Over-reliance on AI: Both teachers and students may become too dependent on AI, leading to a reduction in independence and creativity. Over-reliance on AI might diminish critical thinking skills and logical reasoning among students. Decreased Teacher Involvement: The role of teachers might be diminished, with AI tools handling tasks that were previously teacher-led, potentially leading to a decrease in the importance of the teacher-student relationship. Laziness and Reduced Critical Thinking: Students might rely on AI to complete assignments, leading to a lack of effort and reduced engagement in learning. AI might encourage a „shortcut“ mentality, where students seek easy solutions rather than learning and problem-solving. Reduced Socialization and Communication: The use of AI may lead to less social interaction and communication among students, affecting their social skills and ability to focus. **3. Mixed Impacts and Concerns:** Balance between Benefits and Risks: While AI can provide valuable tools for learning, especially in areas where schools lack resources (e.g., science labs), there is concern about maintaining emotional and human interaction in education, which is crucial for child development. Need for Gradual Implementation: Technology should be introduced gradually to avoid negative impacts on critical thinking and ensure that AI enhances rather than replaces human elements in education. **4. Skepticism and Caution:** Skepticism toward AI Integration: Many fear that the integration of AI will lead to a loss of key educational values, such as creativity, problem-solving, and personal development. Some believe AI will lead to laziness and a lack of effort in both teaching and learning processes. Potential for Negative Long-term Effects: Some express concern that the long-term impact of AI will be a decrease in socialization and hands-on learning, with children becoming overly reliant on technology for solutions. **Conclusion** In conclusion, this study examined the attitudes of prospective primary school teachers towards the use of AI in education, focusing on three hypotheses. The first hypothesis proposed that there are no statistically significant differences in opinions and attitudes based on the students' year of study. The results partially supported this hypothesis, as some differences were found across study years, particularly regarding the perception of AI’s role in supporting students with special needs and facilitating lesson planning. Therefore, it was concluded that the year of study has only a minor influence on shaping attitudes and opinions, as the observed differences were not consistent across all evaluated categories. The second hypothesis suggested that there are no statistically significant differences in opinions based on the students' study module. This hypothesis was also partially confirmed, as students from the Informatics module exhibited significantly more positive attitudes towards AI compared to those from the language and arts modules. Nevertheless, no significant differences were found between Informatics and some other modules, such as Educational Sciences, highlighting a subtle connection between the study module and attitudes toward AI. The third hypothesis, which stated that there are no statistically significant differences between the average ratings of positive and negative aspects of AI in teaching, was refuted. The results indicated a statistically significant difference, with students rating the negative aspects of AI—such as concerns about critical thinking, creativity, and ethical implications—more strongly than the positive aspects, such as AI’s potential to improve learning efficiency and support personalized education. Overall, what these findings show is the complexity of integrating AI into education. Prospective teachers not only see the potential benefits of using AI with students but also are very cautious about the possible drawbacks. They worry about the potential impact of creative AI on the kind of work that students do and on the kinds of decisions that teachers make about the kind of work that students do. And they especially worry about the kind of impact that creative AI could have on student autonomy. **Literature** Harry, A., & Sayudin, S. (2023). Role of AI in Education. *Interdiciplinary Journal and Hummanity (INJURITY)*, *2*(3), 260-268. Alneyadi, Saif, Wardat, Yousef, Alshannag, Qasim, & Abu-Al-Aish, Ahmad. (2023). The effect of using smart e-learning app on the academic achievement of eighth-grade students. *EURASIA Journal of Mathematics, Science and Technology Education*, *19*(4), em 2248. HolonIQ. (2023). *Artificial intelligence in education: 2023 survey insights*. HolonIQ. [https://www.holoniq.com](https://www.holoniq.com) Sutaria, N. (2022). Bias and ethical concerns in machine learning. *ISACA J.*, *4*, 1-4. McNulty, N. (2023). *AI data privacy in schools: Navigating the challenges and solutions*. Retrieved from [https://www.niallmcnulty.com](https://www.niallmcnulty.com) GUJJULA, R., & Sanghera, K. (2023). 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AI-Driven Digital Storytelling: A Strategy for Creating English as a Foreign Language (EFL) Materials. *International Journal of Linguistics Studies*, *4*(1), 40-49. Bouchrika, I. Best School Organization Apps for Time Management, Note-Taking & Mind Mapping in 2024. # Analiza digitalnih resursa za provedbu nastave informatike u osnovnim školama Grada Zagreba
[![logo stoo2_1 (no).png](https://hub.ufzg.hr/uploads/images/gallery/2024-09/scaled-1680-/logo-stoo2-1-no.png)](https://hub.ufzg.hr/uploads/images/gallery/2024-09/logo-stoo2-1-no.png) **Odgoj danas za sutra:** **Premošćivanje jaza između učionice i realnosti** 3\. međunarodna znanstvena i umjetnička konferencija Učiteljskoga fakulteta Sveučilišta u Zagrebu Suvremene teme u odgoju i obrazovanju – STOO4 u suradnji s Hrvatskom akademijom znanosti i umjetnosti
##### **Lovro Strmo, Mario Dumančić, Krešo Tomljenović** *Učiteljski fakultet Sveučilišta u Zagrebu, Hrvatska* *lovro.strmo@gmail.com*
**Sekcija - Odgoj i obrazovanje za digitalnu transformaciju****Broj rada: 48****Kategorija: Izvorni znanstveni rad**
##### **Sažetak**
Život u digitalnom dobu postavlja pred obrazovni sustav niz izazova, a razvoj informacijsko-komunikacijskih tehnologija (IKT) stvara potrebu za digitalnim kompetencijama kao ključnim elementom suvremenog obrazovanja. Hrvatski obrazovni sustav uključuje nastavu Informatike kao zaseban predmet u višim razredima osnovne škole, dok se u nižim razredima provodi kao izborni predmet. Unatoč tome, opremljenost škola informatičkom opremom varira, što može utjecati na kvalitetu nastave. Cilj ovog istraživanja bio je ispitati opremljenost osnovnih škola u Zagrebu IKT resursima te analizirati zadovoljstvo nastavnika dostupnom tehnologijom. Istraživanje je provedeno pomoću anketnog upitnika podijeljenog u šest segmenata, od kojih pet opisuje dostupnu opremu, dok posljednji dio ispituje stavove nastavnika. Rezultati nisu pokazali statistički značajne razlike u stavovima nastavnika s obzirom na spol, radno iskustvo i razinu opremljenosti škole, no ukazali su na potrebu daljnjeg ulaganja u školsku infrastrukturu. Razvoj digitalnih vještina prepoznat je na europskoj razini kao ključno područje obrazovne politike. Inicijative poput projekta e-Škole imaju za cilj smanjenje digitalnog jaza i osiguravanje jednake dostupnosti tehnologije u školama. Ovaj rad analizira trenutne resurse, izazove i mogućnosti za unapređenje nastave Informatike, ističući važnost kontinuiranog ulaganja u digitalne alate i edukaciju nastavnika.
***Ključne riječi:***
digitalna podjela, informacijsko-komunikacijske tehnologije, nastava Informatike, osnovna škola, školska digitalna oprema
**Uvod i pregled istraživanja** Društvo znanja, koncept koji je 1950-ih predvidio Peter Drucker, temelji se na primjeni informacijsko-komunikacijskih tehnologija u svim aspektima života (Drucker, 1993). U današnje doba digitalizacija obrazovanja postaje ključan prioritet kako bi se osigurala konkurentnost učenika na globalnom tržištu rada (Valacich & Schneider, 2015). Nastava Informatike u školama ima ključnu ulogu u razvijanju digitalnih vještina, koje su temeljne za suvremeni svijet te je digitalna kompetencija 2006. godine istaknuta kao jedno od osam ključnih područja razvoja u sklopu europskog referentnog okvira koje propisuje Europska komisija (Europska komisija, 2019). U kontekstu sljedećeg stupnja primjene IKT teknologije u obrazovanju se apostrofiraju pojmovi kao *pametno obrazovanje* (eng. *Smart Education*) (Homen & Dumančić, 2023). Prema istraživanjima Europske izvršne agencije za obrazovanje i kulturu (Eurydice, 2025), digitalne vještine postale su sastavni dio obrazovnih strategija u Europskoj uniji. Pandemija COVID-19 dodatno je istaknula potrebu za digitalizacijom školstva, naglasivši kako mnogi obrazovni sustavi nisu bili spremni za prijelaz na online nastavu (Europska komisija, 2022). Može se reći da je razina digitalne izloženosti današnjih generacija učenika, koje se često naziva i *digitalnim domorocima* (Beatty & Egan, 2020), u nesrazmjeru s razinom digitalne izloženosti obrazovnog sustava. U Hrvatskoj, kurikulum nastave Informatike propisuje Ministarstvo znanosti i obrazovanja, no njegova primjena uvelike ovisi o dostupnoj tehnologiji u školama. Iz usporedne analize mreže *Eurydice* o obrazovnim sustavima europskih zemalja vidljivo je da su pojedine europske države uvele Informatiku kao obavezan predmet u ranim razredima osnovne škole, dok druge provode integrirani model u sklopu drugih predmeta (Europska komisija, 2023). Dosadašnja istraživanja pokazuju da je digitalna podjela jedan od ključnih izazova u obrazovanju (Lythreatis, Kumar Singh & El-Kassar, 2022). Neki autori ističu kako tehnologija u obrazovanju može imati i druge negativne posljedice, poput smanjenja kritičkog razmišljanja i povećane ovisnosti o digitalnim alatima (Kraner, 2022). No, uz pravilnu implementaciju, digitalna pismenost može značajno poboljšati obrazovne ishode i pripremiti učenike za buduće karijere. Primjerice, Tomljenović i Zovko (2016) su pokazali da se bolji rezultati u nastavi matematike ostvaruju uporabom IKT, u odnosu na klasično izvođenje nastave, bez primjene IKT. Korištenje IKT-a u nastavi ne zamjenjuje klasične načine i metode poučavanja, već služi učiteljima i nastavnicima kao alat koji je u stanju obogatiti, unaprijediti ili pak nadopuniti pojedine dijelove nastavnog procesa (Dukić, Petrinšak & Pinjušić, 2020). Hrvatska ne odskače značajno od europskog prosjeka u računalnoj i informacijskoj pismenosti, ali unutar obrazovnog sustava postoje velike razlike u opremljenosti škola, što može utjecati na jednake mogućnosti za sve učenike (NCVVO, 2025). ***Cilj i hipoteze istraživanja*** Cilj istraživanja je ispitati dostupnu IKT opremu koju učenici koriste u nastavi, a nastavnici u pripremi i izvođenju nastave, u osnovnim školama na području Grada Zagreba. Dodatno, želi se ispitati zadovoljstvo nastavnika stanjem dostupne opreme, zadovoljstvo mogućnostima opreme koju koriste za pripremu i izvođenje nastave, učestalost korištenja pojedinih uređaja i tehnologija, tehničke karakteristike i starost tih uređaja te mogućnost ostvarivanja ishoda učenja u sklopu nastavnog predmeta informatika s opremom koju škole imaju na raspolaganju. U istraživanju su postavljene sljedeće hipoteze: **H1.** Stavovi nastavnika se ne razlikuju statistički značajno s obzirom na spol nastavnika. **H2.** Stavovi nastavnika se statistički značajno razlikuju s obzirom na radno iskustvo nastavnika. **H3.** Stavovi nastavnika se statistički značajno razlikuju s obzirom na razinu opremljenosti škole. **Metode istraživanja** Provedeno je kvantitativno istraživanje pomoću anketnog upitnika na platformi *Google Obrasci*. Anketa je podijeljena u šest segmenata u kojima se ispituje: 1. osobne karakteristike ispitanika kao što su škola, spol, radno iskustvo, uključenost u program e-škole i slično; 2. broj učenika i broj razrednih odjela u školama u kojoj radi ispitanik te broj učenika i razrednih odjela koji pohađaju nastavu Informatike; 3. podaci o digitalnoj tehnologiji u školi koji obuhvaćaju popis opreme koja se koristi od strane nastavnika i učenika te učestalost korištenja svakog pojedinog uređaja; 4. starost opreme koju učenici koriste u nastavi Informatike; 5. starost opreme koju nastavnici koriste za pripremu i/ili izvođenje nastave; 6. subjektivni stavovi nastavnika o adekvatnosti dostupne opreme - Likertova skala stavova s 5 nivoa gdje prvi nivo označava „uopće se ne slažem“ a peti nivo označava „u potpunosti se slažem“. Ispitanici su 15 nastavnika Informatike u osnovnim školama na području Zagreba. Anketa je provedena u lipnju i rujnu 2024. godine. Podaci su analizirani pomoću deskriptivne statistike, a korelacijske analize korištene su za ispitivanje povezanosti između razine opremljenosti i stavova nastavnika. Statistička analiza je provedena alatom *Statistica*, verzija 14.2.0.18. **Rezultati** Anketni uzorak sadrži 15 ispitanika, od kojih je 10 učiteljica i 5 učitelja Informatike. 93,3% ispitanika je uključeno u program e-škole. Raspodjela ispitanika s obzirom na radni staž je prikazan na Grafikonu 1. ![](https://hub.ufzg.hr/uploads/images/gallery/2025-05/embedded-image-sqicury9.png) Grafikon 1. Radno iskustvo na poziciji učitelja/nastavnika Informatike Odgovori na pitanja o dostupnoj IKT opremi za nastavu Informatike u školi su prikazani na Grafikonu 2. Vidljivo je iz odgovora da su u svim školama prisutna stolna računala, u 53,3% njih prisutna su i prijenosna računala, a tableti i pametne ploče u 60%. Dodatna oprema poput uređaja za snimanje, robota za programiranje, projektora i sličnog dostupna je u 73,3% škola. Pametni telefoni dostupni su samo u jednoj školi. ![](https://hub.ufzg.hr/uploads/images/gallery/2025-05/embedded-image-dy18arwy.png) Grafikon 2. Dostupna oprema u informatičkoj učionici Učestalost korištenja opreme u nastavi Informatike je prikazana na Grafikonu 3. Ponuđene su 4 kategorije učestalosti korištenja: nikada, rijetko, često i vrlo često/uvijek. Iz odgovora je vidljivo da nezanemariv broj ispitanika koristi vlastitu opremu za ostvarenje obrazovnih ishoda: 13,33% često, 33,33% rijetko. Pametni sat su navedeni jedini kao oprema koja se nikada ne koristi. Upotreba pametnog telefona je navedena da se koristi „rijetko“ u 53,33% uzoraka no iz Grafikona 2. je vidljivo da samo 6,67% ispitanika navodi da škola posjeduje pametne telefone za izvođenje nastave. Zaključak je da u navedenim slučajevima učenici vjerojatno koriste vlastitu opremu. ![](https://hub.ufzg.hr/uploads/images/gallery/2025-05/embedded-image-0znl17rf.png) Grafikon 3. Dostupna oprema u školama koju učenici koriste u nastavi Zatim je ispitana vrsta i učestalost opreme koju nastavnici koriste za pripremu i izvođenje nastave. Odgovori su vidljivi na Grafikonu 4. Također je vidljivo da nastavnici trebaju koristiti djelomično i svoju vlastitu opremu za ostvarenje obrazovnih ishoda: 13,33% uvijek, 20% rijetko. ![](https://hub.ufzg.hr/uploads/images/gallery/2025-05/embedded-image-e3x6ghea.png) Grafikon 4. Oprema koju učitelji koriste za pripremu i izvođenje nastave te učestalost njezina korištenja Rezultati na pitanje o učestalosti nabave nove IKT opreme u školi su prikazani na Grafikonu 5. Rezultati prikazuju da se kod čak 60% ispitanika oprema obnavlja rjeđe od svakih 4 godine. Zanimljivo je da čak 26,67% ispitanika ne zna koliko se često nabavlja nova IKT oprema s kojom oni rade. ![](https://hub.ufzg.hr/uploads/images/gallery/2025-05/embedded-image-q1m4lanc.png) Grafikon 5. Učestalost nabave nove informatičke opreme Subjektivni stavovi ispitanika na niz pitanja, s deskriptivnom statistikom odgovora, prikazani su u Tablici 1. Iz odgovora je vidljivo da su nastavnici izrazili niže slaganje s tvrdnjama u vezi adekvatnosti opreme i učestalosti obnavljanja opreme za izvođenje nastave. Višim ocjenama su ispitanici ocijenili tvrdnje da je uz postojeću razinu opremljenosti IKT-om moguće ostvariti obrazovne ishode predviđene kurikulumom. Tablica 1. Deskriptivna statistika odgovora na pitanja s Likertovom skalom
**Tekst pitanja** **1** **(%)** **2** **(%)** **3** **(%)** **4** **(%)** **5** **(%)** **X1** **SD2**
Škola u kojoj radim posjeduje dovoljan broj računala, tableta i ostale informatičke opreme za izvođenje nastave informatike. 13,33 20,00 0,00 20,00 46,67 **3,67** **1,59**
Oprema koju posjeduje škola u kojoj radim je u adekvatnom stanju za izvođenje kvalitetne nastave informatike. Stolno računalo 20,00 13,33 26,67 20,00 20,00 **3,07** **1,44**
Laptop\* 13,33 6,67 26,67 13,33 20,00 **2,60** **1,84**
Tablet\* 26,67 33,33 20,00 0,00 13,33 **2,20** **1,42**
Projektor 13,33 6,67 20,00 40,00 20,00 **3,47** **1,30**
Pametna ploča\* 6,67 6,67 6,67 33,33 40,00 **3,73** **1,58**
Ostalo\* 13,33 13,33 20,00 26,67 13,33 **2,73** **1,67**
Oprema koju koristim u nastavi informatike omogućava provođenje svih sadržaja i ostvarivanje svih ishoda predviđenih kurikulumom nastavnog predmeta informatika. 0,00 0,00 20,00 46,67 33,33 **4,13** **0,74**
Škola u kojoj radim dovoljno često obnavlja informatičku opremu. 33,33 6,67 26,67 13,33 20,00 **2,80** **1,57**
Škola u kojoj radim posjeduje adekvatnu brzinu i kvalitetu interneta koja omogućava neometan i kvalitetan rad u nastavi informatike. 0,00 6,67 20,00 53,33 20,00 **3,87** **0,83**
Aplikacije koje su učenicima dostupne su dovoljne za kvalitetnu obradu sadržaja i ispunjavanje ishoda propisanih predmetnim kurikulumom. 0,00 6,67 13,33 53,33 26,67 **4,00** **0,85**
Uz bolju opremu bi nastava Informatike bila značajno sadržajnija/drukčija/bolja. 0,00 0,00 6,67 26,67 66,67 **4,60** **0,63**
1aritmetička sredina, 2 standardna devijacija, \* razlika do 100% se odnosi na ispitanike koji ne koriste navedenu opremu
Najveća razina slaganja je s tvrdnjom da bi uz bolju opremljenost nastava Informatike bila značajno sadržajnija/drukčija/bolja (srednja ocjena 4,6 uz standardnu devijaciju 0,63). Odgovori na navedeno pitanje su prikazani u Grafikonu 6. ![](https://hub.ufzg.hr/uploads/images/gallery/2025-05/embedded-image-dzi7y9db.png) Grafikon 6. Stavovi nastavnika o kvaliteti nastave Informatike uz korištenje bolje opreme U Tablici 2. su prikazani rezultati analize usporedbe stavova s obzirom na spol ispitanika. Rezultati ne pokazuju statistički značajnu razliku u stavovima nastavnika s obzirom na spol nastavnika. Jedino se pokazala statistički značajna razlika u stavu oko adekvatnog stanja tableta za izvođenje nastave, no s obzirom na srednju vrijednost svih parametara (p=0,5) te činjenice da tablete ne koriste svi ispitanici, navedeni rezultat možemo smatrati anomalijom, te bi navedenu povezanost trebalo ispitati na većem uzorku ispitanika. Tablica 2. Analiza stavova nastavnika s obzirom na spol ispitanika
**Tekst pitanja s Likertovom skalom stavova** **U**1 **z** **p**2
Škola u kojoj radim posjeduje dovoljan broj računala, tableta i ostale informatičke opreme za izvođenje nastave informatike. 24,00 0,06 0,95
Oprema koju posjeduje škola u kojoj radim je u adekvatnom stanju za izvođenje kvalitetne nastave informatike. Stolno računalo 23,00 -0,18 0,85
Laptop 20,00 0,55 0,58
Tablet 6,50 -2,20 0,03
Projektor 8,50 -1,96 0,05
Pametna ploča 16,50 -0,98 0,33
Ostalo 18,00 -0,80 0,43
Oprema koju koristim u nastavi informatike omogućava provođenje svih sadržaja i ostvarivanje svih ishoda predviđenih kurikulumom nastavnog predmeta informatika. 15,00 1,16 0,24
Škola u kojoj radim dovoljno često obnavlja informatičku opremu. 17,00 0,92 0,36
Škola u kojoj radim posjeduje adekvatnu brzinu i kvalitetu interneta koja omogućava neometan i kvalitetan rad u nastavi informatike. 22,00 0,31 0,76
Aplikacije koje su učenicima dostupne su dovoljne za kvalitetnu obradu sadržaja i ispunjavanje ishoda propisanih predmetnim kurikulumom. 21,50 -0,37 0,71
Uz bolju opremu bi nastava Informatike bila značajno sadržajnija/drukčija/bolja. 21,00 0,43 0,67
1 Mann – Whitney U test, 2 statistička značajnost za p<0,05
U Tablici 3. su prikazani rezultati analize usporedbe stavova nastavnika s obzirom na radno iskustvo ispitanika. Rezultati ne pokazuju statistički značajnu razliku u stavovima nastavnika s obzirom na duljinu radnog staža nastavnika. Srednja vrijednost parametra p za niz tvrdnji je p=0,49. Tablica 3. Analiza stavova nastavnika s obzirom na radno iskustvo ispitanika
**Tekst pitanja s Likertovom skalom stavova** **H**1 **p**2
Škola u kojoj radim posjeduje dovoljan broj računala, tableta i ostale informatičke opreme za izvođenje nastave informatike. 3,93 0,42
Oprema koju posjeduje škola u kojoj radim je u adekvatnom stanju za izvođenje kvalitetne nastave informatike. Stolno računalo 4,47 0,35
Laptop 1,98 0,74
Tablet 2,99 0,56
Projektor 6,05 0,20
Pametna ploča 4,48 0,35
Ostalo 2,32 0,68
Oprema koju koristim u nastavi informatike omogućava provođenje svih sadržaja i ostvarivanje svih ishoda predviđenih kurikulumom nastavnog predmeta informatika. 5,11 0,28
Škola u kojoj radim dovoljno često obnavlja informatičku opremu. 3,34 0,50
Škola u kojoj radim posjeduje adekvatnu brzinu i kvalitetu interneta koja omogućava neometan i kvalitetan rad u nastavi informatike. 4,69 0,32
Aplikacije koje su učenicima dostupne su dovoljne za kvalitetnu obradu sadržaja i ispunjavanje ishoda propisanih predmetnim kurikulumom. 3,47 0,48
Uz bolju opremu bi nastava Informatike bila značajno sadržajnija/drukčija/bolja. 0,64 0,96
1 Kruskal – Wallis test (4, N= 15), 2 statistička značajnost za p<0,05
Tablica 4. prikazuje prikazuje statističku analizu razlike u stavovima s obzirom na razinu opremljenosti škole. Također se nije pokazala statistički značajna razlika u stavovima među ispitanicima koji rade u slabije opremljenim školama u odnosu na ispitanike iz bolje opremljenih škola (uz srednju vrijednost p parametra p=0,58). Tablica 4. Analiza stavova nastavnika s obzirom na razinu opremljenosti škole
**Tekst pitanja s Likertovom skalom stavova** **H**1 **p**2
Škola u kojoj radim posjeduje dovoljan broj računala, tableta i ostale informatičke opreme za izvođenje nastave informatike. 2,77 0,43
Oprema koju posjeduje škola u kojoj radim je u adekvatnom stanju za izvođenje kvalitetne nastave informatike. Stolno računalo 0,07 1,00
Laptop 3,83 0,28
Tablet 1,10 0,78
Projektor 0,61 0,89
Pametna ploča 2,77 0,43
Ostalo 2,61 0,46
Oprema koju koristim u nastavi informatike omogućava provođenje svih sadržaja i ostvarivanje svih ishoda predviđenih kurikulumom nastavnog predmeta informatika. 2,80 0,42
Škola u kojoj radim dovoljno često obnavlja informatičku opremu. 2,25 0,52
Škola u kojoj radim posjeduje adekvatnu brzinu i kvalitetu interneta koja omogućava neometan i kvalitetan rad u nastavi informatike. 0,28 0,96
Aplikacije koje su učenicima dostupne su dovoljne za kvalitetnu obradu sadržaja i ispunjavanje ishoda propisanih predmetnim kurikulumom. 3,11 0,37
Uz bolju opremu bi nastava Informatike bila značajno sadržajnija/drukčija/bolja. 3,12 0,37
1 Kruskal – Wallis test (3, N= 15), 2 statistička značajnost za p<0,05
U Tablici 5. je prikazana statistička analiza razlike u stavovima s obzirom na učestalost nabavke nove opreme u školi. Također se nije pokazala statistički značajna razlika, uz srednju vrijednost p parametra p= 0,4. Tablica 5. Analiza stavova nastavnika s obzirom na učestalost nabave nove opreme
**Tekst pitanja s Likertovom skalom stavova** **H**1 **p**2
Škola u kojoj radim posjeduje dovoljan broj računala, tableta i ostale informatičke opreme za izvođenje nastave informatike. 2,70 0,44
Oprema koju posjeduje škola u kojoj radim je u adekvatnom stanju za izvođenje kvalitetne nastave informatike. Stolno računalo 1,96 0,58
Laptop 2,72 0,44
Tablet 4,05 0,26
Projektor 5,52 0,14
Pametna ploča 3,51 0,32
Ostalo 5,11 0,16
Oprema koju koristim u nastavi informatike omogućava provođenje svih sadržaja i ostvarivanje svih ishoda predviđenih kurikulumom nastavnog predmeta informatika. 1,81 0,61
Škola u kojoj radim dovoljno često obnavlja informatičku opremu. 2,95 0,40
Škola u kojoj radim posjeduje adekvatnu brzinu i kvalitetu interneta koja omogućava neometan i kvalitetan rad u nastavi informatike. 1,35 0,72
Aplikacije koje su učenicima dostupne su dovoljne za kvalitetnu obradu sadržaja i ispunjavanje ishoda propisanih predmetnim kurikulumom. 2,62 0,45
Uz bolju opremu bi nastava Informatike bila značajno sadržajnija/drukčija/bolja. 3,53 0,37
1 Kruskal – Wallis test (3, N= 15), 2 statistička značajnost za p<0,05
** ** **Rasprava** Rezultati pokazuju da su hrvatske škole u prosjeku solidno opremljene, no postoje značajne razlike među školama. Prema istraživanju ICILS (NCVVO, 2014), hrvatski učenici postižu rezultate bliske europskom prosjeku u računalnoj pismenosti, što ukazuje na relativno dobar sustav informatike u obrazovanju. Unatoč naporima projekta e-Škole, digitalna podjela između škola ostaje izazov. Slično istraživanje u Njemačkoj pokazalo je da škole u bogatijim regijama imaju znatno bolju infrastrukturu od onih u manje razvijenim područjima (Spitzer, 2023). Na temelju provedenog istraživanja o opremljenosti osnovnih škola Grada Zagreba informacijsko-komunikacijskom tehnologijom i stavovima nastavnika o nastavi Informatike, uočeno je da, iako većina škola posjeduje osnovnu informatičku opremu, postoje razlike u razini tehnološke infrastrukture. Ove razlike mogu utjecati na kvalitetu i učinkovitost nastave Informatike te na razvoj digitalnih kompetencija učenika. Istraživanju se odazvao mali broj ispitanika. Mogući razlog za slab odaziv može biti period provođenja anketnog upitnika koji se poklopio s krajem školske godine. U tom su periodu brojni učitelji preopterećeni zaključivanjem ocjena, provođenjem posljednjih provjera znanja i ostalim poslovima koji moraju biti dovršeni do kraja školske godine. Međutim, anketni upitnik proslijeđen je i početkom rujna, kada učitelji nisu toliko opterećeni školskim obavezama jer tada školska godina tek počinje. Ni tada odaziv ispitanika nije bio značajniji što može ukazati na općenit manjak interesa i motivacije kod predmetnih učitelja Informatike. Manjak motivacije također je vidljiv u odgovorima anketnog upitnika, u pitanjima o brzini interneta, broju pojedinih uređaja ili učestalosti nabavi opreme za izvođenje nastave Informatike, gdje su odgovori u više slučajeva bili nepotpuni ili posve izostali. Takva neupućenost u opremu kojom škola raspolaže može biti direktno povezana s kvalitetom nastave Informatike, odnosno s načinom i metodama njezinog provođenja. Slab odaziv može se pripisati i općenitoj preopterećenosti učitelja Informatike koji osim poslova vezanih uz nastavu Informatike u školama u kojima rade imaju i brojna druga, službena ili neslužbena zaduženja. Česta zaduženja koja obavljaju učitelji Informatike obuhvaćaju IKT podršku, administraciju e-dnevnika i slične poslove vezane uz tehnološka pitanja. Osim toga, učitelji Informatike često moraju biti na raspolaganju kolegama koji se ne snalaze vješto u baratanju tehnologijom te im stoga treba pomoć oko izvršavanja školskih obaveza koje uključuju korištenje IKT-a. Konkretne razloge slabog odaziva možemo okvirno pripisati gore navedenim razlozima. Obzirom na veličinu uzorka rezultati ovog istraživanja mogu poslužiti kao smjernica te je dobiveni uzorak poželjno proširiti dodatnim istraživanjem kako bi rezultati bili relevantniji. Analiza rezultata ankete i razlika u stavovima s obzirom na spol nastavnika nije pokazala statističku značajnost, što je u skladu s očekivanjem. Time je potvrđena prva postavljena hipoteza istraživanja: *Stavovi nastavnika se ne razlikuju statistički značajno s obzirom na spol nastavnika.* Druga hipoteza: *Stavovi nastavnika se statistički značajno razlikuju s obzirom na radno iskustvo nastavnika* opovrgnuta je jer statistička analiza pokazuje da ne postoji statistički značajna razlika u stavovima nastavnika s obzirom na radno iskustvo. Takav rezultat nije u skladu s očekivanjem. Očekivan je kritičniji stav mlađih učitelja prema stanju i dostupnosti opreme te veća sklonost prema češćoj nabavi i obnavljanju opreme. Stavovi nastavnika prema opremljenosti škola također ne pokazuju statistički značajnu razliku, čime je i treća hipoteza: *Stavovi nastavnika se statistički značajno razlikuju s obzirom na razinu opremljenosti škole* opovrgnuta. Takav rezultat također nije očekivan jer se logičnim činilo da će nastavnici koji rade u školama s manje opreme imati snažniju tendenciju prema nabavi nove opreme i negativniji stav prema mogućnostima izvođenja nastave s dostupnom opremom. Istraživanje nije pokazalo jasne razlike u stavovima nastavnika prema opremi koju imaju na raspolaganju. Razlog za to može biti mogućnost prilagodbe nastavnika na postojeće radne uvjete. Konkretno, nastavnici koji s manje opreme uspijevaju ispuniti sve ishode učenja imaju i manju potrebu za nabavom nove opreme. To potvrđuje i odgovor ispitanika na treću tvrdnju gdje se većina ispitanika donekle (46,7%) ili u potpunosti (33,3%) slaže s tvrdnjom da uspijevaju ostvariti sve ishode učenja predviđene kurikulumom uz opremu koju imaju na raspolaganju. Iako se stavovi nastavnika statistički značajno ne razlikuju u okviru radnog iskustva i opremljenosti škola, većina se nastavnika u potpunosti ili donekle slaže s tvrdnjom da bi uz bolju opremu nastava Informatike bila sadržajnija, drukčija ili bolja. Stoga bi bilo za očekivati da više nastavnika smatra kako škola u kojoj rade ne nabavlja dovoljno često opremu, međutim to nije slučaj prema odgovorima ispitanika u ovom upitniku. U skladu s očekivanjima su stavovi nastavnika prema pojedinim uređajima i njihova zastupljenost u nastavi. U nastavi dominira korištenje stolnih ili prijenosnih računala, dok većina nastavnika gotovo nikad ili vrlo rijetko koristi pametne telefone, a nikad pametne satove. Istraživanje je također pokazalo da iako je pojedina oprema dostupna, ona često nije u stanju koje omogućava optimalno izvođenje nastave Informatike. Iako su svi ishodi učenja zadovoljeni, postavlja se pitanje koliko učenici zapravo razvijaju informacijsku i računalnu pismenost. Iako taj aspekt nije bio konkretno predmet ovog istraživanja, pojedine prakse ukazuju da možda u nekim školama to nije na prihvatljivoj razini. Primjerice škole koje imaju računala starija od 10 godina nemaju jednake uvjete za rad kao škole koje imaju računala stara do dvije godine. Također, mnoge škole imaju na raspolaganju tablete, ali ih često ili ne koriste u nastavi zbog njihovih tehničkih ograničenja ili koriste tek za jednostavne zadatke i pristup sadržajima na webu. Ono što ipak ukazuje na pozitivne prakse u pojedinim školama je korištenje zanimljive dodatne opreme poput Microbit pločica ili grafičkih tableta i robota. Učenici koji imaju mogućnost raditi s takvim uređajima imaju daleko bogatiju i sadržajniju nastavu Informatike, a samim time bolje razvijaju i digitalne kompetencije. Međutim, takva razina opremljenosti nije dostupna svim školama, što dodatno ukazuje na digitalnu podjelu koja se javlja u školama na području Grada Zagreba. Iako je 14 od 15 škola uključeno u Carnetov program e-škole, iz rezultata je vidljivo da je razlika u opremljenosti i dalje prisutna. Provedeno istraživanje potvrdilo je početnu pretpostavku neusklađene opremljenosti škola, a utjecaj toga bit će vidljiv u daljnjim istraživanjima, konkretno u onima koja će se odnositi na razvoj digitalnih kompetencija. **Zaključak** Istraživanja provedena u proteklih 10 godina pokazuju pozitivan položaj Hrvatske u odnosu na ostale europske države u okviru nastave Informatike te digitalnog opismenjavanja učenika. Kako bi se nastavila pozitivna praksa i smanjila digitalna podjela, a samim time i omogućilo svakom učeniku stjecanje potrebnih digitalnih vještina za život i rad u suvremenom dobu potrebno je provesti dodatna istraživanja usredotočena na korištenje IKT u nastavi te ulagati u projekte koji za cilj imaju digitalizaciju društva. Ovo istraživanje pokazalo je da su osnovne škole u Zagrebu relativno dobro opremljene, ali postoji prostor za napredak, posebice u ravnomjernoj raspodjeli resursa. Unatoč pokazanoj razlici u razini opremljenosti škola nije utvrđena statistička značajnost u zadovoljstvu nastavnika dostupnom opremom te stavovima o mogućnostima za ostvarivanje obrazovnih ishoda. Dodatno, ovim istraživanjem je pokazana potreba za redovitim i planskim ulaganjem u digitalnu infrastukturu škola te motivaciju i edukaciju nastavnika. Stoga ovo istraživanje može poslužiti kao motivacija za daljnja istraživanja IKT u obrazovanju, kao i nastave Informatike općenito. Iako se IKT javlja u svim aspektima školstva te se koristi u svim predmetima na nekoj razini, njezino korištenje i dalje je najistaknutije u nastavi Informatike. Samim time Informatika kao nastavni predmet dobiva na važnosti jer upravo u sklopu nastavnih sadržaja tog predmeta učenici ostvaruju najviše ishoda učenja vezani za digitalne kompetencije te učenike priprema za izazove digitalnog društva. **Literatura** Beatty , C., & Egan, S. (2020). Screen Time in Early Childhood: A Review of Prevalence, Evidence and Guidelines. 13. 17-31. Dohvaćeno iz https://shorturl.at/PmIam Drucker, P. F. (1993). The Rise of the Knowledge Society. *The Wilson Quarterly, 17*(2), 52-71. Dohvaćeno iz [https://www.jstor.org/stable/40258682](https://www.jstor.org/stable/40258682) Dukić, D., Petrinšak, S., & Pinjušić, P. (2020). ICT in the Primary School: Practice and Attitudes of Informatics Teachers. *Tehnički glasnik, 14*(3), 257-264. Dohvaćeno iz https://doi.org/10.31803/tg-20200403052511 Europska komisija. (2019). *Key Competences for Lifelong Learning.* Luksemburg: Europska unija. Dohvaćeno iz https://data.europa.eu/doi/10.2766/569540 Europska komisija. (2022). *Preporuka Vijeća o ključnim kompetencijama za cjeloživotno učenje*. Preuzeto 15. siječnja 2025. iz Službene Web stranice Europske unije: https://education.ec.europa.eu/hr/focus-topics/improving-quality/key-competences Europska komisija. (2023). *Informatičko obrazovanje u školama u Europi.* Eurydice, Europska izvršna agencija za obrazovanje i kulturu. Luksemburg: Ured za publikacije Europske unije. Dohvaćeno iz [https://data.europa.eu/doi/10.2797/729544](https://data.europa.eu/doi/10.2797/729544). Eurydice. (2025). *Eurydice Hrvatska*. Dohvaćeno iz Eurydice: [https://www.eurydice.hr/hr/sadrzaj/mreza-eurydice/](https://www.eurydice.hr/hr/sadrzaj/mreza-eurydice/) Homen, M., Dumancic, M. (2023). Report on Smart Education in the Republic of Croatia. In: Zhuang, R., *et al.* Smart Education in China and Central & Eastern European Countries. Lecture Notes in Educational Technology. Springer, Singapore. https://doi.org/10.1007/978-981-19-7319-2\_5 Kraner, D. (2022). Prednosti i nedostaci korištenja internetskih medija u odgoju i učenju. *Probuditi kreativnost; Zbornik radova međunarodnog znanstvenoga skupa: Izazovi učenja i poučavanja u kontekstu pandemije i migracija*, 200-216. Dohvaćeno iz [https://ojs.kbf.unist.hr/index.php/proceedings/issue/view/35](https://ojs.kbf.unist.hr/index.php/proceedings/issue/view/35) Lythreatis, S., Kumar Singh, S., & El-Kassar, A.-N. (2022). The digital divide: A review and future research agenda. *Technological Forecasting and Social Change, 175*. Dohvaćeno iz [https://doi.org/10.1016/j.techfore.2021.121359](https://doi.org/10.1016/j.techfore.2021.121359) NCVVO. (2025). *Nacionalni centar za vanjsko vrednovanje obrazovanja*. Dohvaćeno iz Nacionalni centar za vanjsko vrednovanje obrazovanja: https://www.ncvvo.hr/ NCVVO. (2014). *NCVVO Međuarodna istraživanja - ICILS.* Dohvaćeno iz ICILS 2013; Priprema za život u digitalnom dobu: https://www.ncvvo.hr/medunarodna-istrazivanja/icils/ Spitzer, M. (2023). *Digitalna demencija* (Svez. 2). Naklada Ljevak. Valacich, J., & Schneider, C. (2015). *Information Systems Today; Managing in the Digital World.* Prentice Hall Press, United States. Tomljenović, K., & Zovko, V. (2016). The Use of ICT in Teaching Mathematics—A Comparative Analysis of the Success of 7th Grade Primary School Students. Croatian Journal of Education : Hrvatski Časopis Za Odgoj i Obrazovanje, 18(Sp.Ed.2), 215–221. https://doi.org/10.15516/cje.v18i0.2177
[![logo stoo2_1 (no).png](https://hub.ufzg.hr/uploads/images/gallery/2024-09/scaled-1680-/logo-stoo2-1-no.png)](https://hub.ufzg.hr/uploads/images/gallery/2024-09/logo-stoo2-1-no.png) **Teaching (Today for) Tomorrow:** **Bridging the Gap between the Classroom and Reality** 3rd International Scientific and Art Conference Faculty of Teacher Education, University of Zagreb in cooperation with the Croatian Academy of Sciences and Arts
**Analysis of digital resources for the implementation of computer ** **science courses in elementary schools in the City of Zagreb**
##### **Abstract**
Life in a digital age puts many challenges on the educational systems, while the development of the information and communication technologies (ICT) creates a need for development of digital competencies as they become key elements of modern education. Education system in Croatia offers *Informatics* as individual subject in higher and as an optional subject in the lower grades of elementary school. However, computer equipment varies in different schools, which can impact the quality of the teaching. This study aimed to examine ICT equipment available in elementary schools in Zagreb and to analyze teacher's satisfaction with the available equipment. The research was conducted using a questionaire divided into six segments, five of which describe available equipment and the last segment describes teacher's attitudes. The results didn't show statistically significant differences in teacher’s attitudes with regard to gender, work experience and available equipment but did highlight the need for further investment in computer infrastructure in schools. The development of digital skills is recognised on European level as a key area of educational policy. Initiatives such as the *e-Škole project* aim to lower the impact of digital divide and to ensure equal access to technology in schools. This paper analyses available resources, challenges and opportunities of improvement of Informatics subject while emphasising the importance of continuous investment in digital tools and education of teachers.
***Key words:***
computer science teaching, digital divide, elementary school, information and communication technology, school’s digital equipment
# ICT In Primary Education – Students’ Perspective
[![logo stoo2_1 (no).png](https://hub.ufzg.hr/uploads/images/gallery/2024-09/scaled-1680-/logo-stoo2-1-no.png)](https://hub.ufzg.hr/uploads/images/gallery/2024-09/logo-stoo2-1-no.png) **Teaching (Today for) Tomorrow:** **Bridging the Gap between the Classroom and Reality** 3rd International Scientific and Art Conference Faculty of Teacher Education, University of Zagreb in cooperation with the Croatian Academy of Sciences and Arts
#### **Krešimir Pavlina, Ana Pongrac Pavlina, Anita Modrušan ** *Faculty of Humanities and Social Sciences, University of Zagreb* *kpavlina@ffzg.unizg.hr*
**Section - Education for digital transformation****Paper number: 49****Category: Original scientific paper**
##### **Abstract**
ICT (Information and Communication Technology) integration in primary education has revolutionized teaching. Through computers, tablets, and interactive whiteboards, educators create dynamic and immersive learning environments. Students engage with interactive educational software, digital textbooks, and online resources, enhancing comprehension and retention. ICT fosters collaborative learning opportunities, as students collaborate on projects and communicate with peers globally. It cultivates critical thinking, problem-solving, and digital literacy skills essential for success in the 21st century. However, challenges like the digital divide and concerns regarding screen time and digital distractions warrant careful consideration. Despite challenges, ICT empowers educators to deliver innovative and engaging lessons, preparing students to thrive in an increasingly digital society. Effective implementation requires ongoing professional development, robust infrastructure and pedagogical strategies that leverage technology effectively. This study examines primary school students' attitudes toward the use of Information and Communication Technology (ICT) in their education. The survey was conducted in April and May 2023 on 199 students with goal explore their perspectives on ICT's impact on learning experiences. The study explored factors influencing these attitudes, including the effectiveness of digital tools, screen time management, and teacher integration of technology. Results reveal that students appreciate the benefits of ICT, especially digital quizzes, games, and adaptive platforms that enhance engagement. However, preferences vary, with some students favoring traditional methods over digital tools. Many students feel more motivated and confident when tasks involve digital technology, though prolonged screen time and excessive use are less favorable. Overall, students prefer a balanced approach to ICT integration, with moderate and occasional use being most effective. To maximize ICT’s potential, it is crucial to tailor its use to individual needs, ensuring it complements traditional methods.
***Key words:***
*ICT; primary education; students*
**Introduction** The integration of Information and Communication Technology (ICT) into primary education has revolutionized the teaching and learning process, aligning education with the demands of an increasingly digital society. ICT tools, such as interactive whiteboards, digital textbooks, gamified applications, and adaptive learning platforms, offer opportunities to make learning more engaging, interactive, and personalized. These technologies not only support cognitive development but also foster essential 21st-century skills, such as critical thinking, creativity, and digital literacy (Dingli et al. (2018)). However, the successful implementation of ICT in primary education requires addressing significant challenges, including teacher readiness, student attitudes, the digital divide, and the development of infrastructure and supportive policies. ICT tools in primary education are transforming traditional pedagogies by providing diverse and interactive methods for delivering content. Digital platforms and applications offer a range of multimedia resources, enabling students to visualize and interact with complex concepts. For example, Saif et al. (2021) presents how augmented reality (AR) enhances student engagement and comprehension. AR allows students to manipulate virtual models or explore learning content, turning abstract topics into tangible learning experiences. Adaptive educational platforms use artificial intelligence (AI) to tailor content to individual student needs, addressing specific strengths and weaknesses. This customization has been shown to improve learning outcomes and foster inclusivity by supporting students with varying abilities and learning styles (Lara Nieto-Márquez et al. (2020)). In addition, gamified platforms enhance motivation and sustained interest, as students receive real-time feedback and experience a sense of accomplishment. ICT also facilitates collaborative learning. Digital tools enable students to work together on projects and connect with peers across the globe. These interactions encourage teamwork, cross-cultural understanding, and problem-solving. For instance, Kangas et al. (2022) highlighted how the integration of ICT in STEAM (Science, Technology, Engineering, Arts, and Mathematics) projects promote creativity and interdisciplinary thinking, preparing students for the complex challenges of the future. The success of ICT in primary education depends significantly on students' attitudes toward technology. Positive perceptions can enhance engagement, motivation, and academic achievement. Rodriguez-Jimenez et al. (2023) argue that students often view ICT as a valuable addition to their learning experiences, particularly when tools are intuitive and aligned with their interests. Interactive applications and gamification have been particularly effective in maintaining students’ curiosity and enthusiasm. However, not all students embrace ICT seamlessly. Technical challenges, lack of relevance in digital content, and insufficient teacher support can lead to frustration and disengagement (Althubyani (2024)). Ensuring that digital tools are accessible, reliable, and well-integrated into the curriculum is essential for fostering a positive learning environment. Teachers are pivotal to the effective implementation of ICT in primary education. Their preparedness, attitudes, and teaching strategies directly impact how technology is utilized in classrooms. Despite the growing availability of digital tools, many educators feel inadequately trained to integrate ICT into their teaching practices effectively. Althubyani (2024) emphasized the importance of professional development programs in equipping teachers with the technical skills and pedagogical frameworks needed to harness ICT effectively. Furthermore, teacher attitudes toward ICT play a crucial role in its adoption. Educators who view technology as an enabler of innovative teaching are more likely to use it creatively and confidently. Building a culture of collaboration, where teachers share best practices and successes, can enhance their confidence and willingness to experiment with new digital tools. While ICT has the potential to democratize education, socio-economic disparities often hinder its equitable implementation. The digital divide remains a significant barrier, with students from underprivileged backgrounds facing limited access to devices and reliable internet connectivity. Kangas et al. (2022) stressed that this inequality restricts opportunities for many students, exacerbating existing educational disparities. Addressing the digital divide requires multi-faceted approaches, including government initiatives to provide devices and internet access to underserved communities, investment in school infrastructure, and partnerships with technology developers. Schools can also play a crucial role by implementing inclusive ICT programs and ensuring that all students, regardless of their socio-economic background, have opportunities to develop digital skills. The successful integration of ICT in primary education calls for a coordinated approach involving educators, policymakers, and technology developers. Policies should prioritize investments in teacher training, infrastructure, and research to support sustainable ICT adoption. Furthermore, ICT initiatives must align with broader educational goals, such as fostering critical thinking, creativity, and collaboration. Research into emerging technologies like virtual reality (VR), AI, and AR will continue to shape the future of ICT in education. Saif et al. (2021) suggested that these technologies could create even more immersive and engaging learning experiences, further enriching the educational landscape. However, to maximize the potential of ICT, it is essential to address challenges related to access, equity, and teacher readiness. This paper examines students’ attitudes toward ICT in primary education, drawing on recent studies to explore the factors influencing these attitudes and their implications for teaching and learning. By synthesizing evidence from contemporary research, it aims to provide insights into how educators can optimize ICT integration to maximize its benefits while addressing its challenges. Ultimately, understanding and shaping students' attitudes toward ICT will be crucial in preparing them for a rapidly evolving digital world. **Methodology** This study explores the attitudes of primary school students toward the use of ICT in their education. Study was conducted between April and May 2023, the research surveyed 199 students, aiming to gain insights into their perspectives on how ICT impacts their learning experience. By examining students’ experiences, preferences, and the challenges they face, this paper aims to provide a deeper understanding of the role ICT plays in shaping their educational journey and to offer recommendations for optimizing its use in primary education. **Results** Students had to express their agreement with 17 statements about their attitudes about ICT in education on a scale from 1 (completely disagree) to 5 (completely agree). Figure 1. *I think I manage my screen time well* [![image.png](https://hub.ufzg.hr/uploads/images/gallery/2025-05/scaled-1680-/5yiimage.png)](https://hub.ufzg.hr/uploads/images/gallery/2025-05/5yiimage.png) The results presented in Figure 1 reflect a mixed but relatively balanced view on screen time management. While almost half of the students (47.7%) rated the statement positively (Agree or Strongly Agree), a significant number (31.2%) remained neutral, and about 21.1% expressed disagreement or strongly disagreed. This suggests that, while many students feel they manage their screen time well, there is still a notable portion who may either struggle with it or are uncertain about how well they manage it. Figure 2. *I feel great after spending more than an hour in front of the screen* [![image.png](https://hub.ufzg.hr/uploads/images/gallery/2025-05/scaled-1680-/X3Vimage.png)](https://hub.ufzg.hr/uploads/images/gallery/2025-05/X3Vimage.png) The results in Figure 2 show a relatively mixed response to the statement, but with a notable tendency toward positive feelings about extended screen time. While 41.7% of students (combined total of Agree and Strongly Agree) reported feeling good after spending more than an hour in front of a screen, a considerable portion of students (29.7%) disagreed or strongly disagreed, suggesting that many students may not feel great after prolonged screen time. The remaining 28.6% of students felt neutral, suggesting that the effects of screen time might be less impactful or not strongly felt by this group. This variation highlights differing experiences and perceptions about the impact of prolonged screen time. Figure 3 *I understand the material better when the teacher explains it to me with the help of a computer and projector than with the help of a blackboard and chalk* [![image.png](https://hub.ufzg.hr/uploads/images/gallery/2025-05/scaled-1680-/8G3image.png)](https://hub.ufzg.hr/uploads/images/gallery/2025-05/8G3image.png) Figure 3 display results that suggest a divided view on the effectiveness of using a computer and projector for teaching compared to the traditional blackboard and chalk. While 35.2% of students (combined total of Agree and Strongly Agree) feel that digital tools improve their understanding, a same portion of students (34.7%) disagreed (Strongly Disagree and Disagree). The largest group, 30.2%, were neutral, indicating that for many students, the method of instruction might not make a significant difference in their comprehension of the material. These findings reflect a mix of preferences, with some students favoring traditional methods, others preferring digital tools, and many remaining undecided. Figure 4 *I better understand the material we practice with the help of digital quizzes and games than when we solve tasks in a workbook or on worksheets* [![image.png](https://hub.ufzg.hr/uploads/images/gallery/2025-05/scaled-1680-/a51image.png)](https://hub.ufzg.hr/uploads/images/gallery/2025-05/a51image.png) The results in Figure 4 indicate a somewhat mixed response to the use of digital quizzes and games versus traditional workbook or worksheet tasks for understanding the material. While 38.2% of students (combined total of Agree and Strongly Agree) feel that digital tools improve their understanding, a significant portion (31.7%) disagreed or strongly disagreed with this statement. The largest group, 30.2%, remained neutral, suggesting that for many students, there is little difference between the two methods or that they are equally effective. These findings suggest that while digital tools are favored by some, others may still prefer more traditional methods for learning. Figure 5 *My teachers use digital technology often enough in the teaching process* [![image.png](https://hub.ufzg.hr/uploads/images/gallery/2025-05/scaled-1680-/Bnbimage.png)](https://hub.ufzg.hr/uploads/images/gallery/2025-05/Bnbimage.png) The results suggest a generally positive view regarding the use of digital technology in teaching, with 60.3% of students (combined total of Agree and Strongly Agree) believing that digital technology is used often enough in the classroom. However, there is still a portion of students (11.0%) who feel that their teachers use it too infrequently, with 28.6% of students remaining neutral. Overall, the data indicates that while most students feel that digital technology is integrated into the teaching process at an appropriate frequency, there is still room for improvement, particularly for those who feel it is underused. Figure 6 *My teachers overuse digital technology* [![image.png](https://hub.ufzg.hr/uploads/images/gallery/2025-05/scaled-1680-/3XOimage.png)](https://hub.ufzg.hr/uploads/images/gallery/2025-05/3XOimage.png) The results in Figure 6 indicate that the majority of students, 64.8% (combined total of Strongly Disagree and Disagree), do not feel that their teachers overuse digital technology, suggesting that most students find the use of digital tools to be balanced or appropriate. On the other hand, a smaller group of students, 12.0% (combined total of Agree and Strongly Agree), feels that digital technology is overused in the teaching process. The remaining 23.1% were neutral, indicating that they did not have strong opinions on whether digital technology is overused or not. Overall, the findings suggest that, for most students, the use of digital technology in teaching does not seem excessive. Figure 7 *My teachers use digital technology that is appropriate and interesting* [![image.png](https://hub.ufzg.hr/uploads/images/gallery/2025-05/scaled-1680-/tP0image.png)](https://hub.ufzg.hr/uploads/images/gallery/2025-05/tP0image.png) Results in Figure 7 indicate a generally positive perception of the digital technology used by teachers. A significant 65.3% of students (combined total of Agree and Strongly Agree) feel that the technology used is both appropriate and interesting, reflecting a high level of engagement with the tools used in the classroom. However, a smaller portion of students, 12.5% (combined total of Strongly Disagree and Disagree), feel that the technology is either not suitable or not engaging. The 22.1% who rated it neutral may be indifferent or feel that the technology is neither particularly exciting nor ineffective. Overall, the data suggests that the majority of students find the digital tools used in their classrooms to be effective and engaging. Figure 8 *I can study for an hour without using my mobile phone/tablet/computer in order to rest* [![image.png](https://hub.ufzg.hr/uploads/images/gallery/2025-05/scaled-1680-/4Kcimage.png)](https://hub.ufzg.hr/uploads/images/gallery/2025-05/4Kcimage.png) Figure 8 presents results that suggest that most students are able to study for an hour without relying on their mobile phones, tablets, or computers to rest. 59.8% of students (combined total of Agree and Strongly Agree) feel confident in their ability to study without digital distractions. However, there is still a portion of students, 22.7% (combined total of Strongly Disagree and Disagree), who find it challenging to study for an hour without the use of these devices, possibly indicating a reliance on digital tools for breaks or focus. The 17.6% neutral responses suggest that for some students, this may vary depending on the situation. Overall, most students report a strong ability to focus and study without digital interruptions. Figure 9 *Digital devices often distract me while studying* [![image.png](https://hub.ufzg.hr/uploads/images/gallery/2025-05/scaled-1680-/u69image.png)](https://hub.ufzg.hr/uploads/images/gallery/2025-05/u69image.png) The results reveal that a sizable number of students feel that digital devices are a source of distraction while studying. Almost 45.7% of students (combined total of Agree and Strongly Agree) believe that digital devices often interfere with their focus during study sessions. However, a larger portion of students, 30.7% (combined total of Strongly Disagree and Disagree), do not feel that digital devices are a frequent source of distraction. The 23.6% neutral responses suggest that for some students, the impact of digital devices on their focus may vary, depending on the situation. Overall, the data highlights that while many students feel distracted by digital devices, there is also a significant portion who feel that they can study without digital interruptions. Figure 10 *I think we learn more with the use of digital technology* [![image.png](https://hub.ufzg.hr/uploads/images/gallery/2025-05/scaled-1680-/i0Kimage.png)](https://hub.ufzg.hr/uploads/images/gallery/2025-05/i0Kimage.png) The results presented in Figure 10 show a mixed but generally positive view of digital technology's role in learning. While 35.7% of students (combined total of Agree and Strongly Agree) feel that digital technology helps them learn more, a significant 31.2% (combined total of Strongly Disagree and Disagree) do not believe it contributes significantly to their learning. The 33.2% neutral responses suggest that for many students, the impact of digital technology on their learning is either unclear or not strongly felt. Overall, while many students see the value in digital tools for learning, there is also a notable portion who do not feel that these tools make a substantial difference. Figure 11 *I find the teaching in which digital technology is used more interesting* [![image.png](https://hub.ufzg.hr/uploads/images/gallery/2025-05/scaled-1680-/8Dvimage.png)](https://hub.ufzg.hr/uploads/images/gallery/2025-05/8Dvimage.png) The results in Figure 11 suggest a generally positive perception of digital technology in making lessons more interesting. A combined 50.2% of students (Agree and Strongly Agree) feel that lessons incorporating digital technology are more engaging. However, 19.1% of students (Strongly Disagree and Disagree) do not feel that digital tools make the teaching more interesting, indicating that for some, traditional teaching methods might be preferred. The 30.7% neutral responses suggest that a significant portion of students is either indifferent or does not find a noticeable difference between lessons with or without digital technology. Overall, the data highlights a strong tendency toward finding digital technology-enhanced teaching more engaging, although not all students share this view. Figure 12 *Digital technology should be used every school hour* [![image.png](https://hub.ufzg.hr/uploads/images/gallery/2025-05/scaled-1680-/e14image.png)](https://hub.ufzg.hr/uploads/images/gallery/2025-05/e14image.png) The results show a mixed opinion on the idea of using digital technology in every school hour. A significant 45.7% of students (combined total of Strongly Disagree and Disagree) believe that digital technology should not be used on every school hour of certain subject. However, 29.6% of students remain neutral, suggesting that some students might see value in digital technology but do not feel it needs to be always used. Only a smaller portion, 24.7% (combined total of Agree and Strongly Agree), feels that digital technology should be integrated into every school hour. Overall, the data suggests that while many students see the value of digital technology, they do not believe it should be overused or incorporated into every lesson. Figure 13 *Digital technology should be used during complete lesson* [![image.png](https://hub.ufzg.hr/uploads/images/gallery/2025-05/scaled-1680-/L75image.png)](https://hub.ufzg.hr/uploads/images/gallery/2025-05/L75image.png) Results presented in Figure 13 suggest that most students do not feel digital technology should be used during complete lesson. Majority of 63.3% students (combined total of Strongly Disagree and Disagree) believe that digital technology should not be continuously used throughout lesson. A smaller portion, 13.5% (combined total of Agree and Strongly Agree), supports the idea of using digital technology during complete lesson, but this group is relatively small. The 23.1% neutral responses suggest that some students may feel that digital technology could be used at certain times but not necessarily all the time. Overall, the data indicates a clear preference for using digital technology in moderation, rather than consistently throughout complete lesson. Figure 14 *Digital technology should be used occasionally* [![image.png](https://hub.ufzg.hr/uploads/images/gallery/2025-05/scaled-1680-/rUaimage.png)](https://hub.ufzg.hr/uploads/images/gallery/2025-05/rUaimage.png) Figure 14 display results that indicate a strong preference for the occasional use of digital technology. A total of 66.9% of students (combined total of Agree and Strongly Agree) feel that digital technology should be used in moderation, specifically on an occasional basis. On the other hand, only 11.0% of students (combined total of Strongly Disagree and Disagree) disagree with statement that digital technology should be used occasionally. The 22.1% neutral responses suggest that some students may not have strong feelings on the matter, but the overall trend shows that most students prefer a balanced approach, with digital technology used occasionally rather than constantly. Figure 15 *I am happy when at school we get the task of recording an educational video ourselves* [![image.png](https://hub.ufzg.hr/uploads/images/gallery/2025-05/scaled-1680-/j9cimage.png)](https://hub.ufzg.hr/uploads/images/gallery/2025-05/j9cimage.png) The results presented in Figure 15 show that a large portion of students, 57.1% (combined total of Strongly Disagree and Disagree), does not enjoy the task of recording an educational video, with many finding it less appealing. However, a smaller group, 24.3% (combined total of Agree and Strongly Agree), enjoys that type of task. Only 18.7% of students were neutral, indicating that for some students, the activity does not evoke strong feelings either way. Overall, while a minority of students find recording educational videos enjoyable, the majority do not feel particularly happy about this task. Figure 16 *I easily create my own digital content (video, presentation, digital poster, etc.)* [![image.png](https://hub.ufzg.hr/uploads/images/gallery/2025-05/scaled-1680-/y8Iimage.png)](https://hub.ufzg.hr/uploads/images/gallery/2025-05/y8Iimage.png) Figure 16 presents results that indicate that most students feel confident in their ability to create digital content. A combined 69.2% of students (Agree and Strongly Agree) report being able to easily create content like videos, presentations, and digital posters. However, 13.2% (combined total of Strongly Disagree and Disagree) find it difficult to create digital content, indicating some challenges in this area. The 17.7% neutral responses suggest that for some students, the ability to create digital content may vary depending on the task or situation. Overall, the data shows that the majority of students are confident in their ability for digital content creation, but a small group faces difficulties. Figure 17 *I try harder when we get a task in which we need to use digital technology (record a video, make a presentation, digital poster, etc.)* [![image.png](https://hub.ufzg.hr/uploads/images/gallery/2025-05/scaled-1680-/8W2image.png)](https://hub.ufzg.hr/uploads/images/gallery/2025-05/8W2image.png) The results in Figure 17 suggest that a significant number of students feel more motivated to try harder when tasks involve digital technology. A combined 51.0% of students (Agree and Strongly Agree) report that they put in more effort when digital technology is part of the task. However, 18.7% (combined total of Strongly Disagree and Disagree) do not feel more motivated by the use of digital tools. A sizable 30.3% of students were neutral, suggesting that for some students, the type of task or other factors may play a more important role than the use of digital technology. Overall, while many students find digital technology motivating, it does not seem to be a universal motivator for all students. **Conclusions** This study highlights the diverse perspectives of primary school students regarding the integration of ICT in their education. The findings reveal that while students generally appreciate the use of digital technology in the classroom, their preferences, experiences, and challenges vary significantly. Many students recognize the benefits of ICT in making learning more engaging, interactive, and effective, particularly through tools like digital quizzes, games, and adaptive platforms. However, a considerable number of students expressed neutral or mixed feelings about the extent of ICT usage, with some preferring traditional methods for certain aspects of learning. Students largely favor a balanced approach to ICT integration, with occasional use being seen as most effective. While many students feel confident in creating digital content and report increased motivation for tasks involving digital tools, others find prolonged screen time or excessive use of technology to be less desirable. These findings underscore the importance of tailoring ICT use to individual and group needs, ensuring it complement rather than overwhelms traditional teaching methods. To maximize the benefits of ICT in primary education, it is crucial to address key challenges, including minimizing digital distractions, bridging the digital divide, and ensuring that educators are adequately trained to integrate technology effectively. Future research should explore the long-term impacts of ICT on students' learning outcomes and well-being, as well as investigate strategies to optimize its use in fostering critical thinking, creativity, and collaboration. By adopting a thoughtful, inclusive approach, ICT can serve as a powerful tool for enhancing educational experiences and preparing students for the demands of a digital world. **References** Althubyani, A. R. (2024). Digital Competence of Teachers and the Factors Affecting Their Competence Level: A Nationwide Mixed-Methods Study. *Sustainability*, *16*(7), 2796. Dingli, S., Baldacchino, L. (2018). Creativity and digital literacy: skills for the future. Rodriguez-Jimenez, C., de la Cruz-Campos, J. C., Campos-Soto, M. N., & Ramos-Navas-Parejo, M. (2023). Teaching and learning mathematics in primary education: The role of ICT-A systematic review of the literature. *Mathematics*, *11*(2), 272. Kangas, K., Sormunen, K., & Korhonen, T. (2022). Creative learning with technologies in young students’ STEAM education. In *STEM, Robotics, Mobile Apps in Early Childhood and Primary Education: Technology to Promote Teaching and Learning* (pp. 157-179). Singapore: Springer Nature Singapore. Lara Nieto-Marquez, N., Baldominos, A., Cardena Martinez, A., & Perez Nieto, M. A. (2020). An exploratory analysis of the implementation and use of an intelligent platform for learning in primary education. *Applied Sciences*, *10*(3), 983. Saif, A. S., Mahayuddin, Z. R., & Shapi'i, A. (2021). Augmented reality based adaptive and collaborative learning methods for improved primary education towards fourth industrial revolution (IR 4.0). International Journal of Advanced Computer Science and Applications, 12(6). # Teacher’s perspective for didactic-methodological potentials of metaverse
[![logo stoo2_1 (no).png](https://hub.ufzg.hr/uploads/images/gallery/2024-09/scaled-1680-/logo-stoo2-1-no.png)](https://hub.ufzg.hr/uploads/images/gallery/2024-09/logo-stoo2-1-no.png) **Teaching (Today for) Tomorrow:** **Bridging the Gap between the Classroom and Reality** 3rd International Scientific and Art Conference Faculty of Teacher Education, University of Zagreb in cooperation with the Croatian Academy of Sciences and Arts
##### **Jelica Babić, Ljiljana Bujišić, Marija Vorkapić, Sanja Čomić** *University of Belgrade - Faculty of Education, Belgrade, Serbia* *risticjelica.uf.bg@gmail.com*
**Section - Education for digital transformation****Paper number: 50****Category: Original scientific paper**
##### **Abstract**
Education in the 5.0 era requires precise didactic-methodical reflection on using AI driven educational technology to create innovative and stimulating teaching environments. One of the key challenges facing the educational system determines the main research question of this paper, and relates to how to improve the existing teaching models with the latest technological solutions such as metaverse. This study aims to assess the 219 in-service primary school teachers’ perspectives about the metaverse concept and to examine the views on the possible didactic-methodological potential of integrating metaverse in education. Data for this study were collected using an online survey as the primary research instrument, incorporating a 5-point Likert scale for attitudes about one prospective teaching scenario, and then were interpreted using descriptive statistics, the Chi-squared test and ANOVA. The results of research show (still) insufficient knowledge of the term “metaverse”. Also, aspects of the teaching process for which our sample of teachers believes that the metaverse can have a positive impact were highlighted, as well as how three key factors (work experience, time spent on the computer, and grades) influence these attitudes. Based on the observed scenario, participants believe that metaverse could have greater contribution to different aspects such as better evaluation of student work, improvement of existing teaching models etc. Implications for future research may be directed to fully understand the educational benefits of metaverse. It is essential to explore teachers' perspectives across various educational levels, using diverse samples and teaching scenarios, as well as to investigate the process of evaluation and impact on child development.
***Key words:***
artificial intelligence; attitudes; benefits; in-service education; metaverse.
**Introduction** The rapid technological advancements of the 21st century, particularly the growing influence of artificial intelligence (AI), call for a thorough reevaluation of teaching methods and the integration of digital tools to create dynamic and stimulating learning environments. Soon, groundbreaking technologies are set to revolutionize the educational experience, redefining what we currently perceive as innovative. Among these is the metaverse, as a prospective form of immersive reality technology with the potential to establish a completely new framework for education. The term "metaverse" is a compound word formed by "meta" (meaning beyond or transcending) and "verse" (derived from "universe," denoting the whole world), referring to a new virtual universe that exists beyond the real world (Zhang et al., 2022). Today we come to different points of view on the metaverse, from that a metaverse merely be a new term for virtual reality (VR), augmented reality (AR), and mixed reality (MR) to it is much more than that (Park & Kim, 2022 according to Hwang & Chien, 2022). Extended reality (EX) technology, as an umbrella term for VR, AR, and MR, is just one aspect of the metaverse ecosystem but is commonly explored alongside the metaverse because it is an existing set of technologies that offers cues as to what may lie ahead in future metaverse applications, especially given its immersive qualities (UNICEF & DIPLO, 2023). AI generally plays an important role in the metaverse (Hwang & Chien, 2022). Because of this statement, the most comprehensive definition for understanding the concept of the metaverse is a worldwide virtual environment, a type of Web 3.0 tool with the potential to reimagine education with extended reality (XR) is expected to introduce a new and unique way of learning (UNESCO IITE & NetDragon, 2023). As is clear from the findings (Alfaisal et al. 2024; Mystakidis, 2022; Chua & Yu, 2024; UNESCO IITE & NetDragon, 2023) a metaverse: 1) refers to a three-dimensional model of the internet often referred to as Web 3.0., an immersive, virtual world (multiuser platforms) - an interconnected web of social, networked immersive environments that provide a more immersive experience than the internet; 2) interaction occurs between the users and digital artifacts in real-time, irrespective of their location; 3) participants themselves can create an avatar (a configurable digital body - a lifelike manifestation of the user who can look quite different) and users enter the metaverse using their avatars to interact with other avatars and digital objects in virtual space, with the opportunities to shop, play games, work, socialize, and learn; 4) integration of the real as well as physical universe through which the users can imagine various and myriad digital mirrors of the actual world and mirrors that are not present in the actual world for different purposes; 5) technically the system can work without leaving the actual world while maintaining a consistent connection with the virtual world without any time restrictions. The findings (Al-kfairy et al., 2024) reveal that user adoption of the metaverse in educational contexts is influenced by multiple factors at technological, environmental, and individual levels. From a technological perspective, the core of the metaverse is based on artificial intelligence (AI). One of the most significant applications of AI’s simulation capabilities is its ability to make non-player characters (NPCs) behave like real humans in the metaverse. This, in turn, enables learners to interact and collaborate with intelligent NPC tutors, NPC peers, and NPC tutees, as well as other human learners represented as avatars (Hwang & Chien, 2022). Currently, interactive 3D platforms such as Roblox, Minecraft, and Fortnite serve as models for the future development of the metaverse (Chua & Yu, 2024). This opens up a vast range of opportunities for implementing an intelligent metaverse system to support the development of innovative educational paradigms. To unlock the full potential of the metaverse to support different learning scenarios (environmental levels) in educational settings, many meta-analyses have been conducted (Flores-Castañeda et al., 2024; Geng & Su, 2024; Tlili et al., 2022) refer to a metaverse the potential to lead to the improvement of the virtual (online, blended and hybrid) learning as well as experiential, game-based and problem-based learning, collaborative, cooperative, self-directed learning), and emotional (involving the regulation of emotions). Moreover, curriculum gamification in the social virtual world opens new interdisciplinary cooperation that can enrich and differentiate in comparison to current online learning methods (Jovanović & Milosavljević, 2022). Through a carefully crafted combination of the best aspects of technology and essential pedagogical strategies, traditional teaching can be enhanced and transformed into a more engaging and stimulating learning environment. The virtual world enabled by the metaverse can shift the traditional teaching model from a static approach to a dynamic one across diverse learning scenarios, fostering student-centered collaboration by offering learning resources and real-time assessments (Díaz, 2020). The main potential on the individual level lies in the following: an immersive interactive experience for learning without time and space limitations, visualization of risky situations and historical periods, enhancing STEM education, individualization according to the student's pace, inclusive environment, motivation, engagement and playful activities in the teaching process, improving communication and developing creativity, developing skills that require long-term practice, reducing feelings of anxiety and depression, cost reduction, prevention of misconduct and school violence (Hwang & Chien, 2022, Lin et al., 2022, Ristić et al., 2022). On the other hand, the key challenges of adopting the metaverse open new questions about: hyperrealistic experiences, identity formation, limited view of social interaction, health risks and physical safety, exposure to inappropriate content, data security and privacy, increase in digital violence, commercial exploitation and manipulation, parental control, increasing global inequality (UNICEF & DIPLO, 2023). The integration of the metaverse into education will depend on various factors, significantly influencing all critical aspects of the system. To align with the needs of modern learners, schools must evolve, discarding outdated practices that traditional education has proven ineffective. The need for teachers to develop new competencies to effectively utilize and integrate AI technologies into their teaching theory and practice was emphasized (Mandić, 2024). This involves aligning AI tools with learning objectives, designing learning activities based on artificial intelligence, and leveraging AI to support various teaching strategies in the application of modern educational technologies. Moreover, to ensure more effective implementation of the metaverse in the future, it is essential to offer technical support to teachers, encourage training both within and beyond the classroom through synchronous and asynchronous methods, and create a dynamic, interactive, and collaborative virtual platform for students (Tlili et al., 2022). Understanding the changes brought by intelligent metaverse systems will potentially completely alter the methodological approach to teaching that is deeply rooted in the educational systems of many countries. Taking into account the predictions of the US survey that by 2027, Generation Z respondents (born between 1997 and 2012) will spend an average of 4.7 hours per day in metaverse spaces (Aielloto et al., 2022) it is extremely important to consider what kind of future we want for education with the metaverse. The study (Nguyen et al., 2025) explored metaverse literacy in cognitive, affective, behavioral, and ethical learning domains with first-year bachelor’s students enrolled in an undergraduate program. Their findings show that the learning experience with the metaverse significantly alters students' perceptions of the effort required for adoption and improves their metaverse literacy in education. To answer the question of how to organize quality and efficient time spent in the metaverse and according to which criteria, in-service as well as pre-service teachers need to be prepared for the critical evaluation of situations where the metaverse can provide learning like never before and trained to prevent reported challenges and risks of implementing metaverse in future. The top of that, educational stakeholders need to assess the readiness of educational systems for such types of changes. The impact of incorporating AI and the metaverse into education that remains largely unexplored in most research studies (Nguyen et al., 2025; Almeman et al., 2025), but the need is emphasized for academic and industry professionals to recognize the essential need to properly equip students and graduates for the digital age with this kind of technology (Xu & Impagliazzo, 2024). In connection with that, the main research question of this study focused on exploration whether advanced educational technologies like the metaverse can enhance existing teaching models. It examines teachers' familiarity with the metaverse, it's perspectives on potential benefits for education, and the impact of work experience, grade level, and computer usage on their attitudes. According to research in other countries, we will see that teachers generally notice similar, greater, or lesser potentials that have an impact on their attitudes toward the application of metaverse. Somewhat similar to our participants, English as a Foreign Language (EFL) respondents in Turkey believe that the use of metaverse can contribute to the understanding of abstract topics in younger students. Also, results showed that participants have a positive attitude towards integrating the metaverse into English language teaching, and it was perceived through suitability for being innovative, experiential learning and authentic tasks, developing intercultural communicative competence, task flexibility, for young learners, to provide the transition from theory to practice, for gamified teaching, for avatar use, for motivating participants, to expose participants to the target language. Negative attitudes were perceived through problems in teacher and parent preferences, problems in the psychology of users, in setting boundaries, management, and security, in accessing materials/expenses, problems in using with young learners, in using avatars, the bad influence on the mental and physical health of students, as well as the possibility that students mix the virtual world and reality (Kebeci, 2024). In a qualitative study on the metaverse, teachers from different schools, fields of study, and ages also from Turkey believe that the application of the metaverse will have both positive and negative impacts on humanity. Most of them believe that the metaverse is important for education and that it would make knowledge more permanent, high-quality, and efficient, as well as contribute to distance education. Teachers have seen the advantages of the metaverse in terms of active learning, practical work of experiments, and reducing the traditional form of work. They see the disadvantages through the problem of socialization and the possibility of escaping from real life, mixing real and virtual situations, health problems, and dependence on technology. Most teachers would decide to use the metaverse in teaching (Semerci et al., 2024). As mentioned, artificial intelligence (AI) is the key to the development of the metaverse in general. Accordingly, the development of artificial intelligence highlights the need to enhance teachers' digital competencies. If the metaverse evolves to meet educational needs and becomes adapted (both hardware and software) for use in school settings, it will undoubtedly be of great importance for the key digital competencies for AI (ZVKOV, 2023) to include competencies for the use and integration of the metaverse. A little further away from us, respondents from six different parts of the world within the K-12 level of education indicated that teachers' with more years of work experience have less concern about AI, but do not see any more benefit. Interestingly, no evidence has been found that the age of the subjects, the gender identity, the level of education or the subject taught by the teachers influence the perceived advantages or concerns with the application of AI. Teachers' in Brazil, Israel, and Japan see the advantages of using AI in education more than teachers in Norway, Sweden, and America. Interestingly, concerns are more prominent in Israel, Norway, and Sweden than in the USA, Brazil and Japan (Viberg et al., 2024). All this kind of studies, led us to another additional research question whether exist positive or negative views on the metaverse. **Methods ** The research seeks to address the central question: Can the latest technological solutions in educational technology, such as the metaverse, improve existing teaching models? This inquiry focuses on the potential of the metaverse to enhance educational practices and learning outcomes. The research centers on in-service teachers who teach in all grades of Serbian primary school[\[1\]](#_ftn1) from 1st to 8th grade, to assess their awareness of the metaverse concept. Additionally, the study examines their perspectives on the possible didactic-methodological potential of integrating the metaverse into education. The research explored how the integration of IT-developed teaching methods and metaverse—can revolutionize traditional teaching models. To gather data, the instrument used in this study was an online survey. This approach allowed for efficient data collection from a broad sample of 219 in-service teachers', ensuring diverse insights into their understanding and attitudes toward the metaverse. The research utilized a descriptive methodology to analyze the collected data. This method provided a comprehensive overview of teachers' awareness, perceptions, and readiness to adopt the metaverse in their teaching practices, offering valuable insights into the future of educational technology integration. The potentials of the metaverse particularly stood out in teaching implementation and evaluation, as well as in strengthening the holistic perspective of experiential learning. Four research tasks we dealt with in this research is: 1) How familiar are teachers with the terminological definitions of augmented reality, virtual reality, and metaverse? 2) To what extent do teachers think that metaverse can potentially contribute to certain aspects of educational work? 3) What influence do the variables of work experience, grade, and time using the computer have on teachers' attitudes? 4) Are there differences in attitudes, specifically whether some teachers exhibit more positive or negative attitudes? The participants were first provided with a brief description of what the metaverse is, along with a scenario that could represent a prospective model of an educational situation. This prospective teaching scenario is the result of the winning project *The Empowered Teachers for the META future* of the GCD4FE (The Global Competition on Design for Future Education) 2022 and called Ancientcraft. According to Ristić et al. (2022) this is a STEM education scenario designed to promote cultural identity by raising awareness of cultural heritage which enables learners to travel through time to experience different historical periods related to manufacturing (see Figure 1 and Figure 2). The core focus is on problem-solving, hands-on learning, role-playing, and/or game-based learning where students are empowered to develop skills in traditional crafts. Students get hints and timely feedback on how to create a specific Serbian rug called “ćilim” from an NPC avatar as a tutor. For a more objective evaluation of students in the metaverse, a monitoring system constantly operates in the background of this metaverse environment, providing effective statistical reports to in-service teachers' based on Digital Bloom's Taxonomy as evaluation criteria, collecting feedback, and fostering holistic early childhood development. This designed evaluation model can serve as an aid to teachers in decision-making and more objective student assessment. [![image.png](https://hub.ufzg.hr/uploads/images/gallery/2025-05/scaled-1680-/JMGimage.png) ](https://hub.ufzg.hr/uploads/images/gallery/2025-05/JMGimage.png) [![image.png](https://hub.ufzg.hr/uploads/images/gallery/2025-05/scaled-1680-/5NPimage.png)](https://hub.ufzg.hr/uploads/images/gallery/2025-05/5NPimage.png) *Figure 1. Ancientcraft (video) Figure 2. Ancientcraft (immersive interaction between student and avatar) * Upon viewing the video, the participants were instructed to assess their perspectives regarding the didactic-methodological potentials of the metaverse using a Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). The Likert scale is oriented on one side towards a more qualitative approach to teachers' work, while on the other side, it encompasses students' competencies. The potential of the metaverse was significant for improving the work of teachers' and educators are covered through statements such as: to what extent does the metaverse contribute to improving existing teaching models (problem-based, project-based, research-based), better evaluation of student work, and better monitoring of student performance. For students, this is encompassed through all three domains - educational, cognitive, and psychomotor. In this regard, we wanted to investigate the extent to which respondents believe that the metaverse contributes to student motivation, cultural identity, better understanding of content about Serbian tradition, acquisition of procedural knowledge in carpet making, and development of fine motor skills. By applying the Chi-square test, we wanted to examine the relationship between teachers' attitudes about the potentials of the metaverse and factors such as respondents' work experience, computer usage, and the grade they teach, i.e., with independent variables. Also, the application of ANOVA statistical procedure, and tests such as: T-test and Post-hoc test (Tukey HSD, Bonferroni) aimed to examine variations in attitudes towards the metaverse. ** ** **Results and Discussion ** For experimental research, a question was asked, *Can the latest technological solutions in educational technology such as metaverse improve the existing teaching models?* This research aimed to assess the in-services teachers’ awareness (in the wider territory of the city of Belgrade) about the metaverse concept and to examine the views on the possible didactic-methodological potential of integrating metaverse in education. The data collected by the online survey was analyzed using a descriptive research method. Also, in the online survey, we used a conceptual proposal of a prospective teaching scenario with metaverse. Table 1 illustrates the distribution of the research sample by grade and gender. The research sample was 219 in-service primary school teachers (111 teaching grades 1–4, and 108 teaching grades 5–8). In terms of gender, the majority of the sample were females (87.2%), while male teachers accounted for 12.8%. The distribution by grade level was balanced, with 50.7% teaching lower grades (1–4) and 49.3% teaching upper grades (5–8). In addition to the sample's demographic characteristics, we also selected characteristics related to years of work experience and time spent using computers in daily work. The sample structure is shown in Tables 1 and 2. Table 1 . Sample structure by grade and gender
Grade Gender
Male Female N f(%)
1-4 8 103 111 50.7
5-8 20 88 108 49.3
N 28 191 219
f(%) 12.8 87.2 100
** ** Table 2. represents the sample structure based on work experience and the amount of time spent using a computer in daily activities. Work experience was categorized into two groups: less than 10 years and more than 10 years. The majority of teachers (60.3%) had more than 10 years of experience, while 39.7% had less than 10 years of experience (up to 5 years 22.3% (49) and from 5 to 10 years 17,4% (38). Starting from the fact that teachers with more work experience are older, and vice versa - those with less experience are younger, it is an interesting fact that teachers with more teaching experience use computers for more hours per day compared to teachers with less work experience. Regarding time spent using a computer, most teachers (47.9%) reported using a computer for 1–3 hours per day, 32% for more than 3 hours per day, and 19.2% for less than one hour daily. Table 2. Sample structure by work experience and time using a computer
Work experience Time using a computer (hours)
0 0-1 1-3 >3 N f (%)
<10 1 26 40 20 87 39.7
>10 1 16 65 50 132 60.3
N 2 42 105 70 219
F (%) 0.9 19.2 47.9 32 100
To answer the first research task, we examined to what extent, according to the Likert scale of 1-5, in-service teachers' are familiar with terminological definitions of augmented reality, virtual reality, and metaverse. The results are shown in Table 3. Table 3. Terminological definitions of augmented reality, virtual reality, and metaverse
Mean Median Mode
Augmented reality 2.56 3.00 1
Virtual reality 3.61 4.00 5
Metaverse 1.87 1.00 1
As can be seen from Table 3, the teachers' are best acquainted with the term virtual reality, and the least with the term metaverse, which can be seen based on the mean value of the answers. The fact that they are most familiar with the term virtual reality is not so surprising because many meta-analyses emphasize the rapid adoption of immersive VR technologies into teaching on a regular basis, thanks to increasingly accessible and affordable hardware ( Radianti et al., 2020, Hamilton et al., 2021). Shortly, metaverse will be even more relevant in the educational system, and the potential reason for this result can be found in the fact that it is not yet applied in education in Serbia and that teachers in Serbia do not have training on examples of how to use metaverse in education. A similar result of the research is a qualitative study on the metaverse in education that included 57 teachers from different fields in Turkey. However, 25 teachers had not heard of the metaverse at all, while the rest had heard of it from movies, games, and social networks, but none of the respondents had experienced it. The respondents defined the metaverse as a virtual world and reality, adapting to digital life and avatars. By integrating training for the metaverse, they believe they would be more efficient, secure, and future-ready (Semerci et al., 2024). Developing awareness and encouraging teachers' to reflect on and act towards developing their own and their students’ digital competencies is of great importance, considering that teachers have the task of preparing students for the future — through the development of critical thinking, attitudes, focusing on lifelong learning, and similar goals (Mandić, 2024а). Therefore, teachers' must be familiar with these terms, and schools to be equipped so that all those who are involved in education can become familiar with it and implement it in their work with students. Another study highlights that there is still a need for teacher training and awareness of global trends regarding the application of augmented reality in education. The need for AR training is indicated by educators around the world, such as Libya, whose research shows that educators from Libya are not familiar with how they can integrate AR and activities with children, they are not trained for it, and due to a lack of equipment, they did not prepare activities that include AR (Tutkun, 2024). The second research task was to examine to what extent in-service teachers' think that metaverse can potentially contribute to certain aspects of educational work. The results are shown in Table 4. Table 4. Teachers' attitudes on the contribution of metaverse in the teaching process
It contributes to Mean Median Mode
a better understanding of the content of Serbian tradition 3 4.0 4
the acquisition of procedural knowledge about making rugs 3.64 4.0 4
the development of cultural identity 3.74 4.0 4
a realistic experience of the past 3.95 4.0 5
greater motivation for research on Serbian tradition 3.97 4.0 4
to the development of fine motor skills 3.0 3.0 4
a greater degree of individualization 3.48 4.0 4
experiential learning 3.76 4.0 4
better monitoring of student work 3.53 4.0 4
a better evaluation of student work 3.47 4.0 4
the creation of a stimulating learning environment 3.91 4.0 4
the improvement of existing teaching models (problem-based, project-based, research-based) 4.0 4. 4
From Table 4, we can see that in-service teachers' generally have a positive attitude about metaverse contribution to the education process. They believe that metaverse has the best potential for improving existing teaching methods (Mean=4.0) and the least potential for improving students' motor skills (Mean=3.0). It is encouraging that the result shows that in-service teachers' primarily believe that the metaverse can enhance evolving teaching models. We can say that they are sufficiently familiar with the characteristics of innovative models and recognize the potential for their improvement. Taking into account that schools employ educational staff who have worked using traditional methods as well as those trained to apply developmental teaching models, we were interested in whether teaching experience, the grade they teach, and the amount of time they spend on a computer influence teachers’ attitudes. So, the third research task was to examine what influence do the variables of work experience, class and time using the computer have on teachers' attitudes. Results are shown in Table 5. Table 5. Correlation of factors with the attitudes about contribution of metavers.
Work experience Time use of computer Grade
It contributes to χ2 df p χ2 df p χ2 df p
a better understanding of Serbian tradition 8.514 4 .074 6.254 12 .903 6.955 4 .138
the acquisition of procedural knowledge about making rugs 8.417 4 .077 8.799 12 .720 11.118 4 .025
the development of cultural identity 5.332 4 .255 8.747 12 .724 3.126 4 .537
a realistic experience of the past .730 4 .948 7.782 12 .802 6.599 4 .159
greater motivation for research on Serbian tradition 12.075 4 .017 13.964 12 .303 7.921 4 .095
the development of fine motor skills 3.665 4 .453 4.487 12 .973 1.086 4 .896
a greater degree of individualization 5.805 4 .214 5.537 12 .938 8.240 4 .083
experiential learning 8.481 4 .075 7.002 12 .858 10.390 4 .034
better monitoring of student work 8.892 4 .064 9.880 12 .626 6.307 4 .177
a better evaluation of student work 12.385 4 .015 14.903 12 .247 3.304 4 .508
the creation of a stimulating learning environment 10.854 4 .028 8.288 12 .762 4.040 4 .401
the improvement of existing teaching models 10.208 4 .037 8.870 12 .714 4.875 4 .300
(problem-based, project-based, research-based)
The *Chi-square test* showed that work experience has an influence on the attitude that metaverse contributes greater motivation for research on Serbian tradition, better evaluation of student work, creation of a stimulating learning environment, and improvement of existing teaching models (problem-based, project-based, research-based) (p<.05), but time used on the computer has no influence on metaverse contributes (p>.05). On the other hand, the grade influences the attitude that metaverse contributes to the acquisition of procedural knowledge about making rugs and experiential learning(p<.05). Considering that procedural knowledge answers the question of how something works (Miščević Kadijević, 2011), it plays a crucial role in understanding the processes involved in various tasks. The One-way ANOVA statistical procedure is applied to examine variations in attitudes towards the metaverse depending on teachers' work experience (up to 5 years, from 5 to 10 years, more than 10 years) and between lower (1-4) and upper (5-8) grade teachers'. Based on separate one-way ANOVAs, it was determined that there are differences in attitudes, both towards the grade and across different years of experience (Table 6). Table 6. Results of factors with the attitudes about contribution of metavers – ANOVA
**ANOVA**
It contributes to Work Experience Grade
F Sig. F Sig
a better understanding of Serbian tradition 2.648 .073 5.178 .024
the acquisition of procedural knowledge about making rugs 2.919 .056 8.929 .003
the development of cultural identity 1.904 .151 2.437 .120
a realistic experience of the past .428 .653 5.673 .018
greater motivation for research on Serbian tradition 1.564 .212 5.489 .020
the development of fine motor skills .089 .915 .181 .671
a greater degree of individualization .670 .513 4.779 .030
experiential learning 1.315 .271 7.559 .006
better monitoring of student work .824 .440 2.228 .137
a better evaluation of student work .599 .550 1.641 .202
the creation of a stimulating learning environment 3.833 .023 3.241 .073
the improvement of existing teaching models 3.922 .021 3.660 .057
(problem-based, project-based, research-based)
In relation to work experience, there are statistically significant differences in attitudes about the creation of a stimulating learning environment (p = 0.023) and the improvement of existing teaching models (problem-based, project-based, research-based) (p = 0.021). To examine the impact of three factors (up to 5 years, from 5 to 10 years, more than 10 years) on these two selected dependent variables, a post-hoc tests (Tukey HSD and Bonferroni test) were applied (Table 7 and Table 8). Table 7. Post-hoc analysis of variations in attitudes for the creation of a stimulating classroom environment.
95% Confidence Interval
Dependent variable Test WE (I) WE (J) Mean Difference ( I-J) Std. Error Sig. Lower Bound Upper Bound
the creation of a stimulating learning environment Tukey HSD <5 5-10 .312 .260 .455 -.30 .93
>10 .551\* .201 .018 .08 1.03
5-10 <5 -.312 .260 .455 -.93 .30
>10 .239 .222 .529 -.28 .76
>10 <5 -.551\* .201 .018 -1.03 -.08
5-10 -.239 .222 .529 -.76 .28
Bonferroni <5 5-10 .312 .260 .695 -.32 .94
>10 .551\* .201 .020 .07 1.04
5-10 <5 -.312 .260 .695 -.94 .32
>10 .239 .222 .846 -.30 .77
>10 <5 -.551\* .201 .020 -1.04 -.07
5-10 -.239 .222 .846 -.77 .30
\*WE (Work experience)
Regarding the attitude that the metaverse contributes to the creation of a stimulating learning environment, a significant difference was found between the groups of teachers' "up to 5 years of experience" and "more than 10 years of experience" (p = 0.018 for Tukey HSD, p = 0.020 for the Bonferroni test). This difference is positive (Mean difference = 0.551), meaning that more experienced teachers' (with over 10 years of experience) rate the stimulating learning environment more positively compared to those with less experienced teachers' (up to 5 years of experience). Table 8. Post-hoc analysis of variations in attitudes for the improvement of existing teaching models (problem-based, project-based, research based
95% Confidence Interval
Dependent variable Test WE (I) WE (J) Mean Difference ( I-J) Std. Error Sig. Lower Bound Upper Bound
the improvement of existing teaching models (problem-based, project-based, research-based Tukey HSD <5 5-10 .341 .258 .386 -.27 .95
>10 .557\* .200 .016 .08 1.03
5-10 <5 -.341 .258 .386 -.95 .27
>10 .216 .220 .590 -.30 .74
>10 <5 -.557\* .200 .016 -1.03 -.08
5-10 -.216 .220 .590 -.74 .30
Bonferroni <5 5-10 .341 .258 .565 -.28 .96
>10 .557\* .200 .018 .07 1.04
5-10 <5 -.341 .258 .565 -.96 .28
>10 .216 .220 .984 -.32 .75
>10 <5 -.557\* .200 .018 -1.04 -.07
5-10 -.216 .220 .984 -.75 .32
\*WE (Work experience)
Regarding the attitude that the metaverse contributes to the improvement of existing teaching models (problem-based, project-based, research-based), a significant difference was found between the groups: "up to 5 years of experience" and "more than 10 years of experience" (p = 0.016 for Tukey HSD, p = 0.018 for the Bonferroni test). The difference is negative (Mean difference = -0.557), meaning that teachers with more than 10 years of experience have significantly lower average ratings for this attitude compared to less experienced teachers' up to 5 years experience. This means that teachers with up to 5 years of work experience have a more positive attitude compared to teachers with more than 10 years of experience. In relation to grade, in Table 6 are statistically significant differences in attitudes about: a better understanding of Serbian tradition (p = 0.024), the acquisition of procedural knowledge about making rugs (p = 0.003), a realistic experience of the past (p = 0.018), greater motivation for research on Serbian tradition (p = 0.020 a greater degree of individualization (p = 0.030), experiential learning (p = 0.006). Table 9 . T-test for the difference in attitudes based on grade level (lower and upper grades)
It contributes to Grade N Mean Std. Deviation Std. Error Mean
a better understanding of Serbian tradition 1-4 111 4.02 1.120 .106
5-8 108 3.66 1.224 .118
the acquisition of procedural knowledge about making rugs 1-4 111 3.88 1.142 .108
5-8 108 3.38 1.345 .129
the development of cultural identity 1-4 111 3.86 1.197 .114
5-8 108 3.59 1.297 .125
a realistic experience of the past 1-4 111 4.14 1.156 .110
5-8 108 3.74 1.292 .124
greater motivation for research on Serbian tradition 1-4 111 4.16 1.132 .107
5-8 108 3.78 1.292 .124
the development of fine motor skills 1-4 111 3.03 1.449 .138
5-8 108 2.94 1.42 .137
a greater degree of individualization 1-4 111 3.66 1.239 .118
5-8 108 3.28 1.331 .128
experiential learning 1-4 111 3.97 1.217 .116
5-8 108 3.51 1.279 .123
better monitoring of student work 1-4 111 3.64 1.234 .117
5-8 108 3.39 1.252 .120
a better evaluation of student work 1-4 111 3.56 1.263 .120
5-8 108 3.34 1.232 .119
the creation of a stimulating learning environment 1-4 111 4.05 1.163 .110
5-8 108 3.75 1.261 .121
the improvement of existing teaching models 1-4 111 4.13 1.105 .105
5-8 108 3.81 1.298 .125
The t-test analysis in Table 9. showed that lower grade teachers (1-4) generally have higher mean values across all categories when compared to upper grade teachers (5-8), suggesting that they hold a more positive attitude. Specifically, lower grade teachers reported that metaverse could contribute to a better understanding of Serbian tradition (Mean = 4.02) compared to upper grade teachers (Mean = 3.66). They also rated the contribution to acquisition of procedural knowledge about making rugs higher (Lower grade: Mean = 3.88, Upper grade: Mean = 3.38), and they expressed a more favorable view on providing a realistic experience of the past (Lower grade: Mean = 4.14, Upper grade: Mean = 3.74). Furthermore, lower grade teachers regard that metaverse could contribute to greater motivation for research on Serbian tradition (Lower grade: Mean = 4.16, Upper grade: Mean = 3.78) and a higher degree of individualization in teaching (Lower grade: Mean = 3.66, Upper grade: Mean = 3.28). Lastly, they rated contribution to experiential learning more positively (Lower grade: Mean = 3.97, Upper grade: Mean = 3.51). ** ** **Conclusions ** Artificial intelligence has a wide range of possibilities suitable for application in teaching, which can complement a relatively new teaching model—information-development teaching—that is still in the process of being developed and applied. One of the characteristics of this teaching model is the new spatial organization and didactic-methodological equipment, bidirectional communication, and the possibility of discussion, as well as the active role of students, who become researchers constructing knowledge independently (Vilotijević & Mandić, 2016). The metaverse aims to become an integral part of education in the future, allowing students to visualize historical periods and experience immersive learning without time or spatial constraints (UNESCO IITE & NetDragon, 2023). ** **Improving the teaching process should follow global innovations in practice but with caution due to the challenges that artificial intelligence brings. To recognize the advantages of the metaverse, such as immersive student experiences, stimulating environments, and cost-effectiveness, teachers' must develop and refine their digital competencies. Only the purposeful application of the metaverse leads to desired outcomes, such as greater dedication to the educational aspect of students, long-lasting knowledge, preparing students for the future, increased student motivation, and more. The importance of maintaining a balance between nature and artificial intelligence is recognized in addressing the health risks associated with the use of the metaverse, as pointed out by experts, as well as in raising student awareness of the purposeful use of artificial intelligence. A mixed learning environment is flexible and stimulating, supporting diverse learning styles and teaching models while encouraging the use of various applications and web tools (Ristić 2019) making it essential for children and students to continuously develop lifelong learning competencies (Mandić et al., 2024). For this reason, teachers' should be familiar with the characteristics and possibilities of applying the metaverse in working with students, as well as the associated risks. It is essential that, in addition to a theoretical approach, they have the opportunity to implement this in practice. To foster children's well-being in a technology-enhanced educational environment, it is essential to adopt a methodological approach that promotes interdisciplinary collaboration, teamwork, problem-solving, and the cultivation of children's creativity (Matović & Ristić, 2024). Given that the metaverse is still not widely used in our educational system, and the reduced awareness among teachers about terms like virtual reality, augmented reality, and the metaverse, we believe that training should be organized to enable teachers' to be practically equipped to apply these tools with students. The results of the first research task show that, despite awareness of the existence of artificial intelligence, as well as the principles of its application and dissemination, our respondents are still the least familiar with the term metaverse, as well as what it represents (Mean=1.87). The reason for this result may lie in the age structure of our sample, but also in the fact that the application of computer-based learning is still unevenly distributed across the territory of Serbia. To effectively implement the metaverse in the future, teachers' need to be well-acquainted with the characteristics of innovative teaching models, not only in Central Serbia but also in other regions. We consider it important to note that primary school teachers of the Zlatibor district in Serbia are largely familiar with innovative teaching models, and teachers with a higher level of education are more aware of the importance of applying them in the teaching of nature and society, and are most applicable to teachers up to 10 years of age and over 30 years of work (Milenović et al., 2024). Participants in our study believe that metaverse contributes to greater motivation for researching Serbian tradition (Mean=3.84), better evaluation of student work (Mean=3.53), creation of a stimulating learning environment (3.91), and improvement of existing teaching models (problem-based, project-based, research-based) (4.0). The application of this teaching model supported by the metaverse, further strengthens the development of cultural identity, which our respondents also recognize (Mean=3.74). Work experience and grade level have an impact on the formation of attitudes, whereas the amount of time spent using a computer does not exert any influence. Results indicate that more experienced teachers (over 10 years of experience) rate the contribution of the metaverse to a stimulating teaching environment more positively compared to those with less experience (up to 5 years of experience), while teachers with less experience (up to 5 years) express a more positive attitude towards the contribution of the metaverse to the improvement of existing teaching models compared to more experienced teachers. On the other hand, lower grade teachers (1-4) have more positive attitudes across all aspects compared to upper grade teachers (5-8). Artificial intelligence cannot express emotions and cannot replace the teachers' live presence, so we believe that teachers' should be encouraged to explore it further, enabling them to successfully guide students to use artificial intelligence as an enhancement to their ideas. Given that most students are active on social media and follow digital world trends, implications for further research could focus on students as well, comparing their awareness and attitudes about the metaverse with those of teachers, and examining the current readiness of educational stakeholders to implement artificial intelligence in the classroom. Tlili et al. (2022) highlight a significant gap in metaverse research, noting that limited studies focus on early childhood, primary, and secondary education. Furthermore, no research has explored the use of the metaverse in education for students with disabilities, emphasizing the need for developing accessible and inclusive educational environments. The spatial and temporal virtual freedom offered by the metaverse has the potential to enhance inclusiveness and participation for students with disabilities and special needs. Future implications for teacher competence training include the creating of a professional development program for educators focused on the metaverse, aimed at bridging the gap between theoretical concepts and real-world applications. IT corporations should prioritize the educational aspects of the metaverse, while teachers' play a crucial role in the development of educational metaverse environments. Pilot study observations, with specific tasks on how to organize, plan, apply, and evaluate the metaverse in particular educational situations at all levels of education (from kindergarten to faculty as well as professional training courses for some professions) and especially for vulnerable groups will be essential for assessing its effectiveness. **References: ** 1. Aiello, C., Bai, J., Schmidt, J. & Vilchynskyi, Y . (2022). Probing reality and myth in the metaverse. *McKinsey &Company*. 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Ekwueme (Ed.). *Proceedings of the 7th International African Conference on Contempory Scientific Research - Predictors of Deep Learning and Competence Development in Children Aged 5–7 Using Augmented Reality Technology* (pp. 71–79). Libya, Tripoli: ARG 29. UNICEF & DIPLO. (2023). Vosloo, S., Penagos, M., Radunovic, V., Begovic, B. & Psaila, SP. T*he Metaverse, Extended Reality and Children (RAPID ANALYSIS).* Retrieved on 10th January 2025, Retrived on 10th Januar 2025 from [https://www.unicef.org/innocenti/reports/metaverse-extended-reality-and-children](https://www.unicef.org/innocenti/reports/metaverse-extended-reality-and-children) 30. UNESCO IITE & NetDragon. (2023). *E Library for Teachers.* Retrived on 7th November 2024 from [*www.elibrary.iite.unesco.org*](http://www.elibrary.iite.unesco.org) 31. Vilotijević, M. & Mandić, D. (2016). Informatics-developmental teaching in an efficient school \[Informatičko-razvijajuća nastava u efikasnoj školi\]. Beograd: Srpska akademija obrazovanja – Učiteljski fakultet. 32. Viberg, O., Cukurova, M., Feldman-Maggor, Y. et al. (2024). What Explains Teachers’ Trust in AI in Education Across Six Countries? *International Journal of Artificial Intelligence in Education* *34,* (4). [https://doi.org/10.1007/s40593-024-00433-x](https://doi.org/10.1007/s40593-024-00433-x) 33. Xu, X., Impagliazzo, J. (2024). Metaverse Services in Computing and Engineering Education. *Frontiers of Digital Education 1*(2)*.* 132–141. [https://doi.org/10.1007/s44366-024-0004-0](https://doi.org/10.1007/s44366-024-0004-0) 34. Zhang, X., Chen, Y., Hu, L., & Wang, Y. (2022). The metaverse in education: Definition, framework, features, potential applications, challenges, and future research topics. *Frontiers in Psychology, 13*, 1016300.[ ](https://doi.org/10.3389/fpsyg.2022.1016300)[https://doi.org/10.3389/fpsyg.2022.1016300](https://doi.org/10.3389/fpsyg.2022.1016300) 35. 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# Demographic influences on university students' attitudes towards artificial
[![logo stoo2_1 (no).png](https://hub.ufzg.hr/uploads/images/gallery/2024-09/scaled-1680-/logo-stoo2-1-no.png)](https://hub.ufzg.hr/uploads/images/gallery/2024-09/logo-stoo2-1-no.png) **Teaching (Today for) Tomorrow:** **Bridging the Gap between the Classroom and Reality** 3rd International Scientific and Art Conference Faculty of Teacher Education, University of Zagreb in cooperation with the Croatian Academy of Sciences and Arts
#### **Hasmujaj Elona, Andersons Aigars** *University of Shkoder, Albania* elona.hasmujaj@unishk.edu.al
**Section - Education for digital transformation****Paper number: 51****Category: Original scientific paper**
##### **Abstract**
Recent studies conducted with university students show that attitudes towards artificial intelligence (AI) can vary significantly based on demographic variables such as gender, age, education level and field of study. This study aims to understand the attitudes of students at the University of Shkoder, regarding artificial intelligence (AI) and to identify the possible variables that influence these attitudes. The research employs a descriptive research design, according to the quantitative approach. A sample of 170 university students, including 144 females and 26 males, was selected using non-probability sampling due to convenience. The AI attitude scale (AIAS-4) developed by Grassini in 2023, administered online, was used for data collection. The results indicated that female students display a more positive attitude towards AI compared to their male colleagues. Moreover, our research has proven a significant difference in attitudes towards AI among university students specializing in different branches, with Social Work students showing a significantly positive attitude towards AI compared to other branches. The findings of this study suggested that there are no statistically significant differences regarding AI attitudes among students of different age groups. Furthermore, we examined the influence of educational level on AI attitudes and found no significant difference in attitudes at different educational levels among university students. In conclusion, the study at the University of Shkoder reveals that female students hold a more positive attitude towards AI compared to males. Social Work students show notably positive views. Age and educational levels don't significantly impact AI attitudes among university students. To promote diversity, AI education should be tailored to different fields of study, and ongoing research is crucial for understanding evolving attitudes towards AI.
***Key words:***
artificial intelligence; artificial intelligence attitudes; Ai education; demographic variables; students.
**Introduction** Artificial intelligence (AI) has become a transformative force across industries and academic fields, influencing how societies operate and how individuals engage with technology (Russell & Norvig, 2021). As AI technologies continue to advance, understanding the factors that shape attitudes towards AI becomes increasingly important, especially among university students who represent the future workforce and societal leaders. Positive attitudes towards AI may foster greater acceptance and effective utilization of these technologies, while negative attitudes could hinder their adoption and integration (Grassini, 2023). Consequently, exploring the demographic influences on attitudes toward AI provides a pathway for tailoring AI education and policy-making to address diverse needs and perceptions. From personalized learning systems in education to intelligent medical diagnostics and autonomous transportation, AI has proven its potential to improve efficiency, decision-making, and innovation (Brynjolfsson & McAfee, 2017; Russell & Norvig, 2021). However, its rapid development has also sparked debates surrounding ethical concerns, including algorithmic biases, data privacy, and potential job displacement (Tegmark, 2017; Binns, 2018). As AI becomes increasingly ubiquitous, understanding the attitudes and perceptions of university students—the future leaders, workforce, and educators—toward AI is essential to facilitate its acceptance and integration. Attitudes towards AI are not uniform and often vary based on demographic variables such as gender, age, education level, and field of study. Gender differences in technology adoption have been well-documented, with females often exhibiting either higher levels of caution or more positive attitudes towards certain technologies, including AI, compared to their male counterparts (Venkatesh et al., 2003). These differences could be attributed to social, cultural, and experiential factors influencing perceptions of technological utility and risks. Similarly, field of study significantly impacts students’ attitudes toward AI. For instance, students in social sciences or social work may approach AI with a focus on its ethical implications and potential societal benefits, while students in technical disciplines may emphasize its functionality and innovation (Zawacki-Richter et al., 2019). However, the role of other demographic factors, such as age and education level, in shaping attitudes remains less explored and often inconclusive (Wang et al., 2022). Conversely, students in the social sciences or humanities may be more concerned with its ethical, societal, and cultural implications (Holmes et al., 2019; Zawacki-Richter et al., 2019). These disciplinary differences underline the importance of tailored AI education to address varied perspectives and needs. Despite the growing body of literature, the role of other demographic factors, such as age and education level, remains less conclusive. Some studies suggest younger individuals are more open to technological innovations due to higher exposure, while others emphasize the significance of educational exposure over age (Makransky et al., 2020; Wang et al., 2022). In this study, we investigate the attitudes of university students at the University of Shkoder toward AI and explore how demographic factors influence these attitudes. Using a quantitative descriptive research design and the AI Attitude Scale (AIAS-4) developed by Grassini (2023), we examine variations in attitudes across gender, age groups, education levels, and fields of study. By addressing gaps in existing research and identifying demographic patterns, this study contributes to the growing body of knowledge on the sociocultural dynamics of AI acceptance. The findings from this research have practical implications for developing targeted AI education programs and informing institutional strategies to enhance AI literacy. Understanding these attitudes can aid in promoting diversity and inclusivity in AI education and policy-making, ensuring that students from varied academic backgrounds are equipped to engage meaningfully with AI technologies. By examining the existing literature and conducting empirical research, we seek to address the following research question: How do gender, age, education level, and field of study shape students’ attitudes toward AI? **Methodology** *Research Design* This study adopted a descriptive research design within a quantitative research approach to explore university students’ attitudes toward artificial intelligence (AI) and the demographic variables influencing these attitudes. This approach was chosen to systematically measure and analyze students’ perspectives on AI, providing insights into variations across gender, academic discipline, age, and educational level. *Participants and sampling* The study involved 170 university students from the University of Shkoder, where 144 are females (84.7%) and 26 are males (15.3%). Participants were selected through a non-probability sampling method, specifically convenience sampling, while practical for this study, has limitations in terms of generalizability. To mitigate potential biases, efforts were made to include students from diverse academic disciplines, ensuring representation from fields such as social work, psychology, and physical education. The inclusion criteria for participants were as follows: Enrollment as a student at the University of Shkoder during the academic year 2023-2024. Availability and willingness to participate in an online survey. Basic familiarity with digital technologies to ensure valid responses to the online questionnaire. *Instruments* The AI Attitude Scale (AIAS-4), developed by Grassini (2023), served as the primary tool for measuring students' attitudes toward AI. The AIAS-4 is a psychometrically validated instrument specifically designed to assess perceptions of AI across multiple dimensions, including its societal, ethical, and practical implications. The AIAS-4 consists of 20 items measured on a 5-point Likert scale, where responses range from 1 (strongly disagree) to 5 (strongly agree), with a Cronbach's alpha coefficient of .915. The scale evaluates attitudes along the following dimensions: 1. Social utility: perceptions of AI’s potential to address societal challenges. 2. Ethical concerns: concerns regarding the moral implications of AI usage. 3. Practical benefits: views on the efficiency and advantages AI brings to various fields. 4. Personal acceptance: willingness to engage with and trust AI technologies. For this study, the AIAS-4 was administered online through a survey platform. The online format enabled wide accessibility and ease of participation while reducing logistical barriers. Before distribution, the survey was pilot-tested with a small group of students to ensure clarity and reliability of the instrument in the study's context. *Procedure* Participants were invited via email to complete the online survey. The survey link included a brief description of the study’s purpose, an assurance of confidentiality, and an informed consent form. Participation was voluntary, and respondents could withdraw at any point without penalty. The data collection process spanned two weeks, during which reminders were sent to maximize participation. To ensure data quality, incomplete responses were excluded from the final analysis. *Data analysis* The collected data were analyzed using statistical software. Descriptive statistics were used to summarize demographic characteristics and overall attitudes toward AI. Inferential analyses, including t-tests and ANOVA, were performed to examine differences in AI attitudes across demographic groups. The reliability of the AIAS-4 in this sample was assessed using Cronbach's alpha, ensuring the instrument’s internal consistency. *Ethical considerations* The study complied with ethical research standards. Participants were assured of their anonymity and the confidentiality of their responses. No personally identifiable information was collected, and all data were stored securely. **Results** The descriptive analysis of the survey data reveals interesting insights about the characteristics of the respondents. Among the students, 15% were male, while the majority, accounting for 85%, were female. This indicates a significant gender imbalance in the sample. The respondents’ ages were categorized into several groups, each representing a specific range. The largest age group was 20-21 years old, comprising 41.8% of the respondents. Following closely behind was the 18-19 years old group, accounting for 38.2%. The smaller age groups consisted of 22-23 years old (7.1%), 24-25 years old (4.1%), and those above 26 years old (8.8%). These results suggest that the majority of the respondents were in their late teens to early twenties, with a smaller proportion being older than 25. Among the respondents, 29.4% were studying Psychology, while 42.4% were pursuing Social Work and 28.2% were engaged in Physics education. These findings indicate that Social Work was the most prevalent discipline among the respondents. The analysis revealed that the largest proportion of respondents (51.8%) were in their second year of study. The first-year students accounted for 30% of the sample. The subsequent years had smaller percentages, with 10% in the third year, 2.9% in the fourth year, and 5.3% in the fifth year. These findings suggest that the survey primarily captured the perspectives of second-year students, with fewer respondents in higher academic years. Independent samples t-test results show a significant difference in AI attitude scores between females and males. The results revealed that female students demonstrated more positive attitudes toward artificial intelligence (M = 73.16, SD = 11.49) compared to male students (M = 66.28, SD = 9.64), t(167) = 2.823, p < .05. Table 1 *Mean Scores, Standard Deviation, and t-values of female and male students in relation to artificial intelligence.*
**Variable** **Gender** **N** **Mean** **SD** **t (167)** **F** **P**
AI attitudes Female 144 73.16 11.49 2.823 0.321 .005\*
Male 25 66.28 9.64
To explore potential differences in AI attitudes across different age groups, an one-way analysis of variance (ANOVA) was conducted. However, no significant differences in AI attitudes (F(4,165) = 1.145, p > .05) were found across different age groups. Table 2 *Mean, standard deviation, F and P for age variable in attitudes toward artificial intelligence*
** ** **Variables** ** ** **Group** **N** **Mean** ** SD** ** ** **F (4, 165)** ** ** ** P** ** **
AI attitudes 18-19 20-21 22-23 24-25 over 26 65 71 12 7 15 71.83 72.73 72.83 63.42 73.60 11.70 10.75 11.76 18.98 8.71 1.145 .337
The results of differences in artificial intelligence across different years of study indicated that there are no significant differences in AI attitudes across different groups F(4,165) = .811, p > .05. Table 3 *Mean, standard deviation, F and P of years of study in attitudes toward artificial intelligence*
** ** **Variables** ** ** **Group** **N** **Mean** ** SD** ** ** **F (4, 165)** ** ** ** P** ** **
AI attitudes First Second Third Fourth Fifth 51 89 17 5 10 13.88 13.58 13.88 7.00 8.30 13.83 11.86 11.03 9.54 5.77 .811 .520
ANOVA results reveal a statistically significant difference in AI attitude scores between the three fields of study F(2,167) = 3.456, p < .05. The results indicated that students of social work exhibited more positive attitudes toward artificial intelligence (M = 73.76, SD = 11.69) compared to students of psychology (M = 73.14, SD = 9.76) and physics education (M = 68.48, SD = 12.17). Table 4 *Mean, standard deviation, F and P for academic discipline in attitudes toward artificial intelligence*
** ** **Variables** ** ** **Group** ** ** **N** ** ** **Mean** ** SD** ** ** **F (2, 167)** ** ** ** P** ** **
AI attitudes Psychology Social Work Physics Education 50 72 48 73.14 73.76 68.48 9.76 11.69 12.17 3.456 .005
**Discussion** The findings from this study provide valuable insights into the complex relationship between university students' attitudes toward artificial intelligence (AI) and demographic factors such as gender, academic discipline, age, and educational level. One of the most striking results is the significantly more positive attitude of female students toward AI compared to their male counterparts. This finding aligns with research indicating that women often view AI technologies through a lens of social utility and practical benefits (Grassini, 2023). It reflects broader societal trends wherein women, historically underrepresented in technological fields, are increasingly recognizing the potential of AI to address societal and workplace challenges. Initiatives aimed at fostering gender diversity in AI-related disciplines could build on this trend, encouraging more women to pursue AI-focused careers and academic pursuits (Brown & Smith, 2021). The study also highlights that Social Work students exhibit notably positive attitudes toward AI compared to students in fields such as Psychology and Physical Education. This enthusiasm could stem from the practical benefits AI offers to social work practice, including client management systems, predictive analytics for social interventions, and enhanced accessibility of services (Johnson et al., 2022). Social Work students may view AI as a tool to amplify their impact in addressing complex societal issues. This underscores the importance of designing AI curricula that resonate with the specific interests and professional goals of students within particular disciplines. Tailoring AI education to highlight relevant application such as ethical AI use in Psychology or AI-driven performance analytics in Physical Education could enhance engagement and learning outcomes across diverse fields of study. Interestingly, the results showed no significant differences in AI attitudes among students across various age groups. This finding challenges assumptions that younger students, often labeled as “digital natives,” might have more favourable attitudes toward technology. Instead, it suggests that AI-related attitudes are influenced by factors beyond age, such as exposure to AI applications, personal interest, or perceived relevance to one’s field of study (Nguyen & Walker, 2023). Similarly, the lack of significant differences in AI attitudes across educational levels indicates that exposure to AI may be relatively consistent among undergraduate students, regardless of their academic progression. This consistency raises an important question: Are current AI education strategies adequately preparing students for the complexities of the evolving technological landscape, or do they merely provide a superficial introduction to AI concepts? The methodological approach used in this study provides a strong foundation for understanding student attitudes but is not without its limitations. The use of convenience sampling, while pragmatic, limits the generalizability of the findings to broader student populations. Moreover, the online administration of the AI Attitude Scale (AIAS-4) might have introduced a selection bias, favouring participants who are more comfortable engaging with technology. Future research should consider employing more diverse sampling techniques and combining quantitative surveys with qualitative methods, such as interviews or focus groups, to capture a richer understanding of student perspectives (Smith et al., 2020). These findings have significant implications for higher education institutions. The variation in attitudes across academic disciplines highlights the necessity of moving beyond one-size-fits-all approaches to AI education. For instance, Social Work students might benefit from courses emphasizing AI's role in advancing social justice, while Psychology students could explore ethical considerations and cognitive models in AI development. Moreover, the enthusiasm of female students toward AI represents an opportunity to create inclusive and supportive learning environments that encourage their sustained engagement and leadership in AI-related fields (Garcia et al., 2021). Interdisciplinary learning opportunities could further enrich students’ understanding of AI. Collaborative projects involving students from diverse academic backgrounds may foster a broader appreciation of AI's multifaceted applications while addressing potential gaps in knowledge or perspective. Additionally, longitudinal studies tracking changes in student attitudes over time could provide valuable insights into how exposure to AI in academic and professional contexts shapes perceptions and readiness to engage with AI technologies. Finally, this study highlights the importance of continuous research into AI attitudes to ensure that educational practices remain aligned with the evolving needs and expectations of students. As AI continues to permeate every aspect of society, understanding and addressing the factors that influence student attitudes will be critical to preparing the next generation for the opportunities and challenges of an AI-driven world. **Conclusion** This study underscores the significant role of gender and academic discipline in shaping university students' attitudes toward AI, while finding no significant impact of age or educational level. Female students and Social Work majors demonstrate notably positive attitudes toward AI, likely influenced by their perception of AI’s societal relevance and practical applications. The findings highlight the importance of creating tailored and inclusive educational approaches to AI, emphasizing the need for ongoing research to understand the dynamic interplay between demographic factors and AI attitudes.** ** **Recommendations** Develop AI courses that address the unique needs and challenges of individual disciplines. For example, focus on AI’s potential in social justice for Social Work students or its ethical implications for Psychology students. Design programs and workshops that actively encourage female students to engage with AI, emphasizing its relevance to societal and professional contexts. Facilitate opportunities for students from diverse fields to collaborate on AI-related projects, promoting a holistic understanding of AI applications. Conduct longitudinal and mixed-method studies with diverse samples to validate findings and explore additional demographic and contextual variables influencing AI attitudes. Integrate AI education into core curricula across age groups and educational levels to ensure consistent exposure and engagement with AI concepts. Identify and mitigate factors that may deter certain groups from engaging with AI, ensuring equitable access and opportunities for all students. **Acknowledgment** We express our gratitude to the University of Shkoder “Luigj Gurakuqi” for providing financial support for our participation in this scientific conference. ** ** **References** Binns, A. (2018). *Algorithmic bias in AI systems*. Springer. Brown, A., & Smith, K. (2021). *Gender diversity in artificial intelligence: Fostering inclusivity in AI-focused careers*. Technology and Society, 33(1), 56-68. Brynjolfsson, E., & McAfee, A. (2017). *The second machine age: Work, progress, and prosperity in a time of brilliant technologies*. W. W. Norton & Company. Garcia, E., Thompson, J., & Taylor, M. (2021). *Inclusive learning environments in AI education: Engaging female students in technology*. Journal of Educational Technology, 45(2), 102-115. Grassini, S. (2023). *Development and validation of the AI Attitude Scale (AIAS-4): A brief measure of attitude toward Artificial Intelligence*. DOI:[10.31234/osf.io/f8hvy](http://dx.doi.org/10.31234/osf.io/f8hvy). Holmes, B., S. D. Binns, & D. G. Washington. (2019). *The ethical implications of artificial intelligence: A sociocultural perspective*. Technology and Ethics, 15(2), 55-73. Johnson, M., Williams, L., & Harris, T. (2022). *AI in social work practice: Transforming client management and intervention strategies*. Journal of Social Work Technology, 27(3), 123-135. Makransky, G., M. M. Børne, L. T. Jensen, & D. E. Damsgaard. (2020). *The role of education in shaping attitudes toward technology*. Journal of Technology and Education, 8(3), 121-135. Nguyen, T., & Walker, R. (2023). *Age and attitude: Exploring generational differences in AI adoption*. Technology and Education Review, 29(4), 204-215. Russell, S., & Norvig, P. (2021). *Artificial intelligence: A modern approach* (4th ed.). Pearson. Smith, D., O’Reilly, K., & Collins, B. (2020). *Advancing AI research through mixed-methods approaches: Integrating qualitative perspectives into quantitative studies*. Journal of AI Research Methods, 12(1), 58-73. Tegmark, M. (2017). *Life 3.0: Being human in the age of artificial intelligence*. Alfred A. Knopf. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). *User acceptance of information technology: Toward a unified view*. MIS Quarterly, 27(3), 425-478. Wang, J., Chen, X., & Zhang, J. (2022). *Exploring the influence of education on technology adoption: A meta-analysis*. Technology, Learning, and Innovation, 21(4), 175-188. Zawacki-Richter, O., B. H. K. S. Hans, & C. D. J. L. M. (2019). *AI in higher education: Trends, benefits, and ethical considerations*. Journal of Higher Education Research, 47(4), 44-59. # MANJE JE VIŠE: prikaz skraćivanja upitnika o stavovima studenata prema umjetnoj inteligenciji (SATAI)
[![logo stoo2_1 (no).png](https://hub.ufzg.hr/uploads/images/gallery/2024-09/scaled-1680-/logo-stoo2-1-no.png)](https://hub.ufzg.hr/uploads/images/gallery/2024-09/logo-stoo2-1-no.png) **Odgoj danas za sutra:** **Premošćivanje jaza između učionice i realnosti** 3\. međunarodna znanstvena i umjetnička konferencija Učiteljskoga fakulteta Sveučilišta u Zagrebu Suvremene teme u odgoju i obrazovanju – STOO4 u suradnji s Hrvatskom akademijom znanosti i umjetnosti
#### **Marija Sablić, Goran Lapat, Sofija Vrcelj ** *Faculty of Humanities and Social Sciences, J. J. Strossmayer University of Osijek* *marija.sablic10@gmail.com*
**Sekcija - Odgoj i obrazovanje za digitalnu transformaciju****Broj rada: 52****Kategorija: Izvorni znanstveni rad**
##### **Sažetak**
Umjetna inteligencija sve je prisutnija u svim područjima života. Dosadašnja istraživanja uglavnom su bila usmjerena na korištenje umjetne inteligencije (AI) iz perspektive učenika, a gotovo da ne postoje istraživanja o stavovima studenata - budućih nastavnika prema AI. Ovaj rad ima za cilj kreiranje skraćene verzije originalnog upitnika kojom bi se u budućnosti lakše mogli ispitivati stavovi o umjetnoj inteligenciji. Na uzorku je od 276 studenata Filozofskih fakultet u Osijeku i Rijeci, te Učiteljskog fakulteta u Čakovcu provedeno istraživanje u kojemu je korišten Scale Measuring Student Attitudes Toward Artificial Intelligence (SATAI) upitnik. Rezultati ukazuju kako skraćena forma upitnika (12 čestica) pozitivno umjereno korelira sa subskalama u sklopu stavova ispitanika prema umjetnoj inteligenciji u obrazovanju u odnosu na dužu verziju (26 čestica). Zaključak istraživanja govori u prilog valjanosti kraće forme upitnika čija je valjanost ista u odnosu na dulju verziju uz upola manji broj čestica. Sukladno tome, nastavnici ga mogu koristiti u istraživanju stavova vezanih uz nove AI obrazovne metode.
***Ključne riječi:***
obrazovanje; SATAI; umjetna inteligencija; valjanost instrumenta
**Uvod** Informacijska tehnologija postala je neizostavni dio suvremenog društva, prožimajući sve aspekte ljudskog djelovanja, od svakodnevnih osobnih aktivnosti do složenih profesionalnih zadataka (Trisoni i sur., 2023). Njezina sveprisutnost očituje se u kontinuiranoj digitalizaciji procesa, automatizaciji rutinskih zadataka te transformaciji tradicionalnih metoda rada i komunikacije. Ova tehnološka integracija posebno je vidljiva u obrazovnom sektoru, gdje digitalni alati i platforme sve više oblikuju način na koji se znanje prenosi i usvaja. Rastući trend digitalizacije dodatno je ubrzan globalnim događajima poput pandemije COVID-19, što je rezultiralo ubrzanim prihvaćanjem digitalnih rješenja u svim područjima društva. Umjetna inteligencija može se definirati kao polje računalne znanosti koje ima za cilj rješavanje različitih problema kognitivne prirode, poput rješavanja problema ili učenja te postavljanja korisnih smjernica za stvaranje i korištenje inteligentnih računalnih sustava koji oponašaju karakteristične sposobnosti ljudskih bića (Chassignol i sur, 2018., Chen i sur. 2020). Razvoj koncepta umjetne inteligencije seže do 1950. godine kada je Alan Turing izumio Turing-ov test. Sljedeći značajan korak bio je razvoj ELIZE, prvog chatbot računalnog programa 1960-ih. Godine 1977. IBM je razvio Deep Blue, šahovsko računalo koje je kasnije pobijedilo svjetskog šahovskog prvaka. Noviji trendovi uključuju osnivanje Applea 2011. te pokretanje OpenAI-a 2015. godine, koji su osnovali Elon Musk i suradnici (Smith i sur. 2006; Van der Vorst i Jelicic, 2019) sve do 2016. godine kada je na Georgia Institute of Technology u Sjedinjenim Državama grupi studenata predstavljena *Jill Watson*, prva virtualna asistentica u nastavi (Kim i sur. 2020). Eksponencijalni razvoj istraživanja umjetne inteligencije u obrazovnom kontekstu tijekom protekla dva desetljeća afirmirao je ovo područje kao značajnu znanstvenu domenu, pri čemu empirijske studije sustavno evaluiraju potencijal implementacije umjetne inteligencije u razvoju inovativnih pedagoških alata i personaliziranih pristupa učenju. Empirijska studija Zhanga i Dafoea (2019) o percepciji strojnog učenja među općom populacijom generirala je ambivalentne rezultate. Dok su ispitanici identificirali specifične dobrobiti implementacije strojnog učenja, istovremeno su iskazali zabrinutost vezanu uz potencijalne negativne implikacije, uključujući mogućnost štetnih posljedica, depersonalizaciju iskustava, restrikciju autonomije odlučivanja te potencijalnu zamjenu ljudskog faktora u različitim domenama djelovanja. Stopa razvoja AI brzo raste te postoji velik broj aplikacija koje su implementirane i testirane u nastavi (Akgun i Greenhow, 2021). Publikacija Digital Educational Outlook prezentira komparativnu analizu inicijativa zemalja članica OECD-a u kontekstu implementacije generativne umjetne inteligencije u obrazovne sustave, te elaborira skup strateških preporuka za njezinu buduću primjenu u obrazovnom procesu (OECD, 2023. Obrazovanje je doživjelo niz promjena i pod utjecajem je umjetne inteligencije koja sa sobom donosi prilike za transformaciju i prilagodbu načina na koji se odvija proces poučavanja/učenja (Mureșan, 2023). Evaluacija implementacije umjetne inteligencije iz studentske perspektive predstavlja imperativ u znanstvenim istraživanjima, s obzirom na njihovu dualnu ulogu kao primarnih korisnika i potencijalnih sukreatora obrazovnih servisa (Djokic i sur., 2024). Suvremeni trendovi u obrazovnoj tehnologiji manifestiraju se kroz personalizirane i adaptivne sustave učenja koji, generirajući individualizirani sadržaj i formativne povratne informacije temeljene na specifičnim potrebama i preferiranim stilovima učenja pojedinca, značajno utječu na povećanje motivacije za učenje (Kaledio i sur., 2024; Harry, 2023; Mrnjaus i sur., 2023). Za razliku od nastavnika, umjetna inteligencija šalje trenutno konstruktivne povratne informacije omogućujući time da studenti/učenici razumiju svoje snage i sposobnosti (Lai i sur., 2024). Mjerenje stavova prema umjetnoj inteligenciji može biti važan čimbenik u uspjehu ili neuspjehu primjene umjetne inteligencije u obrazovanju. Pozitivni stavovi učenika o učenju mogu poboljšati postignuća u učenju (Alias ​​i sur., 2018; Zhai i sur., 2019) te pomoći kreatorima kurikuluma i učiteljima da optimiziraju nastavu (O’Hara, 2022). To je također povezano i s idejom kako će općeniti stavovi ljudi prema umjetnoj inteligenciji vjerojatno igrati veliku ulogu u njihovom prihvaćanju umjetne inteligencije (Schepman & Rodway, 2020). S obzirom na rastući značaj umjetne inteligencije u obrazovanju, javlja se potreba za redovitim praćenjem stavova studenata i nastavnika prema ovoj tehnologiji (Makridakis, 2017.; Olhede i Wolfe, 2018). Postojeći instrumenti za mjerenje ovih stavova, uključujući SATAI upitnik (Suh i Ahn, 2022), često su dugački i vremenski zahtjevni za primjenu. Ovo predstavlja značajan praktični problem, posebice u kontekstu obrazovnih institucija gdje je vrijeme za provođenje istraživanja često ograničeno. Iako duži upitnici općenito pružaju detaljnije podatke, njihova duljina može negativno utjecati na motivaciju ispitanika i kvalitetu odgovora, te potencijalno smanjiti stopu odaziva u istraživanjima. Dodatno, u dinamičnom području kao što je umjetna inteligencija, potrebno je često ponavljati mjerenja kako bi se pratile promjene u stavovima, što dodatno naglašava potrebu za efikasnijim instrumentom. Skraćena verzija SATAI upitnika zadržava zadovoljavajuće psihometrijske karakteristike originalne verzije, uz značajno kraće vrijeme primjene. **Metodologija** Cilj ovog istraživanja je razvoj i validacija skraćene verzije SATAI upitnika uz očuvanje njegovih ključnih psihometrijskih karakteristika. Na temelju dosadašnjih istraživanja i teorijskih polazišta postavljena su sljedeća istraživačka pitanja: 1\. Može li se razviti skraćena verzija SATAI upitnika koja zadržava psihometrijske karakteristike originalne verzije? 2\. Hoće li skraćena verzija SATAI upitnika pokazati zadovoljavajuću faktorsku strukturu koja odgovara teorijskom modelu stavova prema umjetnoj inteligenciji? 3\. Hoće li rezultati dobiveni skraćenom verzijom SATAI upitnika visoko korelirati s rezultatima dobivenim originalnom verzijom? 4\. U kojoj mjeri će skraćena verzija SATAI upitnika pokazati zadovoljavajuću unutarnju konzistenciju? Razvoj skraćene verzije upitnika motiviran je imperativom kreiranja ekonomičnijeg instrumenta koji bi umanjio vremenske zahtjeve administracije i kognitivno opterećenje ispitanika, istovremeno održavajući psihometrijske karakteristike u vidu valjanosti i pouzdanosti pri evaluaciji stavova prema umjetnoj inteligenciji u obrazovnom kontekstu. **Ispitanici ** Istraživanje čini stratificirani uzorak od 276 studenata (N=276) s tri visokoškolske institucije: Filozofskog fakulteta Sveučilišta u Osijeku, Filozofskog fakulteta Sveučilišta u Rijeci te Učiteljskog fakulteta Sveučilišta u Zagrebu. Uzorak je obuhvaćao studente različitih godina studija koji su dobrovoljno pristupili istraživanju, pri čemu je selekcijski proces bio usmjeren na osiguravanje reprezentativne distribucije ispitanika kroz sve relevantne studijske programe navedenih institucija. Proces prikupljanja podataka realiziran je putem digitalne platforme Google Forms, što je omogućilo učinkovitu diseminaciju instrumenta. Instrument je konstruiran s naglaskom na optimizaciju jasnoće i razumljivosti čestica, usmjerujući se na ključne istraživačke konstrukte. Procedura prikupljanja podataka implementirana je u periodu od travnja do lipnja 2024. godine, uz osiguravanje informiranog pristanka sudionika i garanciju anonimnosti podataka **Instrument i postupak** SATAI upitnik (Stavovi studenata prema umjetnoj inteligenciji) razvijen je na Sungkyunkwan sveučilištu u Južnoj Koreji provjerom pouzdanosti i valjanosti od strane osam doktora znanosti iz područja računalnog obrazovanja na uzorku od 305 studenata. Nastavnici ga mogu koristiti za dijagnosticiranje trenutnih stavova studenata prema AI ili za provjeru učinkovitosti novih AI obrazovnih metoda. Za provođenje istraživanja u Hrvatskoj dobivena je pisana suglasnost autora instrumenta Suhn i Ahn (2022). Originalna SATAI ljestvica (prilog 1) sastoji se od 26 čestica koje se sastoje od tri komponente (kognitivni, afektivni i bihevioralni čimbenici), a svako pitanje se mjeri pomoću Likertove skale od 5 stupnjeva u rasponu od 1 (uopće se ne slažem) do 5 (u potpunosti se slažem). Prijevod SATAI upitnika na hrvatski jezik proveden je prema standardnim postupcima međukulturalne adaptacije mjernih instrumenata. Originalni upitnik najprije su nezavisno prevela dva stručnjaka s izvrsnim poznavanjem engleskog jezika i područja umjetne inteligencije u obrazovanju. Zatim je treći stručnjak usporedio i objedinio ta dva prijevoda. Dobivena verzija prevedena je natrag na engleski jezik od strane profesionalnog prevoditelja koji nije bio upoznat s originalnom verzijom. Povratni prijevod uspoređen je s originalnom verzijom kako bi se utvrdila ekvivalentnost. Nakon toga, panel stručnjaka (n=5) pregledao je prijevod i procijenio njegovu sadržajnu valjanost. Provedeno je i preliminarno testiranje na manjoj skupini studenata (n=20) kako bi se provjerila jasnoća i razumljivost čestica. Finalna verzija upitnika formirana je nakon manjih prilagodbi temeljem povratnih informacija. **Rezultati i rasprava** Sve analize provedene su pomoću JASP 0.18.3. (JASP tim, 2024.). Procijenili smo unutarnju faktorsku strukturu SATAI konfirmatornom faktorskom analizom (CFA) koristeći polikorične korelacijske matrice s Robust Maximum Likelihood (MLR). Kao indekse prilagodbe modela koristili smo: (a) Sattora-Bentler skalirani hi-kvadrat (χ2 ) (Satorra & Bentler, 2001.); (b) korijen srednje kvadratne pogreške aproksimacije (RMSEA; Steiger, 2000.), gdje su vrijednosti < 0,05 prihvaćne kao dobre, 0,05–0,08 kao umjerene; (c) usporedni indeks prilagodbe (CFI) i; Vrijednosti Tucker-Lewisovog indeksa (TLI) između .90 i .95 označavaju prihvatljivo, a vrijednosti iznad .95 dobro odgovaraju (Hu & Bentler, 1999.); i standardizirani korijen srednje kvadratne vrijednosti (SRMR) s < 0,08 kao indikacija dobrog uklapanja (Hu & Bentler, 1999). Tablica 1 Indeks podudaranja originalne i skraćene verzije upitnika
**Hi** **Df** **P** **CFI** **TLI** **RMSEA** **SRMR** **Residualna kovarijanca**
Originalna verzija 812,55 268 <.001 .900 .888 .086 .061 7&8; 2&3; 14&15; 18&19;
Skraćena verzija 133.28 51 <.001 .961 .950 .076 .051 -
Skraćena verzija upitnika sastoji se od upola manjeg broja čestica, njih 12 (prilog 2). Analiza skraćene verzije upitnika (Tablica 1) ukazuje na model koji pokazuje zadovoljavajuće psihometrijske karakteristike, što je u skladu s trendom razvoja kraćih verzija psihologijskih mjernih instrumenata (Whittaker & Worthington, 2016; Stanton i Stanton., 2002). Model demonstrira granično prihvatljive pokazatelje prikladnosti, sugerirajući kako struktura upitnika odgovara teorijskim pretpostavkama (Schepman i Rodway, 2020). Ovakvi nalazi podupiru opravdanost skraćivanja originalnog upitnika, što je u skladu sa suvremenim pristupima razvoju mjernih instrumenata koji naglašavaju ravnotežu između psihometrijske preciznosti i praktične primjenjivosti (Dimson i sur., 2020). Kratka forma upitnika, koja sadrži četiri čestice po skali, predstavlja efikasniji mjerni instrument, što je posebno važno u kontekstu istraživanja stavova prema umjetnoj inteligenciji gdje je vrijeme ispitivanja često ograničeno (Chen i sur., 2021). Važno je naglasiti da nije bilo potrebe za dodavanjem rezidualnih kovarijanci, što prema Browne i Cudeck (1992) ukazuje na čistoću faktorske strukture i dobru diskriminativnu valjanost subskala. Uspoređujući indekse prikladnosti između originalne i skraćene verzije, možemo uočiti značajna poboljšanja u skraćenoj verziji. CFI i TLI indeksi pokazuju vrijednosti iznad preporučene granice od .95 za skraćenu verziju (.961 i .950), dok su za originalnu verziju te vrijednosti niže (.900 i .888). Ovo ukazuje na poboljšanu prikladnost modela nakon skraćivanja. RMSEA vrijednost u skraćenoj verziji (.076) također pokazuje poboljšanje u odnosu na originalnu verziju (.086), približavajući se preporučenoj vrijednosti ispod .08 koja označava prihvatljivu prikladnost (MacCallum i sur., 1996). SRMR indeks u obje verzije zadovoljava kriterij za dobru prikladnost (< .08), s nešto boljom vrijednošću za skraćenu verziju (.051 naspram .061). Analiza modifikacijskih indeksa originalne verzije upitnika pokazala je potrebu za dodavanjem nekoliko rezidualnih kovarijanci (između čestica 7 i 8, 2 i 3, 14 i 15, te 18 i 19) kako bi se poboljšala prikladnost modela. Te kovarijance upućuju na preklapanja u sadržaju pojedinih čestica koja nadilaze njihovo zajedničko zasićenje latentnim faktorima. Nasuprot tome, skraćena verzija ne zahtijeva dodavanje rezidualnih kovarijanci, što sugerira da su odabrane čestice koje reprezentiraju jasnije i distinktivnije aspekte odgovarajućih konstrukata, bez značajnih preklapanja u sadržaju. Prema preporukama Klinea (2015), izostanak potrebe za dodavanjem rezidualnih kovarijanci u modelu predstavlja značajan indikator teorijske čistoće instrumenta i jasne diferencijacije između latentnih faktora. Proces skraćivanja upitnika rezultirao je ne samo praktično primjenjivijim instrumentom, već i statistički čišćom strukturom. Tome je doprinijela pažljiva selekcija čestica koja je uzela u obzir faktorska zasićenja, sadržajnu reprezentativnost i izbjegavanje redundantnih ili preklapajućih indikatora. Ovakav pristup je u skladu s preporukama stručnjaka za razvoj mjernih instrumenata (DeVellis, 2016), koji naglašavaju da kraći upitnici mogu nadmašiti duže verzije u psihometrijskim karakteristikama ako su čestice pomno odabrane prema jasnim kriterijima. Tablica 2 Deskriptivna statistika i pouzdanost originalne i skraćene verzije upitnika
** ** **Originalna** **Skraćena**
** ** **Kognitivna** **Afektivna** **Ponašajna** **Total** **Kognitivna** **Afektivna** **Ponašajna** **Total**
**M** 3.50 3.06 2.843 3.14 3.50 3.01 2.90 3.13
**SD** 0.946 0.817 0.889 0.819 0.946 0.885 0.997 0.788
**McDonald Ω** .902 .899 .942 .959 .912 .822 .872 .919
**Cronbach α** .901 .887 .930 .953 .902 .817 .871 .922
Pouzdanost obje verzije upitnika (Tablica 2) pokazuje visoke vrijednosti, što je u skladu s preporukama za pouzdanost mjernih instrumenata u području stavova (Nunnally & Bernstein, 2017). Činjenica da skraćena verzija zadržava visoku pouzdanost (α > .80 za sve subskale) posebno je značajna jer potvrđuje da redukcija broja čestica nije narušila unutarnju konzistenciju instrumenta. Ovi nalazi su u skladu s istraživanjima koja pokazuju da dobro konstruirani kraći instrumenti mogu održati visoku pouzdanost uz značajno smanjenje opterećenja ispitanika (Krupić i Ručević 2015; Gosling, 2024). Detaljniji pregled deskriptivnih pokazatelja (Tablica 2) otkriva znčajnu konzistentnost između originalne i skraćene verzije upitnika. Aritmetičke sredine ukupnih rezultata gotovo su identične (M = 3.14 za originalnu i M = 3.13 za skraćenu verziju), što ukazuje da obje verzije na sličan način pozicioniraju ispitanike na kontinuumu stavova prema umjetnoj inteligenciji. Ova podudarnost vidljiva je i na razini pojedinačnih subskala, gdje su razlike u aritmetičkim sredinama minimalne (najveća razlika iznosi svega 0.06 za ponašajnu subskalu). Standardne devijacije pokazuju sličan obrazac variranja u obje verzije, s nešto većom varijabilnošću odgovora na ponašajnoj subskali skraćene verzije (SD = 0.997) u usporedbi s originalnom verzijom (SD = 0.889). Ovo blago povećanje varijabilnosti može se smatrati prednošću, jer prema Haynes i suradnicima (1999) veća varijabilnost odgovora može povećati mogućnost detektiranja povezanosti s drugim varijablama i povećati diskriminativnost instrumenta. Ukupna standardna devijacija skraćene verzije (SD = 0.788) vrlo je bliska originalnoj verziji (SD = 0.819), što dodatno potvrđuje da skraćena verzija zadržava sličan obrazac varijabilnosti odgovora. Za procjenu pouzdanosti korištena su dva koeficijenta – McDonald's omega (ω) i Cronbach's alpha (α), što je u skladu sa suvremenim preporukama za izvještavanje o pouzdanosti (Dunn i sur., 2014). McDonald's omega smatra se preciznijom mjerom pouzdanosti kada je narušena pretpostavka o tau-ekvivalentnosti, što je često slučaj u psihologijskim mjernim instrumentima (Zinbarg i sur., 2005). Vrijednosti oba koeficijenta pokazuju izvrsnu pouzdanost za obje verzije upitnika, s nešto nižim, ali i dalje visoko zadovoljavajućim vrijednostima za afektivnu i ponašajnu subskalu skraćene verzije. Posebno je značajno da kognitivna subskala skraćene verzije pokazuje blago povećanje pouzdanosti (ω = .912) u usporedbi s originalnom verzijom (ω = .902), unatoč značajnom smanjenju broja čestica. Ovaj pomalo iznenađujući nalaz može se objasniti povećanom homogenošću konstrukta nakon uklanjanja čestica koje su potencijalno mjerile druge aspekte kognitivne komponente stava. Kako navode Boyle (1991) i Smith i McCarthy (1995), eliminacija čestica koje uvode heterogenost u konstrukt može rezultirati povećanjem, a ne smanjenjem pouzdanosti, što se pokazalo i u našem slučaju. Razmatrajući sve navedene pokazatelje, možemo zaključiti da skraćena verzija SATAI upitnika demonstrira izuzetno zadovoljavajuće psihometrijske karakteristike, zadržavajući visoku pouzdanost uz gotovo identične obrasce prosječnih vrijednosti i varijabilnosti odgovora kao originalna verzija. Ovaj nalaz ima značajne praktične implikacije jer sugerira da se skraćena verzija može koristiti kao ekvivalentna zamjena za originalnu verziju, uz značajne uštede u vremenu primjene i smanjenje opterećenja ispitanika. Tablica 3 Korelacije između subskala i ukupnih rezultata originalne (O) i skraćene (S) verzije SATAI upitnika
**Originalna** **Skraćena**
**Originalna** 1 2 3 4 5 6 7
1\. Total\_O
2\. Kognitivna\_O\_S .880\*\*
3\. Afekivna\_O .908\*\* .698\*\*
4\. Ponašajna O .890\*\* .635\*\* .753\*\*
**Skraćena**
5\. Total\_S **.987\*\*** .866\*\* .893\*\* .885\*\*
6\. Kognitivna\_S .880\*\* **1.000\*\*** .698\*\* .635\*\* .866\*\*
7\. Afektivna\_S .862\*\* .667\*\* **.957\*\*** .705\*\* .877\*\* .667\*\*
8\. Ponašajnal\_S .834\*\* .593\*\* .689\*\* **.953\*\*** .865\*\* .593\*\* .642\*\*
- p < .05; \*\* - p < .01 Korelacija između ukupnih rezultata (Tablica 3) originalne i skraćene verzije je iznimno visoka (r = .987), što prema Cohen et al. (2018) predstavlja izrazito snažnu povezanost i značajno prelazi preporučene minimalne vrijednosti za paralelne forme instrumenta (r > .70). Izraženo koeficijentom determinacije (r² = .974), to znači da skraćena verzija objašnjava 97.4% varijance originalne verzije upitnika, što sugerira gotovo potpunu ekvivalentnost u mjerenju istog konstrukta. Iznimno visoke korelacije između odgovarajućih subskala originalne i skraćene verzije instrumenta (sve iznad .95) značajno nadilaze uobičajene vrijednosti dokumentirane u literaturi o psihometrijskoj validaciji skraćenih mjernih instrumenata (Smith i sur., 2006). Najsnažnija povezanost utvrđena je za kognitivnu subskalu (r = 1.00), što je izuzetno rijedak nalaz koji sugerira da četiri odabrane čestice u skraćenoj verziji savršeno reprezentiraju konstrukt mjeren s devet čestica originalne verzije. Korelacije za afektivnu (r = .957) i ponašajnu (r = .953) subskalu također su izuzetno visoke, potvrđujući da skraćene verzije ovih subskala vrlo precizno zahvaćaju iste konstrukte kao i originalne verzije. Interkorelacije između različitih subskala (npr. korelacija između kognitivne i afektivne subskale: r = .667 za skraćenu verziju; r = .698 za originalnu verziju) vrlo su slične u obje verzije upitnika, što dodatno potvrđuje da skraćena verzija replicira ne samo pojedinačne konstrukte, već i njihove međusobne odnose. Ovi nalazi imaju značajne implikacije za validnost konstrukta jer potvrđuju da skraćivanje upitnika nije narušilo teorijski utemeljene odnose između različitih aspekata stavova prema umjetnoj inteligenciji. Slika 1. Strukturalni model upitnika \*SO-faktor višeg reda Strukturalni model (Slika 1) potvrđuje hijerarhijsku prirodu konstrukta, što je u skladu s teorijskim postavkama o stavovima prema umjetnoj inteligenciji (Kim i sur., 2022). Visoka faktorska opterećenja na faktoru drugog reda podržavaju postojanje općenitijeg konstrukta koji objašnjava značajan dio varijance u specifičnijim dimenzijama, što je posebno izraženo za afektivnu komponentu. Ovakva struktura podudara se s nalazima sličnih istraživanja u području stavova prema umjetnoj inteligenciji (Zhang i Aslan, 2021; Archibald i sur., 2023). Za razliku od dosadašnjih istraživanja koja su se oslanjala prvenstveno na kvalitativne metode i pitanja otvorenog tipa za procjenu stavova prema AI u obrazovanju (Kim & Kim, 2019; Park & Shin, 2017), SATAI predstavlja učinkovit kvantitativan pristup mjerenju ovog konstrukta. Ova metodološka inovacija omogućuje precizniju procjenu i lakšu usporedbu rezultata različitih obrazovnih konteksta (Bostrom i sur 2024; Wang i sur. 2022). Rezultati pružaju snažnu empirijsku podršku za korištenje skraćene verzije SATAI upitnika, demonstrirajući da ona uspješno zadržava psihometrijske karakteristike originalne verzije uz dodatne praktične prednosti. Ovo je posebno značajno u kontekstu sve većeg interesa za istraživanje stavova prema umjetnoj inteligenciji u obrazovanju (Martinez, 2022) i potrebe za efikasnim i pouzdanim mjernim instrumentima u ovom području. **Prema zaključku** Cilj ovog istraživanja bio je razviti i validirati skraćenu verziju upitnika o stavovima studenata prema umjetnoj inteligenciji (SATAI). Rezultati pružaju snažnu empirijsku podršku za učinkovitost skraćenog SATAI upitnika, demonstrirajući kako on zadržava psihometrijske karakteristike originalne verzije uz značajne praktične prednosti. Skraćeni SATAI nudi nekoliko praktičnih prednosti: smanjeno opterećenje i umor ispitanika, potencijalno povećane stope odgovora, veću učinkovitost u prikupljanju i analizi podataka te održan psihometrijski integritet s upola manje čestica. Ove prednosti čine skraćeni SATAI posebno vrijednim za istraživače i nastavnike koji žele učinkovito procijeniti stavove studenata prema umjetnoj inteligenciji u obrazovnim kontekstima. Zaključno, ovo istraživanje pruža dokaze za valjanost i pouzdanost skraćenog SATAI upitnika. Njegova sposobnost da zahvati iste konstrukte kao i originalna verzija s manje čestica predstavlja značajan metodološki napredak u području. Preporučujemo usvajanje ove skraćene verzije za buduća istraživanja i praktične primjene u obrazovnim okruženjima, jer ona nudi učinkovitiji, a jednako efektivan alat za mjerenje stavova studenata prema umjetnoj inteligenciji u obrazovanju. Stavove prema umjetnoj inteligenciji potrebno je redovito mjeriti s obzirom na brzi razvoj ovih tehnologija i njihov duboki utjecaj na društvo. Podaci o prihvaćanju umjetne inteligencije od strane studenata mogu informirati nastavnike o načinima na koje bi možda trebalo upravljati uvođenjem umjetne inteligencije. Naša nova skraćena verzija stavova studenata prema umjetnoj inteligenciji koristan je alat koji pomaže u postizanju ovih ciljeva. SATAI može pomoći nastavnicima da objektivno mjere stavove studenata prema umjetnoj inteligenciji, omogućuje nastavnicima brzu i pouzdanu procjenu studentskih stavova te olakšava longitudinalno praćenje promjena u stavovima. Rezultati se mogu koristiti za dizajniranje, modificiranje, primjenu i prilagodbu obrazovnih programa kako bi zadovoljili potrebe studenata i nastavnika. **Ograničenja i smjernice za buduća istraživanja** Unatoč značajnim doprinosima, istraživanje ima nekoliko ograničenja koja otvaraju prostor za buduća istraživanja: usredotočenost na kvantitativnom mjerenju stavova bez konceptualnog razumijevanja AI, ograničena generalizacija zbog specifičnosti uzorka, potreba za dodatnom validacijom u različitim kulturološkim kontekstima. Buduća istraživanja trebalo bi usmjeriti na razvoj komplementarnih instrumenata za mjerenje konceptualizacije AI, proširenje validacije na različite studentske populacije i obrazovne kontekste, longitudinalna istraživanja stabilnosti stavova prema AI te integraciju kvalitativnih metoda za dublje razumijevanje formiranja stavova. **Literatura** Ahuja, V., Nair, L. V. (2021). 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[![logo stoo2_1 (no).png](https://hub.ufzg.hr/uploads/images/gallery/2024-09/scaled-1680-/logo-stoo2-1-no.png)](https://hub.ufzg.hr/uploads/images/gallery/2024-09/logo-stoo2-1-no.png) **Teaching (Today for) Tomorrow:** **Bridging the Gap between the Classroom and Reality** 3rd International Scientific and Art Conference Faculty of Teacher Education, University of Zagreb in cooperation with the Croatian Academy of Sciences and Arts
##### **LESS IS MORE: a review of shortening the questionnaire on students' attitudes towards artificial intelligence (SATAI)**
##### **Abstract**
Artificial intelligence is increasingly present in all areas of life. Previous research has mostly focused on using artificial intelligence (AI) from the perspective of students, and there is almost no research on the attitudes of students - future teachers towards AI. This work aims to create a shortened version of the original questionnaire, which could be used to more easily examine attitudes about artificial intelligence in the future. A survey was conducted on a sample of 276 students of the Faculties of Philosophy in Osijek and Rijeka, and the Faculty of Teacher Education in Čakovec, in which the Scale Measuring Student Attitudes Toward Artificial Intelligence (SATAI) questionnaire was used. The results indicate that the shortened form of the questionnaire (12 items) correlates positively and moderately with the subscales within the respondents' attitudes towards artificial intelligence in education compared to the longer version (26 items). The conclusion of the research speaks in favor of the validity of the shorter form of the questionnaire, the validity of which is the same compared to the longer version with half the number of particles. Accordingly, teachers can use it to explore attitudes about new AI educational methods.
***Key words:***
artificial intelligence; education; instrument validity; SATAI
# Digital Education: Education today for tomorrow
[![logo stoo2_1 (no).png](https://hub.ufzg.hr/uploads/images/gallery/2024-09/scaled-1680-/logo-stoo2-1-no.png)](https://hub.ufzg.hr/uploads/images/gallery/2024-09/logo-stoo2-1-no.png) **Teaching (Today for) Tomorrow:** **Bridging the Gap between the Classroom and Reality** 3rd International Scientific and Art Conference Faculty of Teacher Education, University of Zagreb in cooperation with the Croatian Academy of Sciences and Arts
#### **Sandra Sovilj-Nikić, Nikolina Katić, Bojana Mihl** *Faculty of Education, University of Novi Sad, Serbia* *sandrasn@eunet.rs*
**Section - Education for digital transformation****Paper number: 53****Category: Professional paper**
##### **Abstract**
Following modern trends and listening to the needs of education, economy and society, a new study program for undergraduate and master's studies at the Faculty of Education in Sombor of the University of Novi Sad entitled Digital Education was accredited. The goal of this study program is to train an educational profile that will be professional help and support for teachers in the application of information-communication technologies (ICT) during the teaching. Having in mind that this profile possesses the necessary knowledge in the field of digital technologies on the one hand and all the necessary pedagogical, psychological and methodical knowledge on the other hand, the Digital Education study program meets the challenges of modern education. The aim of the research in this paper is to examine and determine the degree of digital competence of teachers and the level of capability for independent use of ICT, as well as to determine their attitudes toward the introduction of a professional associate into educational practice, who would be a help and support to teachers in applying of ICT. In the research carried out in primary and secondary schools in the territory of Vojvodina, which is an autonomous region within the Republic of Serbia, a survey method and an anonymous survey questionnaire were used. The questionnaire was filled out by 240 teacher examinees. During the research, a modified Likert scale with multiple choice questions was used. The results of the research show that the majority of teachers have a satisfactory level of digital competence that they have acquired through some form of informal education and that more than 90% examinees use modern technologies for the purpose of preparing lessons and teaching. However, the majority of examinees believe that the introduction of an expert associate in the application of ICT would be extremely helpful for teaching. However, the majority of examinees believe that the introduction of an expert associate in the application of ICT would be extremely helpful for higher quality teaching, which implies the use of digital technologies in full capacity. Also, the results of the research indicate the fact that the examinees are not familiar with the accreditation of the Digital Education study program, as well as that they support the accreditation and consider it useful. Based on the results of the research, it can be concluded that in educational practice there is a real need for accreditation of the Digital Education study program, as well as that it is necessary to work on the promotion of this study program.
***Key words:***
digital technologies, information-communication technologies, learning, study program, teaching
**Introduction** Today, in the era of science and technology rapid development, when artificial intelligence methods are used in many scientific disciplines, among other things, for the development and improvement of speech technologies that enable communication between humans and computers through speech (Sovilj-Nikić, S. et al., 2014; Sovilj-Nikić, S. et al.,2018), information and communication technologies (ICT) have become indispensable in all segments of modern society, including education. Modern education requires a new model of active learning that involves the use of digital resources of the modern world. The need for education based on the application of digital technologies is increasingly emphasized. However, the integration of digital technologies into curricula is a complex process influenced by a number of factors (Balanskat et al., 2006). The use of digital technologies in the teaching process has been adopted in the strategies of many European countries (Novković Cvetković & Belousova, 2018). In order to adapt to modern changes, teachers need to possess a variety of digital competencies (Nikolić et al., 2020). The use of digital media in education is the path to digital literacy of teachers, which is essential for raising the quality of education in the modern world. The digitalization of education is a process that requires institutional action by all levels of management in the education system (Džigurski et al., 2013). Digitalization in education changes the place and role of teachers and textbooks in the teaching process, where they are no longer the main source of knowledge. Modern technology provides students with diverse learning situations, individualization of learning, affects multiple senses and provides higher quality final outcomes. Teaching should be organized in a way that creates such pedagogical situations that engage the complete personality of the student, his/her mental, affective and cognitive capacities. Effectively organized teaching that includes the use of digital media allows the student to be the focus of the cognitive process in all phases of the lesson. In this way, the student is no longer the object of the teacher's action (Nikolić et al., 2020). Teachers need to be digitally literate in order to apply modern technologies in education. Therefore, it is inevitable that teachers are constantly trained in the application of modern educational technology, as well as intensively improve their IT skills (Bjekić et al., 2018). Teachers need to be systematically motivated to supplement their knowledge in order to be able to competently respond to the demands of the profession. Many education systems encounter a lack of qualified teachers for the transfer of IT knowledge, contributing to a major bottleneck in the spread of ICT in the educational process. A well-prepared and educated teacher is the most important component for the successful implementation of teaching (Chetty et al., 2014). Generally speaking, there are major deficiencies in IT skills among teachers. For example, in a survey of elementary school teachers in the United States, only 10% responded affirmatively that they understood the concept of computational thinking (Campbell & Heller, 2019). According to a study conducted by Google (2016), 75% of teachers in the United States misinterpreted “creating documents or presentations on a computer” as indicating a poor understanding of computer literacy. Other studies, surveys, and interviews have found that teachers in India, Saudi Arabia, the United Kingdom, and Turkey have low self-reported confidence in their understanding of modern technology and its applications in education (Raman et al., 2015). To address these challenges, many school systems have introduced continuing professional development, postgraduate certification programs, and ICT credentials that provide additional training and education for teachers (Heintz et al., 2016). This additional training uses the existing teacher workforce to meet the needs for digital competences, rather than recruiting specialized experts from outside the school system. For example, the British Information Society has created 10 regional university centers to conduct training activities, including lectures and meetings to facilitate collaboration and easier application of ICT in education. The Strategy for the Development of Teaching in Serbia emphasizes the importance of digital technologies for the overall development of the education system. Some of the recommendations that can be found are related to the promotion of the pedagogical use of ICT in order to encourage innovation in teaching and achieve high educational standards. It is recommended that teachers also possess ICT, digital and media literacy, as well as knowledge of modern concepts, methods and tools related to the use of ICT in the teaching process (Novković Cvetković & Belousova, 2018). The results of studies on the application of digital and online learning in vocational education in Serbia have particularly emphasized the need for improving teacher competencies that are necessary for the development of digital teaching materials (Džigurski et al., 2013). **Digital education** A new study program for undergraduate and master's studies entitled Digital Education has been accredited at the Faculty of Education of the University of Novi Sad for the 2023/24 academic year (Faculty of Education, 2023). The goal of this study program is to train an educational profile that will provide professional assistance and support to teachers in primary and secondary schools in the application of information and communication technologies during the teaching. Through this study program, students will acquire the necessary professional knowledge in the fields of digital technologies as well as pedagogy, psychology and didactics. They will be able to create and use interactive, dynamic and multimedia educational content based on web platforms such as multimedia web presentations, educational computer games, posting teaching content on school websites, etc. This study program also enables them to master modern teaching methods and technologies, as well as the basics of educational sciences, and to apply the acquired knowledge in practice. While attending the Digital Education study program in basic studies, students will have the opportunity to acquire knowledge in the field of educational sciences, informatics and computing, programming, algorithmic thinking, mobile technologies, application of ICT in education, and multimedia technologies through mandatory and a large number of elective courses. Upon completion of the study program, the student will possess the necessary practical knowledge in the field of digital technologies and informatics, which allows him/her to create and use educational digital materials for various subjects in accordance with current pedagogical practice and modern didactic recommendations. The student is also able to independently decide on the choice of appropriate digital teaching aids, as well as to develop and use personalized educational software. The practical part of education is very important within this study program. Pedagogical practice is mandatory during all four years of basic studies, and also in both semesters of master's studies. In cooperation with experienced mentors in primary and secondary schools, students will gain practical experience in working with students, with constant support from faculty teachers in order to develop their skills as future teaching assistants. The implementation of tasks includes the creation of multimedia presentations and their posting on the school website, as well as the creation of educational digital materials, which include quizzes, crosswords, associations, puzzles, etc. for the needs of various teaching subjects. The Master of Academic Studies Digital Education study program is designed to respect previously acquired knowledge and skills, and further build on theoretical and practical knowledge acquired in basic academic studies through lectures, practical teaching in the form of laboratory and experimental exercises, as well as pedagogical practice. The program involves education in accordance with the latest scientific achievements in the field of scientific research methodology, application of artificial intelligence technologies in education, creation of web content and methods of teaching computer science. Practical teaching is a very important part of the study program. Pedagogical practice is implemented in cooperation with educational institutions that provide students with the opportunity to apply acquired knowledge in real situations and develop practical skills necessary for future work. The goal is to improve the acquired practical knowledge through independent research work that is reflected in the application of new technologies in practice, monitoring their effects and achieved results with a special focus on gifted students on the one hand and students with special needs and students from marginalized social groups on the other hand, and to use the acquired knowledge in further research work. When implementing research tasks, the student applies the knowledge acquired from the Methodology of scientific research work. The implementation of tasks includes studying the literature in the field of applying new technologies in education and designing specific research tasks that will be implemented during the practice in school with a special focus on gifted students on the one hand and students with special needs on the other hand. Upon completion of the Master's degree program, the student will have advanced academic and/or professional knowledge related to theories, principles, and processes in the field of computer science teaching, as well as the application of new technologies in education. The student will also be able to analyze and evaluate different concepts, models, and principles of theory and practice, improving existing practice, and will demonstrate a positive attitude toward the importance of lifelong learning in personal and professional development. Having in mind that this profile possesses the necessary knowledge in the field of digital technologies on the one hand and all the necessary pedagogical, psychological and methodical knowledge on the other hand, the Digital Education study program meets the challenges of modern education. ** ** **Material and method** The research in this paper aims to examine and determine the degree of digital competence of teachers and the level of their capability for independent use of ICT as well as to determine attitudes of teachers toward the introduction of a professional associate into educational practice. These associates would be a help and support to teachers in applying of ICT in preparing lessons and teaching. The research within this paper was conducted in primary and secondary schools in the territory of Vojvodina, which is an autonomous region within the Republic of Serbia. In the research the survey method and an anonymous questionnaire were used. The questionnaire was filled out by 240 teachers. The first part of the survey contains demographic questions related to gender, age and educational level of the examinees that represent the research sample. The gender structure of the examinees, which shows that almost 80% women participated in the research, is given in Figure 1. ![](https://hub.ufzg.hr/uploads/images/gallery/2025-05/embedded-image-keinvnmi.png) Figure 1. Gender structure of examinees Figure 2 shows the age structure of examinees. The smallest number of examinees (2.94%) is under 30 years old, 21.85% are between 31 and 40 years old, 30.25% are between 41 and 50 years old, while 37.39% of examinees are teachers aged 51 to 60. Examinees over 61 years old is 7.56% of the total number. Based on the results presented, it can be seen that more than two-thirds of teachers are between 41 and 60 years old, while the smallest percentage of examinees is under 30 years old. ![](https://hub.ufzg.hr/uploads/images/gallery/2025-05/embedded-image-nc0jfeej.png) Figure 2. Age structure of examinees The educational structure of examinees is shown in Figure 3. Based on the data presented in Figure 3, it can be observed that the largest percentage of examinees (38.4%) completed academic studies or bachelor’s degree, while the smallest number of examinees (1.27%) completed specialized studies. Almost three quarters of the total number of examinees have completed academic studies or a master's degree. Only 1.69% of examinees have a doctorate. ![](https://hub.ufzg.hr/uploads/images/gallery/2025-05/embedded-image-8bqcrect.png) Figure 3. Educational structure of examinees ** ** **Results and discussion** The second part of the questionnaire contains questions whose answers allow to determine the level of digital competence of teachers, as well as the level of ability to independently use ICT for preparing lessons and teaching. Figure 4 shows the examinees' answers to the question related to attending a training course related to the use of ICT in teaching. The majority of the total number of examinees (77.5%) attended some course. It can be noticed that more than three quarters of examinees have some form of informal ICT education. ![](https://hub.ufzg.hr/uploads/images/gallery/2025-05/embedded-image-orjv5ao4.png) Figure 4. Have you attended any training course related to the use of ICT in teaching? The purpose of using modern technologies is shown in Figure 5. Examinees had the opportunity to give multiple answers and it can be seen that modern technologies are used most often for finding teaching materials (26.42%) and preparing for teaching (22.63%), then follow information dissemination (20.85%), communication (17.77%) and social networks (11.02%). Other purposes, including conducting lessons, student motivation, conducting online classes and other needs are represented by less than one percent. ![](https://hub.ufzg.hr/uploads/images/gallery/2025-05/embedded-image-swyxnrwh.png) Figure 5. The purpose of using modern technologies Figure 6 shows the examinees' answers to the question of which modern technology tools and devices they use when preparing lessons and teaching. Examinees could give multiple answers. Based on the answers given, it can be observed that the most commonly used device is a computer or laptop, which was chosen by 230 examinees when preparing lessons and teaching. In addition, examinees separated the Internet (215 examinees), a video beam (192 examinees), e-platforms (137 examinees), educational software (61 examinees), interactive whiteboard (38 examinees) and TV (28 examinees). The smallest number of examinees (only 5 examinees) chose DVD recorder as the device they most often use when preparing lessons and teaching. In the similar research conducted in primary and secondary schools in the territory of the Municipality of Zvornik, the largest percentage of examinees also stated that they use the computer the most out of digital devices when preparing lessons and teaching (Blagojević et al., 2022). Authors who researched the use of computers for the purpose of preparing lessons and teaching by teachers who do not teach informatics came to similar findings (Ilić, 2020). ![](https://hub.ufzg.hr/uploads/images/gallery/2025-05/embedded-image-kf1fd2i1.png) Figure 6. Which modern technology tools and devices do you use when preparing lessons and teaching? Figure 7 shows the examinees' responses related to self-assessment of IT literacy. The largest number of examinees, representing almost half of the total number (44.07%), rated their IT literacy as good with a score of 4. While 27.54% of examinees gave a score of 3, and a quarter of examinees (25.42)% consider their IT literacy is very good. Of the total number of examinees 2.54% rated their IT literacy as 2. Less than one percent (0.42%) of examinees consider their IT literacy is very poor. More than 95% of examinees rated their IT literacy with a score of 3 (adequate) or better. ![](https://hub.ufzg.hr/uploads/images/gallery/2025-05/embedded-image-fyeyeahm.png) Figure 7. Rates of IT literacy Alos, according to research (Rogošić et al., 2021) conducted in secondary vocational schools in Zagreb and Zagreb County, the majority of teachers rate their IT literacy very highly in terms of the use of widely applicable computer programs within the Microsoft Office package. The last part of the questionnaire used a modified Likert scale with multiple-choice questions to examine teachers' attitudes towards the introduction of an ICT professional associate into educational practice. Figure 8 shows that almost two-thirds of the examinees strongly agree or mostly agree with the statement that they would like to have an ICT professional associate. It also can be notice that about 15% of examinees strongly disagree or mostly disagree with that statement and about one fifth of examinees have no opinion regarding ICT specialist as a collaborator. ![](https://hub.ufzg.hr/uploads/images/gallery/2025-05/embedded-image-xgknl9mp.png) Figure 8. I would like to have an ICT specialist as a collaborator More than half of the examinees strongly agree or mostly agree with the statement that a professional associate would be of great help in preparing lessons and teaching, while one quarter of the examinees stongly disagree or mostly disagree with this statement, as shown in Figure 9. Almost one-fifth of examinees have no opinion regarding the significance of assistance from professional associate in preparing lessons and teaching. ![](https://hub.ufzg.hr/uploads/images/gallery/2025-05/embedded-image-vlpagcon.png) Figure 9. Having a professional associate would be of great help to me in preparing lessons and teaching The research findings (Milić & Milojević, 2022) also show that the examinees have a great need for professional help in order to expand their knowledge and improve the skills needed for teaching using ICT. Figure 10 shows that almost two-thirds of the examinees strongly agree or mostly agree with the statement that they would like to have an ICT professional associate which would assist them in creating quizzes and presentations to make the classes more engaging and improve the teaching quality. Almost a fifth of examinees strongly disagree or mostly disagree with this statement, while 15% of them are undecided. Also, according to research (Ranđelovic, 2022), the majority of teachers believe that the application of ICT in teaching contributes to the improvement of the quality and durability of acquired knowledge among students. ![](https://hub.ufzg.hr/uploads/images/gallery/2025-05/embedded-image-mvn7nstb.png) Figure 10. I would like a professional associate to assist me in creating quizzes and presentations to make the classes more engaging and improve the teaching quality Figure 11 shows the examinees' answers about the need to know various digital tools for creattion of teaching materials and tests. Based on the examinees' answers, it can be seen that three quarters of the examinees strongly agree or mostly agree with the statement that they would like to have more knowledge about digital tools in order to create more fun and better teaching materials and tests and thus motivate students to better follow the lessons. About 13% of examinees strongly disagree or mostly disagree with this statement, while 12% have no opinion on this issue. ![](https://hub.ufzg.hr/uploads/images/gallery/2025-05/embedded-image-7ljip9bd.png) Figure 11. I would like to know various digital tools and learn to create teaching materials and tests Similar research (Milić & Milojević, 2022) shows that teachers have a significant need to learn about digital tools intended for the creation of teaching materials, including quizzes, educational computer games and multimedia presentations. Also, according to research (Randjelovic, 2022), the majority of teachers believe that the application of ICT in teaching contributes to the improvement of the quality and durability of acquired knowledge among students. At the end of the questionnaire, there are questions related to the new accredited study program Digital Education. Structure of examinees' answers to the question “Are you familiar with the fact that at the Faculty of Education in Sombor from the academic year 2023/24 is there an accredited study program Digital Education at the undergraduate and master's level, which trains a profile that will help and support teachers in the application of ICT during the preparation lessons and teaching?“ is shown in Figure 12. Based on the responses received, it can be concluded that the more than 80% of examinees are not familiar with the accreditation of the Digital Education new study program. The responses which show that more than two thirds of examinees support the new accredited study program and consider it useful are given in Figure 13. About 7% of examinees do not support the accreditation of the new study program, while about a fifth of examinees are undecided. ![](https://hub.ufzg.hr/uploads/images/gallery/2025-05/embedded-image-3qxqjqvv.png) Figure 12. Digital Education study program ![](https://hub.ufzg.hr/uploads/images/gallery/2025-05/embedded-image-2myzozfa.png) Figure 13. Do you support and consider the accreditation of Digital Education study program is useful? The findings of this research show that the introduction of a professional associate in the field of ICT would be extremely useful for improving the quality of teaching, which implies the use of digital technologies in their full potential and capacity. Also, it should be noted that there are certain limitations of this research that may affect the findings, having in mind that the research was conducted in primary and secondary schools only in the territory of Vojvodina. Therefore, it would be useful to conduct research on the territory of the entire Republic of Serbia and determine the attitudes of teachers in both urban and rural areas. In order to improve the Digital Education study program, it would be useful to direct further research towards examining students' satisfaction with the existing study program, as well as identifying their suggestions for improving the study program. **Conclusions** ** ** The research results presented in this paper show even if the majority of teachers have a satisfactory level of digital competence and use digital technolgies in educational practice, most of them believe that the introduction of a professional associate in the application of ICT would be extremely helpful for higher quality teaching and enable the use of digital technologies in full capacity. Findings of this research show that the majority of teachers are not familiar with the accreditation of the Digital Education study program. Also, they support accreditation and consider it useful. 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