FACTORS SHAPING THE ADOPTION OF ARTIFICIAL INTELLIGENCE IN EDUCATION
Abstract
With the growing influence of Artificial Intelligence (AI) in education, this study explores the factors shaping the intention of future teachers and educators to integrate AI-based tools into their practices. It investigates how variables such as intelligent-ethical Technological Pedagogical Content Knowledge (TPACK), AI-related anxiety, subjective norms, AI for social good, and self-assessed AI competence influence this intention. Data were collected from 157 pre-service teachers at the Faculties of Education in Jagodina and Belgrade, and multiple regression analysis revealed that 72.9% of the variance in intention to use AI was explained by these variables. Intelligent-ethical TPACK (β = 0.501, p < 0.001) emerged as the strongest predictor, followed by AI for social good (β = 0.296, p < 0.001) and subjective norms (β = 0.195, p < 0.01). The results highlight the importance of enhancing teacher education programs to develop intelligent-ethical TPACK and awareness of AI’s role in promoting social good.
References
Adiguzel, T., Kaya, M. & Cansu, F. (2023). Revolutionizing education with AI: Exploring the transformative potential of ChatGPT. Contemporary Educational Technology. https://doi.org/10.30935/cedtech/13152
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
Arya, R. & Verma, A. (2024). Role of Artificial intelligence in education. International Journal of Advanced Research in Science, Communication and Technology, 589–594. https://doi.org/10.48175/ijarsct-19461
Asamoah, M. (2019). TPACKEA Model for Teaching and Students’ Learning. Journal of Academic Ethics, 17, 401–421. https://doi.org/10.1007/s10805-019-09326-4.
Ayanwale, M. A., Sanusi, I. T., Adelana, O. P., Aruleba, K. D. & Oyelere, S. S. (2022). Teachers’ readiness and intention to teach artificial intelligence in schools. Computers and Education: Artificial Intelligence, 3, 100099. https://doi.org/10.1016/j.caeai.2022.100099
Bezzina, S. & Dingli, A. (2024). The transformative potential of Artificial Intelligence for Education. Proceedings of the International Conference on Networked Learning, 14. https://doi.org/10.54337/nlc.v14i1.8077
Bibi, A. (2024). Navigating the ethical landscape: Ai integration in education. Educational Administration Theory and Practice, 1579–1585. https://doi.org/10.53555/kuey.v30i6.5546
Bond, M., Khosravi, H., De Laat, M., Bergdahl, N., Negrea, V., Oxley, E., ... & Siemens, G. (2024). A meta systematic review of artificial intelligence in higher education: a call for increased ethics, collaboration, and rigour. International Journal of Educational Technology in Higher Education, 21 (1), 4. https://doi.org/10.1186/s41239-023-00436-z
Borges, A. F. S., Laurindo, F. J. B., Spínola, M. M., Gonçalves, R. F. & Mattos, C. A. (2021). The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. International Journal of Information Management, 57 (102225), 102225. https://doi.org/10.1016/j.ijinfomgt.2020.102225
Canonigo, A. M. (2024). Levering AI to enhance students’ conceptual understanding and confidence in mathematics. Journal of Computer Assisted Learning, 40 (6), 3215–3229. https://doi.org/10.1111/jcal.13065
Celik, I. (2023). Towards Intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behavior, 138, 107468. https://doi.org/10.1016/j.chb.2022.107468
Chai, C. S., Wang, X. & Xu, C. (2020). An extended theory of planned behavior for the modelling of Chinese secondary school students’ intention to learn artificial intelligence. Mathematics, 8 (11), 2089. https://doi.org/10.3390/math8112089
Chai, C., Lin, P., Jong, M., Dai, Y., Chiu, T. & Huang, B. (2020). Factors Influencing Students' Behavioral Intention to Continue Artificial Intelligence Learning. 2020 International Symposium on Educational Technology (ISET), 147–150. https://doi.org/10.1109/ISET49818.2020.00040.
Christian, M., Pardede, R., Gularso, K., Dewi, Y. S. & Amiro, T. (2024). Examining Learning Anxiety in AI-Enhanced Educational Environments Among Urban Lecturers. 2024 3rd International Conference on Creative Communication and Innovative Technology (ICCIT), 1–5. https://doi.org/10.1109/ICCIT62134.2024.10701128.
Dahlin, E. (2021). Mind the gap! On the future of AI research. Humanities & Social Sciences Communications, 8 (1). https://doi.org/10.1057/s41599-021-00750-9
De Vellis, R. (2003). Scale development: Theory and applications (2nd ed.). California: Thousand Oaks, California: Sage ISBN-13. 978-0761926047
Djordjevic, S., Kostic, M., Milosevic, D., Cvetkovic, M., Mitrovic, K. & Mladenovic, V. (2023). Prediction of overhydration in the process of pediatric hemodialysis using artificial neural network. 2023 12th Mediterranean Conference on Embedded Computing (MECO). https://doi.org/10.1109/MECO58584.2023.10154915
Gerlich, M. (2024). Public anxieties about AI: Implications for corporate strategy and societal impact. Administrative Sciences, 14 (11), 288. https://doi.org/10.3390/admsci14110288
Gómez-Trigueros, I. (2023). Digital skills and ethical knowledge of teachers with TPACK in higher education. Contemporary Educational Technology. https://doi.org/10.30935/cedtech/12874.
Hair Jr. J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2010). Multivariate data analysis (seventh ed.). Prentice-Hall International. ISBN: 978-1-292-02190-4
Ivanov, S., Soliman, M., Tuomi, A., Alkathiri, N. A. & Al-Alawi, A. N. (2024). Drivers of generative AI adoption in higher education through the lens of the Theory of Planned Behaviour. Technology in Society, 77 (102521), 102521. https://doi.org/10.1016/j.techsoc.2024.102521
Jaffar, M., Jogezai, N., Latiff, A., Baloch, F. & Khilji, G. (2024). University teachers at the crossroads: unpacking their intentions toward ChatGPT's instructional use. Journal of Applied Research in Higher Education. https://doi.org/10.1108/jarhe-10-2023-0463.
Jain, K. K. & Raghuram, J. N. V. (2024). Gen-AI integration in higher education: Predicting intentions using SEM-ANN approach. Education and Information Technologies. https://doi.org/10.1007/s10639-024-12506-4
Jatileni, C. N., Sanusi, I. T., Olaleye, S. A., Ayanwale, M. A., Agbo, F. J. & Oyelere, P. B. (2023). Artificial intelligence in compulsory level of education: Perspectives from Namibian in-service teachers. Education and Information Technologies, 1–28. https://doi.org/10.1007/s10639-023-12341-z
Johnson, D. & Verdicchio, M. (2017). AI Anxiety. Journal of the Association for Information Science and Technology, 68. https://doi.org/10.1002/asi.23867.
Kusmawan, U. (2023). Redefining Teacher Training: The Promise of AI-Supported Teaching Practices. Journal of Advances in Education and Philosophy. https://doi.org/10.36348/jaep.2023.v07i09.001.
Leddy, M. & Creanor, N. (2024). Exploring How Education Can Leverage Artificial Intelligence for Social Good. European Conference on Innovation and Entrepreneurship. https://doi.org/10.34190/ecie.19.1.2906.
Lin, H., Karusala, N., Okolo, C. T., D’Ignazio, C. & Gajos, K. Z. (2024). “Come to us first”: Centering Community Organizations in Artificial Intelligence for Social Good Partnerships. Proceedings of the ACM on Human-Computer Interaction, 8(CSCW2), 1–28. https://doi.org/10.1145/3687009
Ma, S. & Lei, L. (2024). The factors influencing teacher education students’ willingness to adopt artificial intelligence technology for information-based teaching. Asia Pacific Journal of Education, 44 (1), 94–111. https://doi.org/10.1080/02188791.2024.2305155
Mandić, D. (2023). Report on Smart Education in the Republic of Serbia. In: Zhuang, R., et al. (eds.). Smart Education in China and Central & Eastern European Countries. Lecture Notes in Educational Technology, 271–291. Springer.
Mandić, D. P., Miščević, G. M. & Bujišić, L. G. (2024). Evaluating the quality of responses generated by ChatGPT. Metodička teorija i praksa, 27 (1), 5–19. https://doi.org/10.5937/metpra27-51446
Milutinović, V. (2016). An exploration of acceptance of innovative computer use in teaching mathematics among pre-service class teachers and mathematics teachers. Zbornik Instituta za pedagoska istrazivanja, 48 (2), 339–366. https://doi.org/10.2298/ZIPI1602339M
Milutinović, V. (2022). Examining the influence of pre-service teachers’ digital native traits on their technology acceptance: A Serbian perspective. Education and Information Technologies, 27, 6483–6511. https://doi.org/10.1007/s10639-022-10887-y
Milutinović, V. (2024). Unlocking the code: Exploring predictors of future interest in learning computer programming among primary school boys and girls. International Journal of Human-Computer Interaction, 1–18. https://doi.org/10.1080/10447318.2024.2331877
Milutinović, V. & Mandić, D. (2022). Predviđanje prihvatanja upotrebe računara na tradicionalnom i inovativnom nivou u nastavi matematike u Srbiji. Inovacije u Nastavi, 35 (2), 71–88. https://doi.org/10.5937/inovacije2202071M
Mishra, M. S. (2024). Revolutionizing education: The impact of AI-enhanced teaching strategies. International Journal for Research in Applied Science and Engineering Technology, 12 (9), 9–32. https://doi.org/10.22214/ijraset.2024.64127
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
Mladenović, V., Kostić, M., Milošević, D., Zanaj, E. & Đorđević, S. (2024). SYSTEM FOR PREDICTION AND BALANCING EXCESS FLUID IN THE BODY DURING HEMODIALYSIS BASED ON ARTIFICIAL INTELLIGENCE (Patent No. RS20240030A2). In Patent (RS20240030A2). https://worldwide.espacenet.com/patent/search/family/090057728/publication/RS20240030A2?q=pn%3DRS20240030A2
Moreno, J., Montoro, M. & Colon, A. (2019). Changes in Teacher Training within the TPACK Model Framework: A Systematic Review. Sustainability. https://doi.org/10.3390/SU11071870.
Mubofu, C. & Kitali, L. (2024). Artificial Intelligence in education: Ethics & responsible implementation. Journal of Interdisciplinary Studies in Education, 13 (2). https://doi.org/10.32674/9rjyjp52
Mutawa, A. M. & Sruthi, S. (2024). UNESCO’s AI competency framework: Challenges and opportunities in educational settings. In Advances in Educational Technologies and Instructional Design, 75–96. IGI Global. https://doi.org/10.4018/979-8-3693-0884-4.ch004
Ning, Y., Zhang, C., Xu, B., Zhou, Y. & Wijaya, T. T. (2024). Teachers’ AI-TPACK: Exploring the Relationship between Knowledge Elements. Sustainability, 16 (3), 978. https://doi.org/10.3390/su16030978
Salas-Pilco, S. Z., Xiao, K. & Hu, X. (2022). Artificial intelligence and learning analytics in teacher education: A systematic review. Education Sciences, 12 (8), 569. https://doi.org/10.3390/educsci12080569
Sanusi, I. T., Ayanwale, M. A. & Chiu, T. K. (2023). Investigating the moderating effects of social good and confidence on teachers' intention to prepare school students for artificial intelligence education. Education and information technologies, 1–23. https://doi.org/10.1007/s10639-023-12250-1
Sanusi, I. T., Ayanwale, M. A. & Tolorunleke, A. E. (2024). Investigating pre-service teachers’ artificial intelligence perception from the perspective of planned behavior theory. Computers and Education: Artificial Intelligence, 100202. https://doi.org/10.1016/j.caeai.2024.100202
Sengsri, S. & Khunratchasana, K. (2024). Artificial intelligence competence: A crucial skill for the digital citizens. International Education Studies, 17 (3), 75. https://doi.org/10.5539/ies.v17n3p75
Seo, K., Tang, J., Roll, I., Fels, S. & Yoon, D. (2021). The impact of artificial intelligence on learner–instructor interaction in online learning. International Journal of Educational Technology in Higher Education, 18. https://doi.org/10.1186/s41239-021-00292-9.
Shulman, L. S. (1986). Those who understand: Knowledge growth in teaching. Educational Researcher, 15 (2), 4–14. https://doi.org/10.3102/0013189X015002004
Sun, F., Tian, P., Sun, D., Fan, Y. & Yang, Y. (2024). Pre‐service teachers’ inclination to integrate AI into STEM education: Analysis of influencing factors. British Journal of Educational Technology: Journal of the Council for Educational Technology. https://doi.org/10.1111/bjet.13469
Sutrisman, H., Simanjuntak, R., Prihartanto, A. & Kusumo, B. (2024). The Impact of Using AI in Learning on Understanding of Material by Young Students. International Journal of Educational Research. https://doi.org/10.62951/ijer.v1i3.43.
Talukdar, E. (2023). Impact of Artificial Intelligence based Learning Process on Students' Tendency to Involve in Independent Research at the Higher Secondary School. International Journal on Recent and Innovation Trends in Computing and Communication. https://doi.org/10.17762/ijritcc.v11i9.9924.
Tseng, J., Chai, C., Tan, L. & Park, M. (2020). A critical review of research on technological pedagogical and content knowledge (TPACK) in language teaching. Computer Assisted Language Learning, 35, 948–971. https://doi.org/10.1080/09588221.2020.1868531.
Wangdi, P. (2024). Integrating artificial Intelligence in education: Trends and opportunities. International Journal of Research in STEM Education, 6 (2), 50–60. https://doi.org/10.33830/ijrse.v6i2.1722
Yang, C., Huan, S. & Yang, Y. (2020). A Practical Teaching Mode for Colleges Supported by Artificial Intelligence. Int. J. Emerg. Technol. Learn., 15, 195–206. https://doi.org/10.3991/ijet.v15i17.16737.
Yang, Y. & Xia, N. (2023). Enhancing Students' Metacognition via AI-Driven Educational Support Systems. Int. J. Emerg. Technol. Learn., 18, 133–148. https://doi.org/10.3991/ijet.v18i24.45647.
Zhang, C., Schießl, J., Plößl, L., Hofmann, F. & Gläser-Zikuda, M. (2023). Acceptance of artificial intelligence among pre-service teachers: a multigroup analysis. International Journal of Educational Technology in Higher Education, 20 (1), 49. https://doi.org/10.1186/s41239-023-00420-7
Zhang, J. & Zhang, Z. (2024). AI in teacher education: Unlocking new dimensions in teaching support, inclusive learning, and digital literacy. J. Comput. Assist. Learn., 40, 1871– 1885. https://doi.org/10.1111/jcal.12988.
Zhang, W. & Hou, Z. (2024). College Teachers’ Behavioral Intention to Adopt Artificial Intelligence-Assisted Teaching Systems. IEEE Access, 12, 152812–152824. https://doi.org/10.1109/ACCESS.2024.3445909.
