Research Article

AI transformation in education: Examining teachers’ perceptions using an integrated TAM-TPACK-GenAI framework

Areej ElSayary 1 * , Ghadah Al Murshidi 2 , Karim Ragab 3 , Ahmed Al Zaabi 4
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1 Zayed University, Dubai, UNITED ARAB EMIRATES2 United Arab Emirates University, Al Ain, UNITED ARAB EMIRATES3 Al Ittihad Private School, Dubai, UNITED ARAB EMIRATES4 General Department of Protective Security & Emergency, Dubai, UNITED ARAB EMIRATES* Corresponding Author
Contemporary Educational Technology, 18(1), January 2026, ep636, https://doi.org/10.30935/cedtech/17983
Published: 26 February 2026
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ABSTRACT

Artificial intelligence (AI) is transforming educational systems by enhancing teaching, assessment, and learning personalization. This study investigated teachers’ perceptions of AI integration using an integrated technology acceptance model (TAM), technological pedagogical content knowledge (TPACK), and generative artificial intelligence (GenAI) framework. The main constructs used are perceived usefulness (PU), attitudes toward use (ATU), and behavioral intention (BI), with GenAI dimensions (agency, amplification, adaptivity, and authenticity) embedded within them. The study employed a cross-sectional design with 332 teachers in the emirate of Al Ain, United Arab Emirates. Results showed that PU was the strongest predictor of both ATU and BI, while ATU did not significantly mediate the PU-BI relationship. Amplification and adaptivity were positively perceived, whereas concerns about authenticity and agency tempered attitudes. Teachers aged 30-49 and those with 1-10 years of experience reported higher BI, and teachers of grades 4-9 showed greater PU. The findings highlight the need for professional development that fosters both practical integration and ethical understanding of AI in education.

CITATION (APA)

ElSayary, A., Al Murshidi, G., Ragab, K., & Al Zaabi, A. (2026). AI transformation in education: Examining teachers’ perceptions using an integrated TAM-TPACK-GenAI framework. Contemporary Educational Technology, 18(1), ep636. https://doi.org/10.30935/cedtech/17983

REFERENCES

  1. Abbas, N., Ali, I., Manzoor, R., Hussain, T., & Hussain, M. H. A. I. (2023). Role of artificial intelligence tools in enhancing students’ educational performance at higher levels. Journal of Artificial Intelligence Machine Learning and Neural Network, 35, 36-49. https://doi.org/10.55529/jaimlnn.35.36.49
  2. Adıgüzel, T., Kaya, M., & Cansu, F. (2023). Revolutionizing education with AI: exploring the transformative potential of ChatGPT. Contemporary Educational Technology, 15(3), Article ep429. https://doi.org/10.30935/cedtech/13152
  3. Aldossary, A., Aljindi, A., & Alamri, J. (2024). The role of generative AI in education: Perceptions of Saudi students. Contemporary Educational Technology, 16(4), Article ep536. https://doi.org/10.30935/cedtech/15496
  4. Celik, I. (2022). 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, Article 107468. https://doi.org/10.1016/j.chb.2022.107468
  5. Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20(1). https://doi.org/10.1186/s41239-023-00392-8
  6. Dann, C., Redmond, P., Fanshawe, M., Brown, A., Getenet, S., Shaik, T., Tao, X., Galligan, L., & Li, Y. (2024). Making sense of student feedback and engagement using artificial intelligence. Australasian Journal of Educational Technology, 40(3), 58-76. https://doi.org/10.14742/ajet.8903
  7. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
  8. Davis, R. O. (2024). Korean in-service teachers’ perceptions of implementing artificial intelligence (AI) education for teaching in schools and their AI teacher training programs. International Journal of Information and Education Technology, 14(2), 214-219. https://doi.org/10.18178/ijiet.2024.14.2.2042
  9. Eleftheriou, M., Ahmer, M., & Fredrick, D. (2025). Balancing ethics and support: Peer tutors’ experiences with AI tools in student writing. Contemporary Educational Technology, 17(3), Article ep587. https://doi.org/10.30935/cedtech/16554
  10. ElSayary, A. (2023). An investigation of teachers’ perceptions of using ChatGPT as a supporting tool for teaching and learning in the digital era. Journal of Computer Assisted Learning, 40(3), 931-945. https://doi.org/10.1111/jcal.12926
  11. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104
  12. Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling (PLS-SEM) using R. Springer. https://doi.org/10.1007/978-3-030-80519-7
  13. Henseler, J., Ringle, C. M., & Sarstedt, M. (2014). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135. https://doi.org/10.1007/s11747-014-0403-8
  14. Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. CRC.
  15. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling a Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
  16. Kemp, A., Palmer, E., Strelan, P., & Thompson, H. (2024). Testing a novel extended educational technology acceptance model using student attitudes towards virtual classrooms. British Journal of Educational Technology, 55(5), 2110-2131. https://doi.org/10.1111/bjet.13440
  17. Lan, G., Feng, X., Du, S., Song, F., & Xiao, Q. (2025). Integrating ethical knowledge in generative AI education: Constructing the GenAI-TPACK framework for university teachers’ professional development. Education and Information Technologies, 30, 15621-15644. https://doi.org/10.1007/s10639-025-13427-6
  18. López, J. B. (2024). Implications of artificial intelligence in education. The educator as ethical leader. Journal of Interdisciplinary Education Theory and Practice, 6(2), 142-152. https://doi.org/10.47157/jietp.1505319
  19. Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson.
  20. Luik, P., & Taimalu, M. (2021). Predicting the intention to use technology in education among student teachers: A path analysis. Education Sciences, 11(9), Article 564. https://doi.org/10.3390/educsci11090564
  21. Mishra, N. R., & Varshney, N. D. (2024). Comprehensive analysis of human and AI task allocation in the education sector: Defining futuristic roles and responsibilities. World Journal of Advanced Research and Reviews, 22(3), 1883-1893. https://doi.org/10.30574/wjarr.2024.22.3.1949
  22. Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record the Voice of Scholarship in Education, 108(6), 1017-1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x
  23. Mishra, P., Warr, M., & Islam, R. (2023). TPACK in the age of ChatGPT and generative AI. Journal of Digital Learning in Teacher Education, 39(4), 235-251. https://doi.org/10.1080/21532974.2023.2247480
  24. Muslimin, A., Mukminatien, N., & Ivone, F. (2023). TPACK-SAMR digital literacy competence, technostress, and teaching performance: Correlational study among EFL lecturers. Contemporary Educational Technology, 15(2), Article ep409. https://doi.org/10.30935/cedtech/12921
  25. Njiku, J., Mutarutinya, V., & Maniraho, J. (2021). Building mathematics teachers’ TPACK through collaborative lesson design activities. Contemporary Educational Technology, 13(2), Article ep297. https://doi.org/10.30935/cedtech/9686
  26. Nunnally, J. C. (1978). Psychometric theory (2nd ed.). McGraw-Hill.
  27. Paidicán, M., & Herrera, P. (2022). The technological-pedagogical knowledge for in-service teachers in primary education: A systematic literature review. Contemporary Educational Technology, 14(3), Article ep370. https://doi.org/10.30935/cedtech/11813
  28. Pawar, G., & Khose, J. (2024). Exploring the role of artificial intelligence in enhancing equity and inclusion in education. International Journal of Innovative Science and Research Technology, 9(4), 2180-2185. https://doi.org/10.38124/ijisrt/ijisrt24apr1939
  29. Sadık, O. (2020). Exploring a community of practice to improve quality of a technology integration course in a teacher education institution. Contemporary Educational Technology, 13(1), Article ep285. https://doi.org/10.30935/cedtech/8710
  30. Santos, M. G. D. (2024). The impact of artificial intelligence on teaching careers: Pedagogical, administrative and ethical challenges and opportunities. International Journal of Advanced Research, 12(10), 854-866. https://doi.org/10.21474/ijar01/19705
  31. Scherer, R., Siddiq, F., & Tondeur, J. (2018). 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
  32. Shi, L., Ding, A., & Choi, I. (2024). Investigating teachers’ use of an AI-Enabled system and their perceptions of AI integration in science classrooms: A case study. Education Sciences, 14(11), Article 1187. https://doi.org/10.3390/educsci14111187
  33. Silva, S. a. C., Lara, M. C. B., Muhammad, I. M. S., Silva, D. J. C., Silva, A. D. C., & Rodríguez, R. R. S. (2025). Artificial intelligence as a co-teacher: The future of personalized teaching. LATAM Revista Latinoamericana De Ciencias Sociales y Humanidades, 5(6). https://doi.org/10.56712/latam.v5i6.3283
  34. Suello, A. O., & Alda, R. C. (2025). English teachers’ ethical considerations in AI integration: A narrative inquiry. Journal of Teaching English for Specific and Academic Purposes, 12(3), 769-781. https://doi.org/10.22190/jtesap241016057s
  35. Tawafak, R., Al-Rahmi, W., Al-Shami, A., Alyoussef, I., & Aldaijy, A. (2025). Exploring the impact of digital gameplay on behavioral intentions in learning graphics programming courses. Contemporary Educational Technology, 17(2), Article ep576. https://doi.org/10.30935/cedtech/16115
  36. Torres-Hernández, N., & Arrufat, M. (2023). Pre-service teachers’ perceptions of data protection in primary education. Contemporary Educational Technology, 15(1), Article ep399. https://doi.org/10.30935/cedtech/12658
  37. Tovar, I. Z., & Ocegueda, G. J. R. G. (2025). Attitudes of university professors towards the use of artificial intelligence in teaching and learning. International Journal of Multidisciplinary Research and Analysis, 8(1). https://doi.org/10.47191/ijmra/v8-i01-46
  38. UAE Government Portal. (2023). We the UAE 2031 vision. UAE Government. https://u.ae/en/about-the-uae/strategies-initiatives-and-awards/uae-strategy/we-the-uae-2031
  39. UAE Ministry of Cabinet Affairs. (2019). UAE artificial intelligence strategy 2031. UAE Government. https://ai.gov.ae
  40. UNESCO. (2022). Education and the SDGs. UNESCO. https://www.unesco.org/en/education/sdgs
  41. Zhao, C. (2025). AI-assisted assessment in higher education: A systematic review. Journal of Educational Technology and Innovation, 6(4). https://doi.org/10.61414/jeti.v6i4.209