The Effect of Generative AI-ChatGPT on Personalized Learning in UAE Secondary Schools: Evidence from Teachers' Perspectives

Generative AI-ChatGPT is becoming a practical issue in school education because it can support interactive explanation, flexible content generation, and individualized learning assistance. This study examined the effect of Generative AI-ChatGPT on personalized learning among secondary school teachers in the United Arab Emirates. A quantitative cross-sectional survey design was used, and data were collected from 389 teachers through a self-administered questionnaire. The AI-ChatGPT construct was measured through six dimensions: natural interaction, adaptive personalization, knowledge reasoning, data dependence, system integration, and learning analytics. Personalized learning was measured through instructional customization, technology integration, technology use, and self-evaluation. The instrument demonstrated strong internal consistency, with Cronbach's alpha values of .959 for AI-ChatGPT and .936 for personalized learning. Descriptive results showed moderate levels of AI-ChatGPT use and personalized learning practice. Multiple regression analysis showed that the AI-ChatGPT dimensions jointly had a statistically significant effect on personalized learning, explaining 76.0% of its variance. Natural interaction and system integration were significant predictors in the combined model. Simple regression results also showed that each AI-ChatGPT dimension had a significant individual effect on personalized learning. The findings indicate that ChatGPT can support personalized learning when its use is guided by teachers, integrated with school systems, and governed by responsible educational practice.

Generative AI; ChatGPT; personalized learning; UAE secondary schools teachers; teachers' perspectives

1. Akyuz, Y. (2020). Effects of intelligent tutoring systems (ITS) on personalized learning (PL). Creative Education, 11(06), 953-978. [Google Scholar] [Crossref]

2. Ayeni, O. O., Al Hamad, N. M., Chisom, O. N., Osawaru, B., & Adewusi, O. E. (2024). AI in education: A review of personalized learning and educational technology. GSC Advanced Research and Reviews, 18(2), 261-271. [Google Scholar] [Crossref]

3. Basham, J. D., Hall, T. E., Carter Jr, R. A., & Stahl, W. M. (2016). An operationalized understanding of personalized learning. Journal of Special Education Technology, 31(3), 126-136. [Google Scholar] [Crossref]

4. Hashim, S., Omar, M. K., Ab Jalil, H., & Sharef, N. M. (2022). Trends on technologies and artificial intelligence in education for personalized learning: Systematic literature. Journal of Academic Research in Progressive Education and Development, 12(1), 884-903. [Google Scholar] [Crossref]

5. Katiyar, N., Awasthi, M. V. K., Pratap, R., Mishra, M. K., Shukla, M. N., & Tiwari, M. (2024). AI-driven personalized learning systems: Enhancing educational effectiveness. Educational Administration: Theory and Practice, 30(5), 11514-11524. [Google Scholar] [Crossref]

6. Maghsudi, S., Lan, A., Xu, J., & van der Schaar, M. (2021). Personalized education in the artificial intelligence era: What to expect next. IEEE Signal Processing Magazine, 38(3), 37-50. [Google Scholar] [Crossref]

7. Murtaza, M., Ahmed, Y., Shamsi, J. A., Sherwani, F., & Usman, M. (2022). AI-based personalized e-learning systems: Issues, challenges, and solutions. IEEE Access, 10, 81323-81342. [Google Scholar] [Crossref]

8. Peng, H., Ma, S., & Spector, J. M. (2019). Personalized adaptive learning: An emerging pedagogical approach enabled by a smart learning environment. Smart Learning Environments, 6(1), 1-14. [Google Scholar] [Crossref]

9. Raj, N. S., & Renumol, V. G. (2022). A systematic literature review on adaptive content recommenders in personalized learning environments from 2015 to 2020. Journal of Computers in Education, 9(1), 113-148. [Google Scholar] [Crossref]

10. Shemshack, A., Kinshuk, & Spector, J. M. (2021). A comprehensive analysis of personalized learning components. Journal of Computers in Education, 8(4), 485-503. [Google Scholar] [Crossref]

11. Short, C. R., & Shemshack, A. (2023). Personalized learning. Edtechnica: The Open Encyclopedia of Educational Technology, 199-204. [Google Scholar] [Crossref]

12. Walkington, C., & Bernacki, M. L. (2020). Appraising research on personalized learning: Definitions, theoretical alignment, advancements, and future directions. Journal of Research on Technology in Education, 52(3), 235-252. [Google Scholar] [Crossref]

13. Yang, A. C., Flanagan, B., & Ogata, H. (2022). Adaptive formative assessment system based on computerized adaptive testing and the learning memory cycle for personalized learning. Computers and Education: Artificial Intelligence, 3, 1-10. [Google Scholar] [Crossref]

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