Review Article

Generative AI in higher education: A scoping review and framework for student motivation and engagement

Edmund De Leon Evangelista 1 * , Syed M. Salman Bukhari 1
More Detail
1 College of Technological Innovation, Zayed University, Abu Dhabi, UNITED ARAB EMIRATES* Corresponding Author
Contemporary Educational Technology, 18(4), October 2026, ep694, https://doi.org/10.30935/cedtech/19499
Published: 06 October 2026
OPEN ACCESS   161 Views   75 Downloads
Download Full Text (PDF)

ABSTRACT

Generative artificial intelligence (GenAI) is increasingly influencing how students write, create, reflect, solve problems, and engage with learning in higher education. However, the evidence remains divided, particularly regarding motivation, engagement, creativity, perception, self-regulation, and psychologically informed learning design. This study presents a PRISMA-ScR-based scoping review of GenAI in higher education from a motivational and psychological perspective. The final corpus included 195 studies published between 2023 and 2025 within a specified eligibility window of January 2021 to December 2025. Records were dual screened, coded using a structured framework, and analyzed across psychological focus, thematic constructs, GenAI tools, study designs, outcomes, contextual characteristics, self-determination theory (SDT) needs, equity, and evidence strength. The screening agreement was 81.9%, with Cohen’s κ = 0.642. Motivation was the most common primary focus (31.3%), followed by perception (26.7%), engagement (18.5%), creativity (15.4%), and general/other (8.2%). The literature was dominated by ChatGPT and general GenAI systems, while survey-based and observational evidence was far more common than experimental evidence, longitudinal research, or studies reporting objective performance outcomes. Among 155 empirical studies, mixed or conditional outcomes were most frequent (46.5%), compared with predominantly positive findings (21.9%). SDT related evidence was most common for competence (42.6%), followed by autonomy (29.7%) and relatedness (22.6%), although direct measurement was rare. Based on these findings, the study proposes the GenAI engagement framework, a literature informed structure that organizes five recurring components of GenAI supported learning without assuming a fixed sequence or causal pathway. The framework is not presented as a validated causal model, but as a structured basis for future research and instructional design. The findings suggest that the educational value of GenAI depends less on the technology itself than on how it is guided, evaluated, and integrated into meaningful learning activities.
Keywords: generative artificial intelligence, student motivation, learner engagement, self-determination theory, higher education, educational psychology, multimodal learning, AI-enhanced learning

CITATION (APA)

Evangelista, E. D. L., & Bukhari, S. M. S. (2026). Generative AI in higher education: A scoping review and framework for student motivation and engagement. Contemporary Educational Technology, 18(4), ep694. https://doi.org/10.30935/cedtech/19499

REFERENCES

  1. Abbas, M., Jam, F. A., & Khan, T. I. (2024). Is it harmful or helpful? Examining the causes and consequences of generative AI usage among university students. International Journal of Educational Technology in Higher Education, 21, Article 10. https://doi.org/10.1186/s41239-024-00444-7
  2. Arksey, H., & O’Malley, L. (2005). Scoping studies: Towards a methodological framework. International Journal of Social Research Methodology, 8(1), 19-32. https://doi.org/10.1080/1364557032000119616
  3. Baidoo-Anu, D., & Owusu Ansah, L. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1), 52-62. https://doi.org/10.61969/jai.1337500
  4. Bannister, P., Santamaría Urbieta, A., & Alcalde Peñalver, E. (2023). A systematic review of generative AI and (English-medium-instruction higher education). Aula Abierta, 52(4), 401-409. https://doi.org/10.17811/rifie.52.4.2023.401-409
  5. Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20, Article 43. https://doi.org/10.1186/s41239-023-00411-8
  6. Collie, R. J., & Martin, A. J. (2024). Teachers’ motivation and engagement to harness generative AI for teaching and learning: The role of contextual, occupational, and background factors. Computers and Education: Artificial Intelligence, 6, Article 100224. https://doi.org/10.1016/j.caeai.2024.100224
  7. Deci, E. L., & Ryan, R. M. (2012). Self-determination theory. In P. A. M. Van Lange, A. W. Kruglanski, & E. T. Higgins (Eds.), Handbook of theories of social psychology (pp. 416-437). SAGE. https://doi.org/10.4135/9781446249215.n21
  8. Dempere, J., Modugu, K., Hesham, A., & Ramasamy, L. K. (2023). The impact of ChatGPT on higher education. Frontiers in Education, 8. https://doi.org/10.3389/feduc.2023.1206936
  9. Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59-109. https://doi.org/10.3102/00346543074001059
  10. Garcia, M. B. (2025). The paradox of artificial creativity: Challenges and opportunities of generative AI artistry. Creativity Research Journal, 37(4), 755-768. https://doi.org/10.1080/10400419.2024.2354622
  11. 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, Article 102521. https://doi.org/10.1016/j.techsoc.2024.102521
  12. Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., . . . Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, Article 102274. https://doi.org/10.1016/j.lindif.2023.102274
  13. Kazu, İ. Y., & Kuvvetli, M. (2023). A triangulation method on the effectiveness of digital game-based language learning for vocabulary acquisition. Education and Information Technologies, 28, 13541-13567. https://doi.org/10.1007/s10639-023-11756-y
  14. Kirkwood, A., & Price, L. (2014). Technology-enhanced learning and teaching in higher education: What is ‘enhanced’ and how do we know? A critical literature review. Learning, Media and Technology, 39(1), 6-36. https://doi.org/10.1080/17439884.2013.770404
  15. Kumar, S., Rao, P., Singhania, S., Verma, S., & Kheterpal, M. (2024). Will artificial intelligence drive the advancements in higher education? A tri-phased exploration. Technological Forecasting and Social Change, 201, Article 123258. https://doi.org/10.1016/j.techfore.2024.123258
  16. Lo, C. K., Hew, K. F., & Jong, M. S.-Y. (2024). The influence of ChatGPT on student engagement: A systematic review and future research agenda. Computers & Education, 219, Article 105100. https://doi.org/10.1016/j.compedu.2024.105100
  17. Omran Zailuddin, M. F. N., Nik Harun, N. A., Abdul Rahim, H. A., Kamaruzaman, A. F., Berahim, M. H., Harun, M. H., & Ibrahim, Y. (2024). Redefining creative education: A case study analysis of AI in design courses. Journal of Research in Innovative Teaching & Learning, 17(2), 282-296. https://doi.org/10.1108/JRIT-01-2024-0019
  18. Panadero, E. (2017). A review of self-regulated learning: Six models and four directions for research. Frontiers in Psychology, 8. https://doi.org/10.3389/fpsyg.2017.00422
  19. Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., & Sutskever, I. (2021). Zero-shot text-to-image generation. In M. Meila, & T. Zhang (Eds.), Proceedings of the 38th International Conference on Machine Learning (pp. 8821-8831).
  20. Runco, M. A. (2014). Creativity: Theories and themes: Research, development, and practice (2nd ed.). Elsevier.
  21. Saúde, S., Barros, J. P., & Almeida, I. (2024). Impacts of generative artificial intelligence in higher education: Research trends and students’ perceptions. Social Sciences, 13(8), Article 410. https://doi.org/10.3390/socsci13080410
  22. Sok, S., & Heng, K. (2024). Opportunities, challenges, and strategies for using ChatGPT in higher education: A literature review. Journal of Digital Educational Technology, 4(1), Article ep2401. https://doi.org/10.30935/jdet/14027
  23. Tricco, A. C., Lillie, E., Zarin, W., O’Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., Hempel, S., Akl, E. A., Chang, C., McGowan, J., Stewart, L., Hartling, L., Aldcroft, A., Wilson, M. G., Garritty, C., . . . Straus, S. E. (2018). PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. Annals of Internal Medicine, 169(7), 467-473. https://doi.org/10.7326/M18-0850
  24. Zhai, X. (2023). ChatGPT for next generation science learning. XRDS: Crossroads, The ACM Magazine for Students, 29(3), 42-46. https://doi.org/10.1145/3589649
  25. Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64-70. https://doi.org/10.1207/S15430421TIP4102_2