How artificial intelligence fosters student creativity in higher education: Evidence from a moderated mediation model

Öz

The rapid integration of artificial intelligence (AI) into higher education has created new opportunities for enhancing student learning and creativity. However, research on the mechanisms by which AI-related competencies influence creative learning outcomes remains limited. This study investigates how AI literacy and AI use for learning affect student creativity, with cognitive engagement as a mediating factor and learning motivation as a moderating factor. Data were collected from 420 university students in London between January and May 2025 and analyzed using confirmatory factor analysis, hierarchical regression, and PROCESS-based mediation and moderated mediation models. The findings indicate that both AI literacy and AI use for learning positively influence student creativity. Cognitive engagement partially mediates these relationships, while learning motivation strengthens the link between AI competencies and engagement. The results highlight the importance of combining AI literacy development, engagement-focused pedagogy, and motivational learning environments to foster creativity in AI-supported education.

Anahtar Kelimeler

• Artificial intelligence literacy
• Cognitive engagement
• Learning motivation
• Student creativity
• Higher education

Kaynakça

1. Anderson, E.W., & Sullivan, M.W. (1993). The antecedents and consequences of customer satisfaction for firms. Marketing Science, 12(2), 125–143. https://doi.org/10.1287/mksc.12.2.125
2. Arnadi, A., Aslan, A., & Vandika, A. Y. (2024). The use of artificial intelligence for personalizing learning experiences. Journal of Educational Science and Local Wisdom, 4(5), 369–380.
3. Baker, R., Smith, L., & Wang, Y. (2020). The effects of artificial intelligence on learning outcomes in higher education. Journal of Educational Technology Development and Exchange, 13(2), 115–132.
4. Brandmo, C., & Bråten, I. (2021). Measuring internet-specific reading motivation and engagement in an academic domain. Nordic Journal of Literacy Research, 7(1), 21–44. https://doi.org/10.23865/njlr.v7.2215
5. Bureau, J. S., Howard, J. L., Chong, J. X., & Guay, F. (2022). Pathways to student motivation: A meta-analysis of antecedents of autonomous and controlled motivations. Review of Educational Research, 92(1), 46–72. https://doi.org/10.3102/00346543211042426
6. Chiu, T. K. (2025). AI literacy and competency: Definitions, frameworks, development and future research directions. Interactive Learning Environments, 33(5), 3225–3229.
7. Chiu, T. K., Ahmad, Z., Ismailov, M., & Sanusi, I. T. (2024). What are artificial intelligence literacy and competency? A comprehensive framework to support them. Computers and Education Open, 6(1), 1–11.
8. Chou, C.-C., & ChanLin, L.-J. (2019). Personalized learning supported by AI: A case study in motivating learners. Educational Technology Research and Development, 67(4), 855–871.

9. Chuang, S. (2021). The applications of constructivist learning theory and social learning theory on adult continuous development. Performance Improvement, 60(3), 6–14. https://doi.org/10.1002/pfi.21963
10. Dalgıç, A., Yaşar, E., & Demir, M. (2024). ChatGPT and learning outcomes in tourism education: The role of digital literacy and individualized learning. Journal of Hospitality, Leisure, Sport & Tourism Education, 34(1), 1–18. https://doi.org/10.1016/j.jhlste.2024.100481
11. Demir, M. (2025). Integrating artificial intelligence into decision processes: A dual role of psychological ownership and emotional intelligence. International Journal of Human–Computer Interaction, 42(10), 1–15. https://doi.org/10.1080/10447318.2025.2595308
12. Dewi, A. C., Maulana, A. A., Nururrahmah, A., Ahmad, A., & Naufal, A. M. F. (2023). The role of technological advancement in the world of education. Journal on Education, 6(1), 9725–9734.
13. Ding, L., Kim, C., & Orey, M. (2017). Studies of student engagement in gamified online discussions. Computers & Education, 115(1), 126–142.
14. Eccles, J. S., & Wigfield, A. (2020). From expectancy-value theory to situated expectancy-value theory: A developmental, social cognitive, and sociocultural perspective on motivation. Contemporary Educational Psychology, 61(1), 1–16.
15. Fauzi, M., & Amalia, R. (2023). Analysis of the impact of AI on student learning: A case study of universities in Indonesia. Journal of Educational Innovation and Learning, 12(1), 45–60.
16. Fitri, W. A., & Dilia, M. H. H. (2024). Optimization of AI technology in enhancing learning effectiveness. Sindoro: Cendikia Pendidikan, 5(11), 11–20.
17. Fredricks, J. A., & McColskey, W. (2012). The measurement of student engagement: A comparative analysis of various methods and student self-report instruments. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of Research on Student Engagement (pp. 763–782). Springer. https://doi.org/10.1007/978-1-4614-2018-7_37
18. Fredricks, J. A., Blumenfeld, P., Friedel, J., & Paris, A. (2005). School engagement. In K. A. Moore & L. H. Lippman (Eds.), What Do Children Need to Flourish? Conceptualizing and Measuring Indicators of Positive Development (Vol. 3, pp. 305–321). Springer Science+Business Media, LLC.
19. Garmston, R., & Wellman, B. (1994). Insights from constructivist learning theory. Educational Leadership, 51(7), 84–86.
20. Gilster, P., & Glister, P. (1997). Digital literacy. New York: Wiley Computer Pub.
21. Guthrie, J. T., Wigfield, A., & You, W. (2012). Instructional contexts for engagement and achievement in reading. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of Research on Student Engagement (pp. 601–634). Springer. https://doi.org/10.1007/978-1-4614-2018-7_29
22. Halpern, D. F., & Dunn, D. S. (2021). Critical thinking: A model of intelligence for solving real-world problems. Journal of Intelligence, 9(2), 22–34.
23. Hein, G. E. (1991). Constructivist learning theory. New York: Lesley College Publication.
24. Huang, F., & Derakhshan, A. (2025). Learning motivation and digital literacy in AI adoption for self-regulated English learning. European Journal of Education, 60(4), 1–14.
25. Hung, C. M., Hwang, G. J., & Huang, I. (2012). A project-based digital storytelling approach for improving students' learning motivation, problem-solving competence and learning achievement. Journal of Educational Technology & Society, 15(4), 368–379.
26. Jang, E., Seo, Y. S., & Brutt-Griffler, J. (2023). Building academic resilience in literacy: Digital reading practices and motivational and cognitive engagement. Reading Research Quarterly, 58(1), 160–176.
27. Melisa, R., Ashadi, A., Triastuti, A., Hidayati, S., Salido, A., Ero, P. E. L., ... & Al Fuad, Z. (2025). Critical thinking in the age of AI: A systematic review of AI's effects on higher education. Educational Process: International Journal, 14(2), 1–15.
28. Miao, F., & Shiohira, K. (2024). AI Competency Framework for Students. New York: UNESCO Publishing.
29. Naumann, J. (2015). A model of online reading engagement: Linking engagement, navigation, and performance in digital reading. Computers in Human Behavior, 53(2), 263–277. https://doi.org/10.1016/j.chb.2015.06.051
30. Nikou, S., De Reuver, M., & Mahboob Kanafi, M. (2022). Workplace literacy skills—how information and digital literacy affect adoption of digital technology. Journal of Documentation, 78(7), 371–391.
31. Pangrazio, L., & Sefton-Green, J. (2021). Digital rights, digital citizenship and digital literacy: What’s the difference?. Journal of New Approaches in Educational Research, 10(1), 15–27.
32. Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annual Review of Psychology, 63(1), 539–569. https://doi.org/10.1146/annurev-psych-120710-100452
33. Qiao, S., Yeung, S. S. S., Zainuddin, Z., Ng, D. T. K., & Chu, S. K. W. (2023). Examining the effects of mixed and non-digital gamification on students' learning performance, cognitive engagement and course satisfaction. British Journal of Educational Technology, 54(1), 394–413.
34. Reddy, P., Sharma, B., & Chaudhary, K. (2020). Digital literacy: A review of literature. International Journal of Technoethics, 11(2), 65–94.
35. Saiz, C., & Rivas, S. F. (2023). Critical thinking, formation, and change. Journal of Intelligence, 11(1), 2–19.
36. Santoso, R., & Dewi, A. (2021). The effectiveness of AI applications in supporting the completion of academic tasks. Journal of Information Technology Education, 9(2), 112–120.
37. Sharma, S., & Gupta, B. (2023). Investigating the role of technostress, cognitive appraisal and coping strategies on students’ learning performance in higher education: A multidimensional transactional theory of stress approach. Information Technology & People, 36(2), 626–660. https://doi.org/10.1108/ITP-06-2021-0505
38. Syafitri, N., & Hasanah, N. (2022). The use of AI technology in distance learning. Indonesian Journal of Educational Technology, 10(3), 85–95.
39. Tinmaz, H., Lee, Y. T., Fanea-Ivanovici, M., & Baber, H. (2022). A systematic review on digital literacy. Smart Learning Environments, 9(1), 21–34.
40. Widiana, A. E., Ashiyam, A. C., & Qulbi, D. A. N. (2025). The utilization of artificial intelligence in improving learning effectiveness. National Education Proceedings: LPPM IKIP PGRI Bojonegoro, 6(1), 279–289.
41. Widodo, Y. B., Sibuea, S., & Narji, M. (2024). Artificial intelligence in education: Enhancing personalized learning. Journal of Information Technology and Computer, 10(2), 602–615.
42. Wigfield, A. (1994). Expectancy-value theory of achievement motivation: A developmental perspective. Educational Psychology Review, 6(1), 49–78.
43. Wigfield, A., & Eccles, J. S. (2000). Expectancy–value theory of achievement motivation. Contemporary Educational Psychology, 25(1), 68–81.
44. Zajda, J. (2021). Globalisation and Education Reforms: Creating Effective Learning Environments: Constructivist Learning Theory and Creating Effective Learning Environments. Berlin: Springer International Publishing. https://doi.org/10.1007/978-3-030-71575-5_3