Psychological Adaptation and Affective Feedback in AI-Enhanced Education: A Systematic Review
Keywords:
artificial intelligence in education, affective computing, psychological adaptationAbstract
This review synthesizes current research at the intersection of artificial intelligence (AI), education, and psychology, with a particular focus on the psychological mechanisms underlying learner adaptation. Evidence from affective computing illustrates the pedagogical value of multimodal emotion recognition, while meta-analyses of large language model (LLM)-based tutoring systems reveal both the potential for enhanced engagement and the risks of cognitive dependency. Empirical studies on adoption further highlight the mediating roles of self-efficacy, motivation, and anxiety in sustaining learner interaction with AI tools. Case analyses identify progressive adaptation, emotional regulation, and transparency as critical enablers of effective learning experiences. The review concludes that ethically grounded, human-centered AI design is indispensable for cultivating resilient, adaptive, and learner-centered educational ecosystems.
References
1. L. Yang, “The evolution of ballet pedagogy: A study of traditional and contemporary approaches,” Journal of Literature and Arts Research, vol. 2, no. 2, pp. 1–10, 2025, doi: 10.71222/2nw5qw82.
2. S. Yu, A. Androsov, H. Yan, and Y. Chen, "Bridging computer and education sciences: A systematic review of automated emotion recognition in online learning environments," Computers & Education, vol. 220, p. 105111, 2024. doi: 10.1016/j.compedu.2024.105111.
3. Y. M. E. Heung, and T. K. Chiu, "How ChatGPT impacts student engagement from a systematic review and meta-analysis study," Computers and Education: Artificial Intelligence, vol. 8, p. 100361, 2025. doi: 10.1016/j.caeai.2025.100361.
4. R. Deng, M. Jiang, X. Yu, Y. Lu, and S. Liu, "Does ChatGPT enhance student learning? A systematic review and meta-analysis of experimental studies," Computers & Education, vol. 227, p. 105224, 2025. doi: 10.1016/j.compedu.2024.105224.
5. T. Budhathoki, A. Zirar, E. T. Njoya, and A. Timsina, "ChatGPT adoption and anxiety: a cross-country analysis utilising the unified theory of acceptance and use of technology (UTAUT)," Studies in Higher Education, vol. 49, no. 5, pp. 831-846, 2024. doi: 10.1080/03075079.2024.2333937.
6. R. Yuvaraj, R. Mittal, A. A. Prince, and J. S. Huang, "Affective Computing for Learning in Education: A Systematic Review and Bibliometric Analysis," Education Sciences, vol. 15, no. 1, p. 65, 2025. doi: 10.3390/educsci15010065.
7. B. Klimova, and M. Pikhart, "Exploring the effects of artificial intelligence on student and academic well-being in higher education: A mini-review," Frontiers in Psychology, vol. 16, p. 1498132, 2025. doi: 10.3389/fpsyg.2025.1498132.
8. A. O. R. Vistorte, A. Deroncele-Acosta, J. L. M. Ayala, A. Barrasa, C. López-Granero, and M. Martí-González, "Integrating artificial intelligence to assess emotions in learning environments: a systematic literature review," Frontiers in psychology, vol. 15, p. 1387089, 2024. doi: 10.3389/fpsyg.2024.1387089.
9. C. Llurba, and R. Palau, "Real-Time Emotion Recognition for Improving the Teaching-Learning Process: A Scoping Review," Journal of Imaging, vol. 10, no. 12, p. 313, 2024. doi: 10.3390/jimaging10120313.
10. J. A. Reyes-Portillo, A. So, K. McAlister, C. Nicodemus, A. Golden, C. Jacobson, and J. Huberty, "Generative AI-Powered Mental Wellness Chatbot for College Student Mental Wellness: Open Trial," JMIR Formative Research, vol. 9, no. 1, p. e71923, 2025.
11. X. Tan, G. Cheng, and M. H. Ling, "Artificial intelligence in teaching and teacher professional development: A systematic review," Computers and Education: Artificial Intelligence, vol. 8, p. 100355, 2025. doi: 10.1016/j.caeai.2024.100355.
12. R. Haubt, "AI in Education Writing and the Role of Turnitin,".
13. J. Fisher, L. K. Falk, and M. Mittelman, "STUDENT PERCEPTIONS OF AI IN UNIVERSITY STUDIES," Journal of International Business Disciplines, vol. 20, no. 1, 2025.
14. A. Almassaad, H. Alajlan, and R. Alebaikan, "Student perceptions of generative artificial intelligence: Investigating utilization, benefits, and challenges in higher education," Systems, vol. 12, no. 10, p. 385, 2024. doi: 10.3390/systems12100385.
15. S. Shifa, S. Hameed, H. Suhail, and A. Kiren, "Emotional Intelligence Meets Artificial Intelligence Affective Computing and Student Well-Being Online Classrooms," The critical review of social sciences studies, vol. 3, no. 3, pp. 271-289, 2025.
16. O. Zawacki-Richter, V. I. Marín, M. Bond, and F. Gouverneur, "Systematic review of research on artificial intelligence applications in higher education-where are the educators?," International journal of educational technology in higher education, vol. 16, no. 1, pp. 1-27, 2019.
17. A. Kapoor, and R. W. Picard, "Multimodal affect recognition in learning environments," In Proceedings of the 13th annual ACM international conference on Multimedia, November, 2005, pp. 677-682. doi: 10.1145/1101149.1101300.
18. E. Benamar Hilal, "Understanding Private Tutoring Online Platforms: A Deep Dive into Online Education Marketplaces," 2025.
19. V. M. Corza-Vargas, R. Martinez-Maldonado, B. Escalante-Ramirez, and J. Olveres, "Students' Ethical, Privacy, Design, and Cultural Perspectives on Visualizing Cognitive-Affective States in Online Learning," Journal of Learning Analytics, vol. 11, no. 3, pp. 24-40, 2024.
20. M. Benouis, E. Andre, and Y. S. Can, "Balancing Between Privacy and Utility for Affect Recognition Using Multitask Learning in Differential Privacy-Added Federated Learning Settings: Quantitative Study," JMIR Mental Health, vol. 11, no. 1, p. e60003, 2024.
21. Y. Chen, S. Liu, D. Zhao, and W. Ji, "Occlusion facial expression recognition based on feature fusion residual attention network," Frontiers in Neurorobotics, vol. 17, p. 1250706, 2023. doi: 10.3389/fnbot.2023.1250706.
22. P. D. Simon, and L. M. Zeng, "Behind the scenes of adaptive learning: A scoping review of teachers' perspectives on the use of adaptive learning technologies," Education Sciences, vol. 14, no. 12, p. 1413, 2024.
23. Z. Salma, R. Hijón-Neira, C. Pizarro, and A. Abdul Moqeet, "Effectiveness of Robot-Mediated Learning in Fostering Children's Social and Cognitive Development," Applied Sciences, vol. 15, no. 7, p. 3567, 2025. doi: 10.20944/preprints202503.0634.v1.
24. J. Wang, and W. Fan, "The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis," Humanities and Social Sciences Communications, vol. 12, no. 1, pp. 1-21, 2025. doi: 10.1057/s41599-025-04787-y.
25. N. Kosmyna, E. Hauptmann, Y. T. Yuan, J. Situ, X. H. Liao, A. V. Beresnitzky, and P. Maes, "Your brain on ChatGPT: Accumulation of cognitive debt when using an AI assistant for essay writing task," arXiv preprint arXiv:2506.08872, vol. 4, 2025.
26. Y. Wu, Q. Mi, and T. Gao, "A comprehensive review of multimodal emotion recognition: Techniques, challenges, and future directions," Biomimetics, vol. 10, no. 7, p. 418, 2025. doi: 10.3390/biomimetics10070418.
27. M. Mosleh, M. Devlin, and E. Solaiman, "Transparent Adaptive Learning via Data-Centric Multimodal Explainable AI," arXiv preprint arXiv:2508.00665, 2025.
28. S. Maity, and A. Deroy, "Human-Centric eXplainable AI in Education," arXiv preprint arXiv:2410.19822, 2024. doi: 10.35542/osf.io/k5u9b.
