Iranian Journal of Learning and Memory

Conceptual Transformation of Learning in the Era of Artificial Intelligence: A Systematic Review of 2024-2025 Studies

Document Type : Original Article

Author

Javad Eynypour

Associate professor of educational psychology, Department of Psychology and educational sciences, Faculty of organizational resources, Amin Police University, Tehran, Iran

https://doi.org/10.22034/iepa.2026.567072.1566
Abstract
Objective: This review examined recent studies (2024–2025) to identify new conceptualizations of learning in the AI era, characterized by hybrid human-system collaboration, real-time personalization using multimodal data, interpretability, and a shift from teacher-centered to learner- and technology-driven approaches.
Method: Nine peer-reviewed studies from 2024–2025 were analyzed thematically, sourced from databases including Scopus, Web of Science, PubMed, Google Scholar, and ResearchGate using keywords like "learning definition", "conceptualization of learning", "AI in education" and "hybrid intelligence."
Result: The findings reveal that learning in the AI era is conceptualized as a dynamic, adaptive, and multidimensional process integrating cognitive, emotional, social, and interactive elements. Emerging theoretical frameworks include hybrid intelligence emphasizing human-system co-evolution, the integration of cognitive load theory with AI and educational neuroscience, and generalized online learning models prioritizing interpretability and continuous autonomous learning. Key novel features encompass multimodal learning leveraging neurophysiological tools, real-time adaptive pathways, human-machine collaborative processes, incorporation of behavioral and interactional data alongside neurophysiological indicators, and a strong emphasis on transparency and interpretability
Conclusions: The core transformation is a paradigm shift toward learner- and technology-centered models, with social interaction gaining prominence in AI-mediated settings. While AI enables personalized feedback and dynamic adaptation, its limitations in context and causal reasoning underscore the necessity of human-AI synergy. Practical implications highlight the need for educator training in new technologies and careful attention to ethics and equitable access. These insights derive from a constrained sample of nine studies and may not encompass the entire scope of research on learning during 2024–2025.

Keywords

Learning
Artificial intelligence
Conceptual Transformation of learning
Hybrid Learning
Subjects
Adel Yar, S. (2025). E-learning ecosystem for higher education in Afghanistan. Journal of Natural Sciences (Founders: Kabul University), 3(3), 11-19. https://doi.org/10.62810/jns.v3i3.177  
Ayonrinde, K. (2025). Position: Interpretability is a bidirectional communication problem. Openreview.net. https://openreview.net/forum?id=O4LaRH4zSI 
Castillo-Martínez, L. M., Flores-Bueno, D., Gómez-Puente, S. M., & Vite-León, V. O. (2024). AI in higher education: A systematic literature review. Frontiers in Education, 9. https://doi.org/10.3389/feduc.2024.1391485 
Cukurova, M., Luckin, R., & Clark Wilson, A. (2024). The interplay of learning, anal ytics and artificial intelligence in education. British Journal of Educational Technology, 55(4), 1234–1256. https://doi:10.1111/bjet.13514   
Gkintoni, E., Antonopoulou, H., Sortwell, A., & Halkiopoulos, C. (2025). Challenging Cognitive Load Theory: The role of educational neuroscience and artificial intelligence in redefining learning efficacy. Brain sciences, 15(2), 203. https://doi.org/10.3390/brainsci15020203
Gkintoni, E., Papamitsiou, Z., & Giannakos, M. (2025). Neuro physiologically guided adaptive learning: Integrating cognitive load theory, AI, and educational neuroscience. SSRN Electronic Journal. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5261831 
Jasim Imran, A., Sajjadian, I., Al-Mamouri, A. H. M., & Nadi, M. A. (2024). The Relationship between Self-directed Learning and Problem-Solving: The mediating role of Self-regulated Learning and the Moderating Role of Gender among University Students. Iranian Journal of Learning and Memory, 7(27), 5-21. https://doi:10.22034/iepa.2025.493481.1510
Järvelä, S., Zhao, G., Nguyen, A., & Chen, H. (2025). Hybrid intelligence: Human– AI coevolution and learning. British Journal of Educational Technology, 56(2), 455–468. https://doi.org/10.1111/bjet.13560 
Lan, M., & Zhou, X. (2025). A qualitative systematic review on AI empowered self-regulated learning in higher education. npj Sci. Learn., 10, 21 (2025). https://doi.org/10.1038/s41539-025-00319-0 
Masih, M., Suleman, S., Khan, M. H., Sahito, Z., & Shahid, S. (2025). The future classroom: Integrating AI and social media for adaptive learning.  Journal of Social Sciences, 4(3), 98-111. https://doi.org/10.63544/ijss.v4i3.150
Miconi, T., & Kay, K. (2025). Neural mechanisms of relational learning and fast knowledge reassembly in plastic neural networks. Nat Neurosci, 28, 406–414. https://doi.org/10.1038/s41593-024-01852-8
Мінцер, О. П. (2024). Процеси навчання в технологіях штучного інтелекту. Medical Informatics and Engineering, 1(2), 4-13, . https://doi.org/10.11603/mie.1996-1960.2024.1-2.14993
Mohmiddin, N. A. M., & Bakar, R. A. (2025). The role of Artificial intelligence in learning Arabic For Non-Native Speakers: A Systematic Literature Review. Al-Qanatir: International Journal of Islamic Studies, 34(3), 198-216. https://doi.org/10.64757/alqanatir.2025.3403/1121
Rodrigues, D. (2025). AI in education: From cognitive enhancement to hybrid intelligence. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5327624papers.ssrn
Soroosh,Z. , Ebrahimighavam,O., & Pourhosein, M. (2024). Leveraging metacognitive approaches to improve educational performance. Iranian Journal of Learning and Memory, 7(28), 5-24. https://doi:10.22034/iepa.2025.526880.1537 
Srisuchinnawong, A., & Manoonpong, P. (2025). Growable and interpretable neural control with online continual learning for autonomous lifelong locomotion learning machines. The International Journal of Robotics Research, 02783649251336385. https://doi.org/10.1177/02783649251336385
Tan, J. L., Taha, B. A., Aziz, N. A., Mokhtar, M. H. H., Mukhlisin, M., & Arsad, N. (2025). A Review of Reinforcement Learning Evolution: Taxonomy, Challenges and Emerging Solutions. International Journal of Advanced Computer Science and Applications, 16(1), 490-502. https://doi.org/10.14569/ijacsa.2025.0160149
Temimi, A., Rebai, R., Aldamen, H., Souki, K., & Amiri, K. (2025). Education at a crossroads: will AI fix learning or break it? Management & Sustainability: An Arab Review, 1–25. https://doi.org/10.1108/MSAR-04-2025-0118
U.S. Department of Education, Office of Educational Technology. (2024). Artificial intelligence and the future of teaching and learning: Insights and recommendations. U.S. Department of Education. https://www.ed.gov/sites/ed/files/documents/ai-report/ai-report.pdf
Wang, S., Tao, X., Ma, H., Li, F., & Wu, C. (2025). EEG assessment of artificial intelligence-generated content impact on student creative performance and neurophysiological states in product design. Frontiers in Psychology, 16, 1508383–1508383. https://doi.org/10.3389/fpsyg.2025.1508383 
Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 252, 124167. https://doi.org/10.1016/j.eswa.2024.124167
Xia, X., & Qi, W. (2023). Interpretable early warning recommendations in interactive learning environments: a deep-neural network approach based on learning behavior knowledge graph. Humanities and Social Sciences Communications, 10(1). https://doi.org/10.1057/s41599-023-01739-2
Younas, M., El-Dakhs, D. A. S., & Noor, U. (2025). The impact of artificial intelligence-based learning tools in academic innovation: a review of Deep seek, GPT, and Gemini (2020–2025). Frontiers in Education, 10. https://doi.org/10.3389/feduc.2025.1689205
Iranian Journal of Learning and Memory
Volume 8, Issue 31
September 2025
Pages 37-48

Home

Editorial Board

Submit Manuscript

Contact Us