A qualitative systematic review on AI empowered self-regulated learning in higher education
This systematic review explores the burgeoning intersection of Artificial Intelligence (AI) applications and self-regulated learning (SRL) in higher education. Aiming to synthesize empirical studies, we employed a qualitative approach to scrutinize AI's role in supporting SRL processes. Through a meticulous selection process adhering to PRISMA guidelines, we identified 14 distinct studies that leveraged AI applications, including chatbots, adaptive feedback systems, serious games, and e-textbooks, to support student autonomy. Our findings reveal a nuanced landscape where AI demonstrates potential in facilitating SRL's forethought, performance, and reflection phases, yet also highlights whether the agency is human-centered or AI-centered leading to variations in the SRL model. This review underscores the imperative for balanced AI integration, ensuring technological advantages are harnessed without undermining student self-efficacy. The implications suggest a future where AI is a thoughtfully woven thread in the SRL fabric of higher education, calling for further research to optimize this synergy.
Authors
- Min Lan (ORCID: https://orcid.org/0000-0003-3632-2389)
- Xiaofeng Zhou (ORCID: https://orcid.org/0000-0001-7512-388X)
Institutions
- Zhejiang Normal University (CN)
Publication Details
- Journal
- npj Science of Learning
- Published
- 2025-05-03
- DOI
- https://doi.org/10.1038/s41539-025-00319-0
- Citations
- 45
- Primary Topic
- Online Learning and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 76.21