A systematic mapping review at the intersection of artificial intelligence and self-regulated learning
Open accessAbstract
Recently, artificial intelligence (AI) has increasingly been integrated into self-regulated learning (SRL), presenting novel pathways to support SRL. While AI-SRL research has experienced rapid growth, there remains a significant gap in understanding the intersection between AI and SRL, resulting in oversight when identifying critical areas necessitating additional research or practical attention. Building upon a well-established framework, from Chatti and colleagues, this systematic mapping review identified 84 studies through the Web of Science, Scopus, IEEE Xplore, ACM Digital, EBSCOHost, Google Scholar, and Open Alex, to explore the intersection of AI and SRL within the four key aspects—Who (stakeholders), What (theory), How (methods), and Why (objectives). The main results revealed that AI-SRL research predominantly focuses on higher education students, with minimal attention to primary education and educators. AI is primarily implemented as an intervention—through adaptive systems and personalization, prediction and profiling, intelligent tutoring systems, and assessment and evaluation—to support students' SRL and learning processes. The direct impact of AI on SRL was primarily focused on the metacognitive and cognitive aspects of SRL, while the motivational aspect of SRL remains underexplored. While over one-third of the AI-SRL s tudies did not specify an SRL theory, Zimmerman’s model of SRL was the most frequently applied among those that did. The use of AI in supporting SRL has extended beyond just focusing on and supporting SRL itself; it has also aimed to enhance various educational and learning activities as end outcomes such as improving academic performance, motivation and emotions, engagement, and collaborative learning. The results of this study extend our understanding of the effective application of AI in supporting SRL and optimizing educational outcomes. Suggestions for further research and practice are provided.
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- Authors
- Banihashem, S. K., Bond, M., Bergdahl, N., Khosravi, H., & Noroozi, O.
- Affiliations
- Open University of the Netherlands (NL)
- University College London (GB)
- Stockholm University (SE)
- The University of Queensland (AU)
- Wageningen University & Research (NL)
- Corresponding author
- Banihashem, Seyyed Kazem
- Venue
- International Journal of Educational Technology in Higher Education
- Published
- 2025-08-01
- Volume
- 22
- Issue
- 1
- ISSN
- 2365-9440 (ISSN-L)
- OpenAlex
- W4412798653
- Cited by
- 82
- References
- 132
- Citations by year
- 2026: 74 · 2025: 8
- FWCI
- 32.89
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- International Journal of Educational Technology in Higher Education (publishedVersion, cc-by)
- DSpace (Open University in the Netherlands) (publishedVersion, cc-by)
- Diva portal (Dalarna University Library) (submittedVersion, other-oa)
- Socio-Environmental Systems Modeling (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UCL Discovery (University College London) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Keywords
- artificial intelligence in education, student self-regulation, intelligent tutoring systems, adaptive learning systems, metacognition, student motivation, university students, personalized learning, academic performance, cooperative learning, research gaps
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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