A systematic mapping review at the intersection of artificial intelligence and self-regulated learning

Guardat en:

Dades bibliogràfiques
Publicat a:	International Journal of Educational Technology in Higher Education vol. 22, no. 1 (Dec 2025), p. 50
Autor principal:	Banihashem, Seyyed Kazem
Altres autors:	Bond, Melissa, Bergdahl, Nina, Khosravi, Hassan, Noroozi, Omid
Publicat:	Springer Nature B.V.
Matèries:	Students Collaboration Mapping Educational technology Feedback Teachers Cognition & reasoning Adaptive systems Learning Artificial intelligence Education Metacognition Self regulation Collaborative learning Academic achievement Tutoring Higher education College students Research Emotions Learning processes Cognitive aspects Motivation Attention Elementary education Cooperative learning Educational activities Learning outcomes Profiles Learning Analytics Literature Reviews Educational Development Scaffolding (Teaching Technique) Stakeholders Self Efficacy Intelligent Tutoring Systems Influence of Technology Distance Education Cognitive Processes Educational Change Trust (Psychology) At Risk Students Short Term Memory Student Records Science Instruction Elementary Secondary Education Language Processing Theory Practice Relationship Algorithms
Accés en línia:	Citation/Abstract Full Text Full Text - PDF
Etiquetes:	Afegir etiqueta Sense etiquetes, Sigues el primer a etiquetar aquest registre!

Descripció
Resum:	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 studies 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.
ISSN:	2365-9440 1698-580X
DOI:	10.1186/s41239-025-00548-8
Font:	Political Science Database