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

Synthesis: Banihashem, Bond, Bergdahl, Khosravi, and Noroozi systematically map the intersection of AI and self-regulated learning (SRL), reviewing 84 studies via a systematic mapping review across Web of Science, Scopus, IEEE Xplore, ACM Digital, EBSCOHost, Google Scholar, and OpenAlex. Using the "Who–What–How–Why" framework (stakeholders, theory, methods, objectives), they find AI–SRL research predominantly targets higher education students with minimal attention to primary education and educators. AI is implemented primarily as an intervention — through adaptive systems and personalization, prediction and profiling, intelligent tutoring systems, and assessment and evaluation. The direct impact of AI on SRL focuses mainly on the metacognitive and cognitive aspects, while the motivational aspect of SRL remains underexplored, and over one-third of studies did not specify an SRL theory.

Key Findings

• 84 studies mapped across six years. The review covers AI–SRL research growth over roughly 2019–2025, examining study levels, participant demographics, and geographical distribution.
• Higher-ed dominance, primary-ed gap. AI–SRL research predominantly focuses on higher education students; there is minimal attention to primary education and to educators as stakeholders.
• AI as intervention, in four main forms. AI is primarily implemented as an intervention to support students' SRL and learning processes: (1) adaptive systems and personalization, (2) prediction and profiling, (3) intelligent tutoring systems, and (4) assessment and evaluation.
• Metacognitive and cognitive focus, motivational gap. The direct impact of AI on SRL is concentrated on the metacognitive and cognitive aspects, while the motivational aspect of SRL remains underexplored — a key research gap.
• Theory often unspecified. Over one-third of the AI–SRL studies did not specify an SRL theory, pointing to a need for stronger theoretical grounding.
• A holistic research-and-practice agenda. The review identifies both research and practical gaps in the AI–SRL nexus to guide future work.

Implications

This mapping review is a central reference for the knowledge base's Self Regulated Learning thread in the AI era. It clarifies who AI–SRL research targets (mostly higher-ed students, not younger learners or educators), how AI is deployed to support SRL (as adaptive/personalized systems, predictive tools, tutors, and assessment), and where the gaps lie — most notably the underexplored motivational dimension and the frequent lack of explicit SRL theory. For designers and researchers, it argues for building AI support that addresses not just the cognitive and metacognitive Regulation of learning but also learners' Motivation, Self Efficacy, and engagement, and for grounding interventions in explicit SRL frameworks. It connects directly to Adaptive Learning, Feedback, and AI Education concepts.

Connected Concepts

• Self Regulated Learning — the core construct under study
• AI Education — the umbrella field
• Higher Ed — the dominant research context
• Adaptive Learning — a primary AI implementation form
• Intelligent Tutoring — AI as tutoring to support SRL
• Metacognition — the SRL aspect most studied
• Motivation — the underexplored SRL aspect
• Feedback — AI-delivered feedback supporting SRL
• Student Modeling — prediction and profiling of learners
• Assessment — AI-based assessment and evaluation

Connected Articles

• Mejeh Fromm SRL Adaptive Learning Feedback 2026 — Adaptive learning technology, differentiated feedback, and SRL phases

Citation

Banihashem, S. K., Bond, M., Bergdahl, N., Khosravi, H., & Noroozi, O. (2025). A systematic mapping review at the intersection of artificial intelligence and self-regulated learning. International Journal of Educational Technology in Higher Education, 22, 50.