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Browse the scientific research and professional literature used across all seven volumes of The Science of Learning for Educators.
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- Vol 5 RE: Chapter 8
artificial intelligenceself-regulated learninglearner agencylearning analyticsadaptive feedbacktheoretical alignmentethical reportingJournal article Evidence summaryAt a glance
This systematic mapping review analyses 84 studies at the intersection of artificial intelligence and self-regulated learning.
Key ideas
Using a framework organised around who is involved, what theories and constructs are addressed, how the research is conducted, and why AI is applied, the authors map publication patterns, stakeholders, methods, technologies, and objectives. The review finds rapid growth but uneven theoretical grounding and limited attention to some phases and actors in self-regulation. Much work focuses on higher education and on using learner data to predict, recommend, prompt, or provide feedback. The authors call for clearer links between AI functions and established self-regulated-learning theory, broader contexts, stronger ethical and methodological reporting, and more attention to learner agency.
Why it matters
For educators, the review offers a structured way to judge whether an AI tool genuinely supports planning, monitoring, reflection, or adaptation.
A transparent research trail
More than a bibliography.
What it contributes
Evidence summaries explain the central idea and why the source matters to educators.
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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
Citation checks
References are checked against publisher records, scholarly indexes, DOI or ISBN metadata, repositories and author records where available. An entry marked [Citation not verified] preserves the wording found in the relevant volume without attributing findings to an unconfirmed work.
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