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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 4 RE: Chapter 25
generative artificial intelligenceself-regulated learningforethought processesperformance processesself-reflectionpersonalized goalsstrategy generationhuman–machine collaborationJournal article Evidence summaryAt a glance
Xia, Liu, Tlili, and Chiu systematically review how generative AI can support self-regulated learning activities.
Key ideas
They analyze seventy-three articles and map GenAI use across forethought, performance, and self-reflection. Six pedagogical affordances emerge: creating personalized goals; searching, analyzing, and integrating resources; monitoring and evaluating progress; recommending strategies; recording progress and providing feedback; and generating new ideas or examples. Common activities include information searching during forethought, strategy generation for problem solving during performance, and feedback and self-assessment during reflection. The review also separates individual influences, such as prior knowledge and motivation, from environmental influences, including teacher support and task design.
Why it matters
Its central design message is that GenAI should function as a human–machine collaborative scaffold for regulation, not simply an answer generator, and that activities must preserve learner monitoring, judgement, and agency.
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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
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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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