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2 references
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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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- Vol 4 RE: Chapters 1, 2, 4, 8, 13, 25, App A, App C
- Vol 4 TE: App C
- Vol 5 RE: Chapters 2, 3, 5, 6, 13, 14, 15, App B
- Vol 5 TE: App C
- Vol 7 PE: App D
- Vol 7 RE: Chapters 2, 7, App D
self-regulated learningforethought phaseperformance phaseself-reflectiongoal settingstrategic planningcausal attributionBook chapter Evidence summaryAt a glance
Zimmerman defines self-regulation as context-sensitive processes through which people pursue goals, not as an inherited stage, general trait, or purely metacognitive skill. His social-cognitive model is cyclical.
Key ideas
Forethought includes task analysis, goal setting, strategic planning, self-efficacy, outcome expectations, interest, and goal orientation. During performance, learners deploy strategies, manage attention, use self-instruction or imagery, structure environments, and observe their actions and conditions. Self-reflection then combines self-evaluation and causal attribution with satisfaction and adaptive or defensive reactions. These reactions feed the next cycle, so interpretations of success and failure influence later goals, effort, and strategy choice. The chapter also distinguishes proactive regulation, in which learners set goals and create favourable conditions, from reactive attempts made only after difficulty occurs. Social models, instruction, feedback, and guided practice contribute to development. This is a theoretical synthesis of research, not one intervention trial.
Why it matters
For teaching, it supports modelling strategies, helping students set specific proximal goals, making performance information visible, prompting defensible attributions, and revising approaches after reflection. Difficulties should be diagnosed across the cycle rather than attributed to motivation or ability alone.
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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
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