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61 references
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- Vol 4 RE: Chapter 18
generative artificial intelligenceself-regulated learningutility valuecognitive strategiesstudent engagementphysics instructionenglish instructionteacher oversightJournal article Evidence summaryAt a glance
Fütterer and colleagues test whether customised generative-AI support can strengthen self-regulated learning in regular secondary classrooms. In a randomized trial, 371 students in grades seven to nine completed six physics or English sessions in one of three conditions: a CustomGPT targeting utility value, a CustomGPT prompting cognitive learning strategies, or standard ChatGPT.
Key ideas
The utility-value condition produced a more favourable change in perceived usefulness than the cognitive-strategy condition. Neither tailored intervention showed clear advantages over the control for effort, domain knowledge, or elaboration-based strategy use. Exploratory analyses suggested that more meaningful engagement with the AI was associated with better-maintained interest, and effects did not differ clearly by subject. The findings provide limited, differentiated evidence rather than showing that generic AI automatically improves self-regulation.
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Carefully designed prompts may support motivation, but strategic behaviour and learning outcomes require stronger instructional integration, reliable measures, and teacher oversight. The supplied citation calls this work a systematic review; the verified publication is an open-access randomized controlled trial.
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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
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