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- Vol 4 RE: Chapters 12, 25, 27, App C
artificial intelligence chatbotsself-regulated learningadaptive guidanceco-regulationcritical reflectionlearner agencyemancipatory learninglearning analyticsJournal article Evidence summaryAt a glance
Wu and colleagues systematically review 78 studies published from 2018 to 2025 to ask how artificial-intelligence chatbots can be designed to support self-regulated learning. They organize design possibilities through Habermas’s technical, practical, and emancipatory knowledge interests.
Key ideas
Technical features can help learners set goals, obtain adaptive guidance, monitor progress, and receive timely feedback. Practical features emphasize dialogue, social interaction, shared understanding, and co-regulation rather than solitary tool use. Emancipatory features encourage learners to question assumptions, reflect critically, recognize power and bias, and retain agency over decisions. The resulting framework maps chatbot functions onto phases of self-regulation and shows that the literature concentrates more heavily on performance support than on deeper reflection or learner liberation. The review identifies gaps in K–12 settings, multimodal learning analytics, long-term evaluation, and designs that genuinely support emancipatory reflection.
Why it matters
Because the included studies vary in methods and outcomes, the synthesis is a design base rather than proof that any chatbot feature reliably improves achievement. Educators should align tools with learning purposes, provide human guidance, and evaluate both benefits and unintended dependency.
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- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
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