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2 references
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- Vol 5 RE: Chapter 15
generative artificial intelligencefeedback engagementfeedback literacystudent agencyfeedback processingfeedback enactmentcritical ai literacyJournal article Evidence summaryAt a glance
Zhan, Boud, Dawson, and Yan develop a conceptual framework for understanding student engagement with feedback in generative-AI environments. Drawing on feedback research and an ecological perspective, they examine three stages: eliciting feedback, processing it, and enacting it in subsequent work.
Key ideas
Generative AI can reduce barriers of time, access, and social anxiety by providing rapid, repeatable, personalized responses. Yet useful engagement depends on students’ feedback literacy: they must formulate productive requests, judge credibility and relevance, compare advice with standards, and decide what to implement. Risks include fabricated or biased information, weak prompts, passive acceptance, reduced human dialogue, and ethical concerns. The authors propose a cyclical model of feedback forethought, control, and retrospect, emphasizing interaction between the opportunities a tool affords and a learner’s capacity to recognize and use them.
Why it matters
This is a theory-building synthesis, not an experimental test of learning gains. Its practical message is that institutions should design feedback environments and teaching activities that develop judgment, agency, and critical AI literacy while preserving meaningful teacher and peer relationships.
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- Vol 4 RE: Chapter 12
- Vol 4 RE: Chapter 6
- Vol 4 RE: Chapters 18, 25, App A, App C
artificial intelligenceself-regulated learningadaptive learning platformspersonalized guidancelearning analyticsai literacyteacher orchestrationJournal article Evidence summaryAt a glance
Zhu and Sari systematically review 25 empirical studies of artificial-intelligence applications intended to support self-regulated learning. The included tools span chatbots, intelligent tutoring systems, adaptive platforms, electronic books, and educational games, and address planning, performance, monitoring, reflection, and iteration in different ways.
Key ideas
The synthesis finds promising support for personalized guidance, feedback, engagement, and strategy use, but also recurring problems involving inaccurate outputs, limited adaptability, behavioral tracking, weak AI literacy, and possible negative effects or dependency. The authors propose an AI–SRL framework with two connected layers. The learner layer follows forethought, performance, self-reflection, and iteration; the instructor layer includes teaching AI literacy, integrating tools into purposeful activities, providing continuing support, monitoring progress, and improving designs over time. This makes teacher orchestration part of the model rather than assuming AI independently produces self-regulation.
Why it matters
The review is limited to English-language studies found in three databases and a still-small, heterogeneous evidence base. Its conclusions should guide careful design and further testing, not be read as proof that AI tools uniformly improve self-regulated learning.
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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
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