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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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- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
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