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2 references
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- Vol 5 RE: Chapters 15, App C
generative artificial intelligencehigher-education assessmentautomated feedbackself-assessmentassessment literacyacademic integritystudent agencyinstitutional policyJournal article Evidence summaryAt a glance
Xia and colleagues scope empirical research on how generative AI is changing higher-education assessment. Following PRISMA-ScR procedures, they search ERIC, Web of Science, and Scopus, screen 969 records, and include 32 studies, mostly from 2023.
Key ideas
Findings are organised across students, teachers, and institutions. At student level, the literature reports opportunities for immediate and diverse feedback, self-assessment, and perceived impartiality, alongside academic-integrity concerns. For teachers, GenAI changes assessment literacy, beliefs about human and automated judgement, and task and feedback design. Institutions face pressure to reconsider policies, staff development, and interdisciplinary provision. The authors argue that assessment should cultivate self-regulated and responsible learning rather than focus only on detecting AI use. This forward-looking scoping review maps an early evidence base; it does not estimate effects or establish that proposed reforms improve learning, and some included studies discuss GenAI generally.
Why it matters
Practical implications include developing assessment, AI, and digital literacy; designing holistic tasks with student agency; clarifying ethical expectations; and aligning institutional policy, teacher capability, and educational purpose. Rapid technological change means these patterns require continuing empirical evaluation.
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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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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
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