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4 references
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- Vol 5 RE: Chapter 15
generative artificial intelligencehigher education assessmentassessment designprocess-focused assessmentcareer-oriented competencieslifelong learning skillsresponsible ai usemixed-method researchJournal article Evidence summaryAt a glance
Weng and colleagues review 34 studies connecting generative AI, higher-education assessment, and learning outcomes. Using a five-stage scoping-review framework, they identify three approaches: traditional assessment, innovative or refocused assessment, and GenAI-incorporated assessment.
Key ideas
They highlight two emerging outcome clusters—career-oriented competencies and lifelong-learning skills—reflecting emphasis on working productively and critically with AI. Most included studies use qualitative, exploratory, descriptive, ethnographic, or phenomenological designs. The resulting map describes research directions rather than establishing that one approach improves learning. The authors argue that traditional methods alone are poorly suited to contexts where students can generate polished products, while authentic, process-focused, and AI-incorporated designs may make learning more visible. They call for research on combinations of approaches, relationships between designs and new outcomes, and more quantitative and mixed-method studies.
Why it matters
For educators, the review supports clarifying which human learning an assessment should evidence, attending to process and product, and teaching responsible AI use. Institutions should avoid replacing educational judgement with detection and instead align policy, graduate capabilities, task design, and evidence of learning.
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- Vol 4 RE: Chapter 25
generative artificial intelligenceself-regulated learningforethought processesperformance processesself-reflectionpersonalized goalsstrategy generationhuman–machine collaborationJournal article Evidence summaryAt a glance
Xia, Liu, Tlili, and Chiu systematically review how generative AI can support self-regulated learning activities.
Key ideas
They analyze seventy-three articles and map GenAI use across forethought, performance, and self-reflection. Six pedagogical affordances emerge: creating personalized goals; searching, analyzing, and integrating resources; monitoring and evaluating progress; recommending strategies; recording progress and providing feedback; and generating new ideas or examples. Common activities include information searching during forethought, strategy generation for problem solving during performance, and feedback and self-assessment during reflection. The review also separates individual influences, such as prior knowledge and motivation, from environmental influences, including teacher support and task design.
Why it matters
Its central design message is that GenAI should function as a human–machine collaborative scaffold for regulation, not simply an answer generator, and that activities must preserve learner monitoring, judgement, and agency.
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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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