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