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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.
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.
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.
Winstone and Carless shift feedback from information delivered by teachers to a process in which students interpret, judge, and use information to improve work. Effective feedback depends on student feedback literacy, opportunities for uptake, and assessment designs making action possible.
The book examines how educators can develop evaluative judgement, enable dialogue, connect internal feedback with external comments, use technology purposefully, and organise peer feedback as learning rather than editing. It foregrounds relationships: trust, tone, emotion, credibility, and agency affect whether information is engaged with. Teacher feedback literacy includes designing for uptake while managing disciplinary, institutional, time, and workload constraints. The volume synthesises theory, research, and cases; it is not one comparative trial and does not imply that more comments produce more learning.
Useful designs create cycles in which learners compare work with criteria and exemplars, discuss interpretations, revise, and carry insights into later tasks. Educators should evaluate feedback by the quality of resulting learner action, not only message timeliness or volume, while ensuring that peer, automated, and teacher inputs serve coherent learning purposes.
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