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3 references
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- Vol 5 RE: Chapters 12, App C
authentic assessmenthigher education assessmentemployability skillscomplex performance tasksfeedback literacylearner agencyassessment alignmentinclusive assessmentJournal article Evidence summaryAt a glance
Vlachopoulos and Makri review 94 articles on authentic assessment in higher education, focusing on implementation, development of employability-oriented “21st-century” skills, and implications for students, educators, institutions, and policy. Authentic tasks ask learners to apply knowledge, skills, and attitudes in complex situations resembling relevant professional or real-world practice rather than reproduce information in decontextualised tests.
Key ideas
Across disciplines, reported benefits include problem solving, critical and creative thinking, collaboration, communication, self-regulation, engagement, feedback literacy, and links between academic learning and future work. Effective designs use clear criteria, realistic complexity, meaningful audiences or products, iterative feedback, reflection, learner agency, and alignment among outcomes, teaching, and assessment. Challenges include workload, scalability, consistency and reliability, student resistance, unfamiliarity, unequal resources, accessibility, technology, academic integrity, staff expertise, and institutional rules. The review calls for training, adequate time and infrastructure, stakeholder involvement, inclusive design, quality assurance, and policies flexible enough to support disciplinary variation.
Why it matters
Because the evidence consists largely of heterogeneous reports and perceptions, authentic appearance alone should not be assumed to cause transferable skills; designs need explicit goals and credible evaluation of learning, equity, and longer-term outcomes.
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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 7 RE: Chapter 4
artificial intelligence applicationshigher educationprofiling and predictionadaptive learning systemsintelligent tutoring systemseducational data ethicsinterdisciplinary researchJournal article Evidence summaryAt a glance
Zawacki-Richter and colleagues systematically review artificial-intelligence research in higher education published from 2007 through 2018. From 2,656 records, they include 146 studies and classify applications into four areas: profiling and prediction, assessment and evaluation, adaptive systems and personalisation, and intelligent tutoring systems.
Key ideas
Much of the literature comes from computer science and STEM contexts, with limited participation by educators and little connection to pedagogical theory. Studies often emphasise technical performance while giving less attention to teaching practice, learner perspectives, ethics, privacy, or broader consequences. The review maps an emerging field; it does not establish that AI applications generally improve learning, and its search period predates generative AI. The authors call for interdisciplinary collaboration, theoretically informed educational questions, and critical attention to risks and benefits.
Why it matters
Institutions and researchers should involve educators and students in problem definition, evaluate educational value rather than model accuracy alone, make data and decisions accountable, and study inclusion, agency, privacy, and possible harms alongside effectiveness. Later AI developments require new evidence rather than automatic extrapolation from this corpus.
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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
Citation checks
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