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2 references
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- Vol 5 TE: App C
learning portfoliosstudent reflectionportfolio selectionassessment criteriagrowth over timestudent agencylearning evidenceJournal article Evidence summaryAt a glance
Paulson, Paulson, and Meyer distinguish a genuine learning portfolio from an arbitrary folder of completed work. A portfolio is a purposeful collection showing a learner’s efforts, progress, and achievements; students participate in selecting contents, identify criteria for selection and quality, and reflect on the evidence.
Key ideas
The authors describe eight characteristics. Portfolios should contain self-reflection, involve students in choosing entries, remain distinct from cumulative records, convey the learner’s activities and development, and show growth through dated or sequenced work. Their purpose may change during the year, but students should control what becomes public. Portfolios also need information that allows readers to understand the work in context and should encourage students to see patterns in their learning.
Why it matters
The central value lies in joining assessment with instruction and making students participants rather than objects of evaluation. For educators, the implication is that simply digitising or collecting assignments is insufficient: a portfolio requires an explicit purpose, selection decisions, criteria, commentary, comparison over time, and dialogue about next steps.
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- Vol 5 RE: Chapter 15
generative artificial intelligenceai assessment scaleassessment validityhuman learning evidenceai-assisted assessmentassessment redesignacademic integrityPreprint Evidence summaryAt a glance
Perkins, Roe, and Furze revise the AI Assessment Scale as both a communication tool and a framework for redesigning assessment. It retains five levels, ranging from no generative-AI use through increasingly integrated use to AI exploration, but replaces the original traffic-light visual with neutral presentation so that levels are not mistaken for a hierarchy of quality.
Key ideas
Each level clarifies what students may do with AI and what evidence of human learning the task should preserve. The revision grounds the framework in social constructivism and assessment validity, incorporates feedback from implementation across school and higher-education settings, and addresses expanding multimodal capabilities. Practical vignettes illustrate how institutions and teachers can adapt the scale rather than impose identical rules on every subject or outcome. The authors warn against using it as a surveillance device, a universal policy, or a substitute for assessment design.
Why it matters
For educators, its strongest use is to make expectations explicit and reconsider whether a task still elicits valid evidence of the intended learning when AI assistance is available.
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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
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