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5 references
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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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- Vol 4 RE: Chapter 18
teacher agencyecological approachcurriculum policyprofessional judgementaccountability pressuresartificial intelligencedecision-making spaceBook Evidence summaryAt a glance
The second edition revisits Priestley, Biesta, and Robinson’s ecological account of teacher agency a decade after the original book.
Key ideas
Agency is treated as an achievement arising through the interaction of teachers’ capacities with cultural, structural, and material conditions, not as an individual possession. The revised volume adds an extensive review of research on the theory and conceptualization of agency and examines how changing curriculum policies, accountability pressures, professional discourse, social relations, and generative artificial intelligence shape what teachers can do. Its temporal model connects professional histories, future aspirations, and practical judgement in present contexts. The authors use this framework to analyze curriculum work and educational change and to identify conditions that enable or constrain meaningful action.
Why it matters
The practical implication is that systems cannot demand agency while withholding time, trust, resources, collegial support, and genuine decision-making space.
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- Vol 4 RE: Chapters 25, 27
artificial intelligenceself-regulated learningintelligent tutoring systemslearning analyticsconversational agentsadaptive learningmetacognitive regulationlearner agencyJournal article Evidence summaryAt a glance
Ren, Lee, and May systematically review how artificial intelligence has been used to support self-regulated learning in education.
Key ideas
Searching literature from 2004 through 2024, they retain twenty-seven studies and classify them by educational level, research method, subject area, AI technology, self-regulated-learning framework, and reported outcome. The review identifies intelligent tutoring and adaptive systems, learning analytics and prediction, conversational agents, and other data-driven supports that can prompt planning, monitoring, feedback use, strategy adjustment, and reflection. It also finds uneven theoretical grounding and limited evidence across contexts, with many studies concentrated in particular settings and phases of regulation.
Why it matters
The authors call for stronger alignment between AI functions and explicit self-regulation theory, more rigorous empirical designs, attention to learner agency and ethics, and research that tests sustained effects rather than short-term performance alone.
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- Vol 4 RE: Chapter 26
generative artificial intelligenceself-directed learningpersonalized learning supportai literacyeducator guidanceacademic integrityinformation evaluationlearning technology researchPreprint Evidence summaryAt a glance
Roe and Perkins map research at the intersection of generative artificial intelligence and self-directed learning through a PRISMA-ScR-informed review of 18 studies published from 2020 to 2024. Their synthesis identifies four recurring themes: possible enhancement of self-directed learning, the continuing importance of educators as guides, personalization of learning support, and the need for caution.
Key ideas
Generative tools can provide rapid, on-demand explanations, feedback, and assistance adapted to a learner’s questions, potentially widening opportunities to plan and pursue learning independently. The evidence also raises concerns about accuracy, overreliance, academic integrity, privacy, and learners’ ability to judge generated information. The authors therefore reject the idea that AI makes teachers unnecessary; educators remain central in developing AI literacy, structuring tasks, and helping learners evaluate outputs. The review emphasizes that the evidence base is young, dominated by text-based systems and short-term studies.
Why it matters
It calls for longitudinal, empirical work on learning outcomes, diverse learner groups, and emerging multimodal tools before strong causal or general claims are made.
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- Vol 4 RE: Chapter 26
lifelong learningartificial intelligence literacycomputational thinkingcritical thinkingcreative thinkingalgorithmic reasoninghuman agencyethical judgmentPreprint Evidence summaryAt a glance
Romero examines lifelong learning demands created by rapid artificial-intelligence adoption from the combined perspectives of computational, critical, and creative thinking. The paper argues that AI literacy should extend beyond operating tools: people need enough conceptual understanding to frame problems, evaluate outputs, recognize limitations, and collaborate responsibly with automated systems.
Key ideas
Computational thinking contributes decomposition, abstraction, pattern recognition, and algorithmic reasoning, while critical thinking supports verification, attention to bias, and ethical judgment. Creative competence helps learners imagine alternatives and use AI as a partner rather than merely accept generated responses. The discussion connects these capabilities to changing workplaces, management, leadership, and the Sustainable Development Goals, where continuing reskilling is necessary but technical proficiency alone is insufficient. Romero also foregrounds social questions about regulation, human agency, and which purposes AI should serve.
Why it matters
This is a conceptual review rather than an intervention study, so it offers a competency-oriented framework and agenda, not causal evidence that a particular curriculum improves employment or learning. Its practical implication is to integrate technical, evaluative, ethical, and creative learning throughout adulthood.
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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
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