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2 references
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- Vol 4 RE: Chapters 1, 2, 4, 6, 7, 8, 13, 14, 17, 19, 21, 25, App A, App B, App C, App D
self-regulated learningco-regulationsocially shared regulationcollaborative learninggroup agencyregulation processeslearning analyticsBook chapter Evidence summaryAt a glance
Hadwin, Järvelä, and Miller distinguish three modes of regulation in collaborative learning. Self-regulation involves an individual’s adaptive control of cognition, motivation, emotion, and behaviour in relation to personal goals.
Key ideas
Co-regulation is a temporary, asymmetric process in which another person or tool supports, prompts, or helps appropriate regulation. Socially shared regulation occurs when group members collectively negotiate goals, monitor progress and conditions, control strategies and motivation, and evaluate joint activity. It is not the sum of individual self-regulation or simply successful cooperation. Groups move among modes as tasks, expertise, and difficulties change, and regulation can be productive or maladaptive. Research therefore needs process-sensitive evidence—talk, actions, traces, physiological or affective indicators—rather than relying only on retrospective questionnaires or final performance.
Why it matters
Instruction can make planning, monitoring, emotional challenges, strategy decisions, and reflection visible through shared tools and prompts, while gradually building group agency. Collaboration scripts should support coordination without replacing learners’ responsibility or assuming every group member experiences the same goals and conditions.
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- Vol 4 RE: App C
- Vol 5 RE: Chapters 4, 8, 12, 15, App C
- Vol 6 RE: App E
- Vol 6 RE: Chapter 14, App D
- Vol 7 RE: Chapters 8, 9, 10, 11, 12, 14, 16, App D
artificial intelligence literacyadaptive learning systemslearning analyticsintelligent tutoring systemscritical thinkingteacher augmentationalgorithmic biasstudent privacyBook Evidence summaryAt a glance
Holmes, Bialik, and Fadel examine two connected questions: what young people should learn in an AI-shaped world and how AI systems might support education. The first part argues that curricula need deliberate updating across knowledge, skills, character, and meta-learning, including data and AI literacy, interdisciplinary understanding, creativity, critical thinking, collaboration, ethics, and the capacity to keep learning.
Key ideas
The second explains major AI techniques and surveys educational applications such as intelligent tutoring, adaptive systems, assessment, learning analytics, conversational agents, and administrative support. The authors distinguish plausible uses from exaggerated promises and emphasise that technology embodies assumptions about learning. AI can offer timely information, personalisation, and assistance, but risks include bias, privacy loss, opaque decisions, surveillance, commercial interests, narrowing educational purposes, and the displacement or deskilling of teachers. The book favours augmenting educators rather than automating them.
Why it matters
It calls for educators, researchers, policymakers, and developers to evaluate systems against educational goals, evidence, equity, and human values, while preparing learners to understand and shape AI rather than merely consume it.
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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
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