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3 references
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- Vol 4 RE: App D
human–ai collaborationsocially shared regulationtrigger eventsmachine learningmultimodal tracesexplainable ailearner agencygroup dialogueJournal article Evidence summaryAt a glance
Järvelä, Nguyen, and Hadwin propose human–AI collaboration for socially shared regulation of learning rather than automation that replaces human judgement. Collaborative groups encounter trigger events—cognitive, motivational, emotional, or social challenges—that require members to notice a problem, interpret it, set or revise goals, select strategies, monitor action, and adapt together.
Key ideas
Multimodal traces and machine learning may help detect patterns that are difficult for participants or researchers to see in real time. The authors’ hybrid human–AI shared regulation model treats people and AI as interacting subsystems with different strengths: computation can integrate streams and recognise candidate events, while learners and teachers contribute contextual meaning, values, goals, agency, and responsibility. AI outputs should therefore prompt awareness, reflection, and group dialogue rather than issue opaque prescriptions. The paper illustrates a research programme and propositions, not a validated classroom product.
Why it matters
Key challenges include theoretical grounding, explainability, privacy, bias, data quality, timing, and maintaining learner control. Productive systems must be co-designed and evaluated for whether they genuinely strengthen regulation and learning.
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- Vol 4 RE: App C
- Vol 5 RE: Chapters 4, 8, 12, 13, 15
- Vol 6 RE: Chapters 12, 14, App D, App E
- Vol 7 PE: App D
artificial intelligence in educationmachine learninghuman intelligenceadaptive supportmetacognitioneducational dataalgorithmic accountabilityhuman agencyBook Evidence summaryAt a glance
Luckin argues that education should use artificial intelligence to amplify distinctly human intelligence rather than automate teaching around narrow performance measures.
Key ideas
She introduces a broad account of human intelligence that includes academic, social, emotional, and metacognitive capacities, then contrasts it with what contemporary machine-learning systems can infer and optimise. The book explains how educational AI may model learners, provide adaptive support, assist assessment, and make aspects of learning visible to teachers. Its central proposal is a reciprocal design agenda: people need enough understanding of AI to judge its outputs, while systems should be built around rich models of learning and human development. Luckin also discusses data, ethics, accountability, and the danger of adopting technology without a clear educational purpose.
Why it matters
For schools, the practical message is to begin with valued learning goals and teacher expertise, evaluate where automation genuinely adds insight, and preserve human agency in consequential decisions.
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- Vol 3 RE: Chapters 5, 9, 13, 14, 15, 16, App D
artificial intelligence literacymachine learninglearning analyticsadaptive feedbackhuman–ai collaborationalgorithmic biasdata privacyethical ai adoptionBook Evidence summaryAt a glance
This practical introduction helps teachers and school leaders understand artificial intelligence well enough to make deliberate educational decisions without becoming technical specialists.
Key ideas
The authors distinguish AI from ordinary automation, explain the role of data and machine learning, and ask educators to begin with valued learning goals rather than available products. Schools are encouraged to become “AI ready” by identifying genuine educational challenges, examining what data are collected and what those data represent, and deciding which tasks require human judgment, relationships, and contextual knowledge. Examples show how AI can support analysis, feedback, personalization, and planning, while also exposing limitations involving bias, privacy, transparency, accountability, and unequal access. The book treats effective adoption as an organizational learning process: educators need shared language, evidence-informed experimentation, ethical review, and a strategy for combining artificial and human intelligence.
Why it matters
Its central message is that AI should expand educational opportunity and strengthen professional capacity, with teachers retaining responsibility for defining success and judging whether a system actually serves learners.
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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
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