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2 references
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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 2 RE: Chapters 1, 2, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, App E
artificial intelligence in educationlearning sciencesmultimodal learner dataadaptive scaffoldingteacher decision-makingeducator–developer collaborationethical safeguardshuman judgmentJournal article Evidence summaryAt a glance
Luckin and Cukurova argue that artificial intelligence for education should be designed from evidence about human learning, not simply adapted from technically successful applications in other fields. Three case studies illustrate how rich and multimodal learner data can support scaffolding, collaborative problem solving, and teacher decision-making when analysis is guided by learning-science constructs.
Key ideas
The authors caution that data are not self-explanatory: useful models depend on valid interpretations of cognition, emotion, interaction, and context. They propose a co-design ecosystem linking educators, AI developers, and researchers. Educators need enough AI understanding to evaluate systems and articulate classroom needs; developers need stronger knowledge of pedagogy and learning; researchers help establish evidence, measurement, and ethical safeguards. The framework keeps educational values and stakeholder expertise central throughout development and evaluation.
Why it matters
Rather than replacing teachers, well-designed AI should augment human judgment, make otherwise difficult learning processes visible, and enable timely support. The paper ultimately calls for interdisciplinary partnerships that assess educational benefit alongside technical performance.
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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
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