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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 7 RE: Chapter 13, App D
intelligent tutoring systemsdialogue-based tutoringlearning analyticspersonalized learning pathwaysautomated assessmentteacher–ai collaborationeducational data ethicslifelong learning companionsReport Evidence summaryAt a glance
This report introduces artificial intelligence in education for a non-specialist audience and argues that it can improve teaching and learning when used to address clearly defined educational needs. It explains established applications such as intelligent tutoring systems, dialogue-based tutors, exploratory environments, automated assessment, and learning analytics.
Key ideas
These systems build models of subject matter, pedagogy, and learners in order to adapt tasks, feedback, or support. The authors distinguish current, relatively narrow capabilities from speculative general intelligence and emphasize that teachers remain essential. They propose combining AI with educators' social, emotional, and contextual expertise to create new forms of support, including continuous assessment, personalized pathways, collaborative-learning analysis, and tools that help teachers see otherwise hidden learning processes. The report also identifies infrastructure, evidence, ethics, privacy, and workforce development as prerequisites for responsible adoption.
Why it matters
Its forward-looking agenda includes lifelong learning companions and better support for complex skills, but insists that progress requires rigorous evaluation and public discussion about educational aims, data, and acceptable human–machine roles.
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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
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