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1 reference
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- Vol 6 RE: Chapters 9, 12, App D, App E
- Vol 7 PE: App D
artificial intelligence in educationlearning analyticsintelligent tutoringautomated assessmentdigital credentialsdata governancealgorithmic biashuman oversightReport Evidence summaryAt a glance
The 2021 OECD Digital Education Outlook surveys how artificial intelligence, learning analytics, robotics, and blockchain may support teaching, learning, assessment, credentials, and system management.
Key ideas
It presents education as a human–technology system: tools can personalise practice, provide feedback, automate selected tasks, and make patterns visible, but they remain dependent on data quality, instructional design, professional judgement, and institutional capacity. Chapters examine intelligent tutoring, classroom orchestration, automated assessment, robots, tamper-resistant credentials, data infrastructure, and international policy. The report distinguishes technically possible applications from proven educational improvement and discusses privacy, security, bias, transparency, interoperability, procurement, and unequal access. It argues that digitalisation should augment rather than bypass teachers and that public governance must shape markets and data use.
Why it matters
For education leaders, the practical sequence is to define the learning or administrative problem, examine evidence and risks, ensure human oversight and accessibility, build staff capability, evaluate real-world effects, and retain alternatives when a technology fails or disadvantages learners.
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What it contributes
Evidence summaries explain the central idea and why the source matters to educators.
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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
Citation checks
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