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2 references
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- Vol 7 RE: App B
- Vol 7 RE: Chapters 7, 11, App D
intelligent tutoring systemslearning outcomesadaptive instructionstep-based guidanceinstructional feedbackdomain modelsprocedural knowledgemeta-analysisJournal article Evidence summaryAt a glance
This meta-analysis evaluates intelligent tutoring systems (ITS)—programs that model learners and adapt instruction—against several alternative learning conditions. The authors synthesised 107 effect sizes from studies spanning school, university, and workplace settings and many subject areas.
Key ideas
Overall, students using an ITS achieved better learning outcomes than those receiving large-group instruction, textbook or workbook study, and other computer-based instruction. Effects were smaller when ITS were compared with individualised human tutoring or small-group instruction, showing that the relevant comparison matters. The analysis also examined system type, procedural versus declarative knowledge, assessment type, and design features as potential moderators. Results support the value of step-based guidance, feedback, and adaptation but reveal substantial variation among systems and studies.
Why it matters
For educators, the paper does not imply that any software labelled intelligent will work; benefits depend on sound domain models, close alignment between tutoring and assessment, and thoughtful integration with teaching.
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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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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
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References are checked against publisher records, scholarly indexes, DOI or ISBN metadata, repositories and author records where available. An entry marked [Citation not verified] preserves the wording found in the relevant volume without attributing findings to an unconfirmed work.
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