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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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