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- Vol 7 RE: Chapter 12, App D
artificial intelligence in educationeducational technology historydata-intensive policyalgorithmic governanceeducational datatechnology companiesclassificationeducational inequalityJournal article Evidence summaryAt a glance
Williamson and Eynon place contemporary artificial intelligence in education within a longer history rather than treating it as a sudden technical revolution. This editorial traces connections among academic AIED research, commercial educational technology, data-intensive policy, and the growing involvement of major technology companies.
Key ideas
The authors argue that accounts focused only on new algorithms can obscure older ambitions to automate teaching, model learners, and optimise educational decisions. They identify missing links between technical research and scholarship on the social, political, and economic conditions through which systems are designed and adopted. Questions of power, classification, infrastructure, labour, commercial interests, and unequal consequences therefore belong inside analysis of educational AI, not outside it. The article is an agenda-setting editorial, not an evaluation showing that a particular AI application improves learning.
Why it matters
Its practical message is to examine claims of novelty historically, investigate who defines educational problems and desirable futures, and combine technical inquiry with critical social research. This broader perspective can expose contingencies and make alternative, more educationally and publicly accountable futures imaginable.
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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
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