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2 references
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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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- Vol 7 RE: Chapter 4
artificial intelligence applicationshigher educationprofiling and predictionadaptive learning systemsintelligent tutoring systemseducational data ethicsinterdisciplinary researchJournal article Evidence summaryAt a glance
Zawacki-Richter and colleagues systematically review artificial-intelligence research in higher education published from 2007 through 2018. From 2,656 records, they include 146 studies and classify applications into four areas: profiling and prediction, assessment and evaluation, adaptive systems and personalisation, and intelligent tutoring systems.
Key ideas
Much of the literature comes from computer science and STEM contexts, with limited participation by educators and little connection to pedagogical theory. Studies often emphasise technical performance while giving less attention to teaching practice, learner perspectives, ethics, privacy, or broader consequences. The review maps an emerging field; it does not establish that AI applications generally improve learning, and its search period predates generative AI. The authors call for interdisciplinary collaboration, theoretically informed educational questions, and critical attention to risks and benefits.
Why it matters
Institutions and researchers should involve educators and students in problem definition, evaluate educational value rather than model accuracy alone, make data and decisions accountable, and study inclusion, agency, privacy, and possible harms alongside effectiveness. Later AI developments require new evidence rather than automatic extrapolation from this corpus.
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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
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