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- Vol 4 RE: Chapter 26
generative artificial intelligenceself-directed learningpersonalized learning supportai literacyeducator guidanceacademic integrityinformation evaluationlearning technology researchPreprint Evidence summaryAt a glance
Roe and Perkins map research at the intersection of generative artificial intelligence and self-directed learning through a PRISMA-ScR-informed review of 18 studies published from 2020 to 2024. Their synthesis identifies four recurring themes: possible enhancement of self-directed learning, the continuing importance of educators as guides, personalization of learning support, and the need for caution.
Key ideas
Generative tools can provide rapid, on-demand explanations, feedback, and assistance adapted to a learner’s questions, potentially widening opportunities to plan and pursue learning independently. The evidence also raises concerns about accuracy, overreliance, academic integrity, privacy, and learners’ ability to judge generated information. The authors therefore reject the idea that AI makes teachers unnecessary; educators remain central in developing AI literacy, structuring tasks, and helping learners evaluate outputs. The review emphasizes that the evidence base is young, dominated by text-based systems and short-term studies.
Why it matters
It calls for longitudinal, empirical work on learning outcomes, diverse learner groups, and emerging multimodal tools before strong causal or general claims are made.
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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
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