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2 references
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- Vol 4 RE: Chapter 6
self-regulated learningmultimodal dataartificial intelligencetemporal process analysisaffective measurementconstruct validityprivacy in learning analyticsJournal article Evidence summaryAt a glance
De Mooij and colleagues systematically review how multimodal data and artificial intelligence have been used to study self-regulated learning. They map studies onto a grid connecting cognitive, affective, metacognitive, and motivational processes with behavioural, contextual, physiological, and other data streams.
Key ideas
The review distinguishes unimodal, horizontal, vertical, and integrated analytic approaches and examines whether methods capture regulation as a temporal and sequential process across learning contexts. Research is moving beyond single self-report measures toward combinations such as log data, gaze, discourse, facial or physiological signals, and performance evidence. However, motivation and affect remain underrepresented, studies define and align modalities inconsistently, and conventional statistics are often used even when integrated temporal analysis is needed. AI offers possibilities for pattern detection but does not remove problems of construct validity, interpretation, privacy, or context.
Why it matters
The authors call for theoretically grounded measurement, transparent alignment of indicators with processes, stronger validation, and interdisciplinary designs that connect rich sensing with educationally meaningful support.
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- Vol 5 RE: Chapter 15
intelligent tutoring systemslearning analyticsnatural language processinglarge language modelsgenerative aimultimodal learning analyticshuman–ai collaborationself-regulated learningPreprint Evidence summaryAt a glance
Feng, Zhang, and Gašević map changes in artificial intelligence in education by analysing 2,398 articles published from 2020 through 2024 across eight core AIED and learning-analytics venues. Their three-level keyword co-occurrence network analysis examines the field’s overall structure, major knowledge clusters, and emerging bridging topics.
Key ideas
Established technical themes—including intelligent tutoring systems, learning analytics, natural language processing, MOOCs, computer vision, and causal inference—remain prominent. Longitudinal patterns show the rapid rise of large language models and generative AI, alongside multimodal learning analytics and human–AI collaboration. Within generative-AI work, personalisation, self-regulated learning, feedback, assessment, motivation, and ethics are central interests. The authors interpret these developments as movement toward more co-adaptive and human-centred educational AI, while the field remains strongly technology focused.
Why it matters
Because results depend on selected venues, author keywords, preprocessing, and network thresholds, the map describes publication patterns rather than establishing instructional effectiveness or forecasting which technologies will endure.
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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
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