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3 references
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- Vol 4 RE: App D
socially shared regulationregulation toolscollaborative learningshared goalslearning analyticsregulatory promptsgroup awarenesslearner agencyJournal article Evidence summaryAt a glance
Järvelä and colleagues develop design principles for technological tools that support socially shared regulation of learning in collaborative groups. Effective collaboration requires more than exchanging ideas: learners must regulate cognition, motivation, emotion, behaviour, goals, and strategy both individually and together.
Key ideas
The authors identify three connected design aims. Tools should increase awareness of one’s own and others’ learning processes; help learners externalise and share goals, plans, progress, and internal states; and prompt the activation or acquisition of regulatory processes when needed. Their illustrative tool uses structured prompts and shared visual information to help groups recognise challenges, discuss regulation, and coordinate action. Support should foster learners’ agency and shared responsibility rather than prescribe every step or substitute a dashboard for dialogue.
Why it matters
The article synthesises theory and prior evidence and demonstrates a design application, but it is not a conclusive trial of effects on achievement. Its contribution is a bridge from socially shared regulation theory to testable CSCL features, highlighting that timing, interpretation, group interaction, and integration with authentic tasks determine whether an intervention becomes useful regulation rather than additional workload.
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- Vol 4 RE: App D
human–ai collaborationsocially shared regulationtrigger eventsmachine learningmultimodal tracesexplainable ailearner agencygroup dialogueJournal article Evidence summaryAt a glance
Järvelä, Nguyen, and Hadwin propose human–AI collaboration for socially shared regulation of learning rather than automation that replaces human judgement. Collaborative groups encounter trigger events—cognitive, motivational, emotional, or social challenges—that require members to notice a problem, interpret it, set or revise goals, select strategies, monitor action, and adapt together.
Key ideas
Multimodal traces and machine learning may help detect patterns that are difficult for participants or researchers to see in real time. The authors’ hybrid human–AI shared regulation model treats people and AI as interacting subsystems with different strengths: computation can integrate streams and recognise candidate events, while learners and teachers contribute contextual meaning, values, goals, agency, and responsibility. AI outputs should therefore prompt awareness, reflection, and group dialogue rather than issue opaque prescriptions. The paper illustrates a research programme and propositions, not a validated classroom product.
Why it matters
Key challenges include theoretical grounding, explainability, privacy, bias, data quality, timing, and maintaining learner control. Productive systems must be co-designed and evaluated for whether they genuinely strengthen regulation and learning.
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- Vol 4 RE: App A, App C
- Vol 4 RE: Chapters 25, 27
artificial intelligenceself-regulated learningmetacognitive awarenessadaptive feedbacklearner agencyhuman-centred regulationlearning analyticsalgorithmic biasJournal article Evidence summaryAt a glance
Lan and Zhou qualitatively synthesise research on artificial-intelligence applications supporting self-regulated learning in higher education. They organise findings around phases and functions of regulation, including goal setting and planning, performance and monitoring, feedback and strategy adjustment, and reflection.
Key ideas
AI systems can collect and visualise learning data, recommend resources or pathways, provide adaptive prompts and feedback, support metacognitive awareness, and help learners make decisions. The review distinguishes human-centred regulation, in which learners retain access and control, from designs that risk outsourcing judgement to opaque automation. Evidence is uneven: studies concentrate on selected phases, tools, and short-term outcomes, while emotional and motivational regulation, long-term development, diverse populations, and transfer receive less attention. Challenges include privacy, bias, explainability, data quality, overreliance, learner agency, and whether recommendations genuinely improve regulation rather than compliance.
Why it matters
Because included interventions and methods are heterogeneous, the review does not establish a single effect size or prove that AI causes better learning. It calls for theory-grounded, longitudinal, and human-centred design in which AI augments learners’ awareness and choice and teachers remain responsible for pedagogical interpretation.
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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
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