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2 references
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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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- Vol 3 RE: Chapters 5, 9, 13, 14, 15, 16, App D
artificial intelligence literacymachine learninglearning analyticsadaptive feedbackhuman–ai collaborationalgorithmic biasdata privacyethical ai adoptionBook Evidence summaryAt a glance
This practical introduction helps teachers and school leaders understand artificial intelligence well enough to make deliberate educational decisions without becoming technical specialists.
Key ideas
The authors distinguish AI from ordinary automation, explain the role of data and machine learning, and ask educators to begin with valued learning goals rather than available products. Schools are encouraged to become “AI ready” by identifying genuine educational challenges, examining what data are collected and what those data represent, and deciding which tasks require human judgment, relationships, and contextual knowledge. Examples show how AI can support analysis, feedback, personalization, and planning, while also exposing limitations involving bias, privacy, transparency, accountability, and unequal access. The book treats effective adoption as an organizational learning process: educators need shared language, evidence-informed experimentation, ethical review, and a strategy for combining artificial and human intelligence.
Why it matters
Its central message is that AI should expand educational opportunity and strengthen professional capacity, with teachers retaining responsibility for defining success and judging whether a system actually serves learners.
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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
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