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3 references
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- Vol 5 RE: Chapter 12
human-centred designlearning analyticsartificial intelligencestakeholder participationhuman controlautomationprivacytrustworthinessJournal article Evidence summaryAt a glance
This systematic review examines how human-centred principles are represented in learning analytics and artificial-intelligence systems for education.
Key ideas
The authors reviewed 108 papers, focusing on stakeholder participation, the balance between human control and automation, and attention to safety, reliability, and trustworthiness. Although many systems acknowledged a need for human control, students and other target users were often only lightly involved in actual design and development. Safety and trustworthiness also received less attention than usability or technical performance. The review recommends involving educators and learners throughout design and deployment, making decisions about automation explicit, and treating reliability, agency, privacy, and trust as core design requirements.
Why it matters
For schools, the study cautions against adopting data-driven tools solely for efficiency: educational stakeholders should help define the purposes, limits, and acceptable consequences of the technology.
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- Vol 5 RE: Chapter 8
artificial intelligenceself-regulated learninglearner agencylearning analyticsadaptive feedbacktheoretical alignmentethical reportingJournal article Evidence summaryAt a glance
This systematic mapping review analyses 84 studies at the intersection of artificial intelligence and self-regulated learning.
Key ideas
Using a framework organised around who is involved, what theories and constructs are addressed, how the research is conducted, and why AI is applied, the authors map publication patterns, stakeholders, methods, technologies, and objectives. The review finds rapid growth but uneven theoretical grounding and limited attention to some phases and actors in self-regulation. Much work focuses on higher education and on using learner data to predict, recommend, prompt, or provide feedback. The authors call for clearer links between AI functions and established self-regulated-learning theory, broader contexts, stronger ethical and methodological reporting, and more attention to learner agency.
Why it matters
For educators, the review offers a structured way to judge whether an AI tool genuinely supports planning, monitoring, reflection, or adaptation.
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- Vol 7 RE: Chapters 11, 14
hybrid intelligencelearning analyticseducational artificial intelligencecognitive augmentationhuman agencyai literacyhuman–ai collaborationJournal article Evidence summaryAt a glance
Cukurova challenges the narrow view of educational AI as a collection of tools, especially generative systems that externalize human cognitive work. He reconnects artificial intelligence, learning analytics, and learning science through a human-centred framework with three possibilities: AI can externalize cognition by performing tasks, shape cognition as people internalize its models and outputs, or extend cognition through tightly coupled human–AI systems.
Key ideas
The last possibility supports hybrid intelligence, in which complementary human and machine capabilities produce outcomes neither could achieve alone. AI and analytics can also make learning processes visible and provide material for scientific inquiry, feedback, awareness, and regulation. Yet prediction alone does not explain learning, and indiscriminate delegation may weaken important human competencies. The article therefore asks researchers and educators to examine which cognitive work should remain human, how agency and meaning-making are preserved, and how systems affect competence over time.
Why it matters
It calls for broader AI literacy, educational-system innovation, and research that evaluates augmentation, ethics, and human development rather than merely tool performance.
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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
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