Introduces AI learning companions as pedagogically informed LLM agents and proposes a three-foundation framework (pedagogical, adaptive, responsible) illustrated by five case studies to prioritize learning over performance.
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COIVis aligns multimodal video concepts with screen space and time to turn eye-tracking data into interpretable learner-state sequences, enabling instructors to explore cohort and individual learning patterns in MOOCs.
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Building AI Companions that Prioritise Learning over Performance
Introduces AI learning companions as pedagogically informed LLM agents and proposes a three-foundation framework (pedagogical, adaptive, responsible) illustrated by five case studies to prioritize learning over performance.
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COIVis: Eye-tracking-based Visual Exploration of Concept Learning in MOOC Videos
COIVis aligns multimodal video concepts with screen space and time to turn eye-tracking data into interpretable learner-state sequences, enabling instructors to explore cohort and individual learning patterns in MOOCs.