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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Differentiated proactive-reactive human-AI tutoring yields 25% more time on task, 36% higher skill proficiency, and 61% greater academic growth than AI-only tutoring, with proactive support showing marginal extra benefit for lower performers.
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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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Improving Hybrid Human-AI Tutoring by Differentiating Human Tutor Roles Based on Student Needs
Differentiated proactive-reactive human-AI tutoring yields 25% more time on task, 36% higher skill proficiency, and 61% greater academic growth than AI-only tutoring, with proactive support showing marginal extra benefit for lower performers.