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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Twelve semistructured interviews yield twelve knowledge-based design requirements for tutoring generative social robots, grouped into self-knowledge, user-knowledge, and context-knowledge categories.
AI to Learn 2.0 is a deliverable-oriented framework with a seven-dimension maturity rubric and capability-evidence ladder that permits opaque AI for exploration but requires final outputs to be auditable, transferable, and supported by human-attributable evidence.
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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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Knowledge-Based Design Requirements for Generative Social Robots in Higher Education
Twelve semistructured interviews yield twelve knowledge-based design requirements for tutoring generative social robots, grouped into self-knowledge, user-knowledge, and context-knowledge categories.
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AI to Learn 2.0: A Deliverable-Oriented Governance Framework and Maturity Rubric for Opaque AI in Learning-Intensive Domains
AI to Learn 2.0 is a deliverable-oriented framework with a seven-dimension maturity rubric and capability-evidence ladder that permits opaque AI for exploration but requires final outputs to be auditable, transferable, and supported by human-attributable evidence.