Randomized experiment finds AI draft assistance raises feedback provision by teaching assistants 10.8 percentage points without harming quality.
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5 Pith papers cite this work. Polarity classification is still indexing.
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Proposes the CoRe-3 (FJS) competency model separating Framing, Judging, and Steering for generative AI use, with preliminary validation via simulations on an open platform showing skill dissociation and validity.
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.
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.
citing papers explorer
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AI Assistance for Discretionary Work: Increasing Feedback Provision in Higher Education
Randomized experiment finds AI draft assistance raises feedback provision by teaching assistants 10.8 percentage points without harming quality.
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Framing, Judging, Steering: An Assessable Competency Model for Teach-ing Students to Reason With Generative AI
Proposes the CoRe-3 (FJS) competency model separating Framing, Judging, and Steering for generative AI use, with preliminary validation via simulations on an open platform showing skill dissociation and validity.
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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.