The authors introduce Agentivism as a learning theory for human-AI interaction that explains how durable capability develops through selective delegation, epistemic monitoring, reconstructive internalization, and transfer under reduced support.
AI Tutoring Outperforms In-Class Active Learning: An RCT Introducing a Novel Research-Based Design in an Authentic Educational Setting
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A structured dialogue intervention corrects 82% of multimodal errors made by LLMs on physics problems, including 100% of visual processing errors.
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Agentivism: a learning theory for the age of artificial intelligence
The authors introduce Agentivism as a learning theory for human-AI interaction that explains how durable capability develops through selective delegation, epistemic monitoring, reconstructive internalization, and transfer under reduced support.
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A Dialogue-Based Framework for Correcting Multimodal Errors in AI-Assisted STEM Education
A structured dialogue intervention corrects 82% of multimodal errors made by LLMs on physics problems, including 100% of visual processing errors.