Proposes a six-move framework (Prime, Probe, Point, Attach, Strengthen, Test) for learning with AI, using an 'effortless' diagnostic to avoid illusion of mastery, backed by cited evidence of design-dependent outcomes including 17% harm from unguarded AI and doubled gains from engineered tutors.
AI Tutoring Outperforms In-Class Active Learning: An RCT Introducing a Novel Research-Based Design in an Authentic Educational Setting
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
other 1
citation-polarity summary
years
2026 3verdicts
UNVERDICTED 3roles
other 1polarities
support 1representative citing papers
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.
A structured dialogue intervention corrects 82% of multimodal errors made by LLMs on physics problems, including 100% of visual processing errors.
citing papers explorer
-
The Effortless Trap: Productive Struggle, AI, and the Illusion of Learning
Proposes a six-move framework (Prime, Probe, Point, Attach, Strengthen, Test) for learning with AI, using an 'effortless' diagnostic to avoid illusion of mastery, backed by cited evidence of design-dependent outcomes including 17% harm from unguarded AI and doubled gains from engineered tutors.
-
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.
-
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.