SOURCE-LINKED INTELLIGENCE
Designing Against Deskilling: Metacognitive Feedback Reduces Cognitive Offloading to LLM Assistants
Cognitive offloading to AI can reduce opportunities to practice skills, creating risks of deskilling. However, it remains unclear how to prevent deskilling without restricting access to AI. Here, we design two interventions to reduce offloading decisions: (1) metacognitive feedback that makes the implications of offloading for users explicit, and (2) an effort-based reward that incentivizes less extensive LLM assistance. We test both in a preregistered online experiment ($N = 704$) with a 2$\times$2 design and a no-AI control. The task was to practice fraction arithmetic with an LLM-based assi
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-17T12:35:04.000Z
- arXiv · Artificial Intelligence · 2026-09-17T12:35:04.000Z
First collected: 2026-09-19T20:26:32.566Z. This is not the publication date.