Designing Against Deskilling: Metacognitive Feedback Reduces Cognitive Offloading to LLM Assistants

Abstract: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 assistant that provided solutions only on explicit request, followed by an unaided test. Metacognitive feedback reduced answer offloading (OR $= 0.47$) and improved test performance (OR $= 1.51$). We found no evidence that the reward affected either outcome. Our results identify metacognitive feedback as a promising design choice to reduce cognitive offloading.

Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)

arXiv:2609.20143 [cs.HC]

(or arXiv:2609.20143v1 [cs.HC] for this version)

https://doi.org/10.48550/arXiv.2609.20143 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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