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

arXiv: https://arxiv.org/abs/2609.20143 · HTML: https://mlsanity.com/paper/2609.20143/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 93 references that are in the map.
- First posted on arXiv 17 Sep 2026.

## Builds on (most cited first, 26 of 26)

- [gpt-oss-120b & gpt-oss-20b Model Card](https://mlsanity.com/paper/2508.10925/) (2025)
- [Studying the effect of AI Code Generators on Supporting Novice Learners in Introductory Programming](https://mlsanity.com/paper/2302.07427/) (2023)
- [The Metacognitive Demands and Opportunities of Generative AI](https://mlsanity.com/paper/2312.10893/) (2023)
- [CodeAid: Evaluating a Classroom Deployment of an LLM-based Programming Assistant that Balances Student and Educator Needs](https://mlsanity.com/paper/2401.11314/) (2024)
- [Appropriate Reliance on AI Advice: Conceptualization and the Effect of Explanations](https://mlsanity.com/paper/2302.02187/) (2023)
- [CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classes](https://mlsanity.com/paper/2308.06921/) (2023)
- [Beware of Metacognitive Laziness: Effects of Generative Artificial Intelligence on Learning Motivation, Processes, and Performance](https://mlsanity.com/paper/2412.09315/) (2024)
- [Do People Engage Cognitively with AI? Impact of AI Assistance on Incidental Learning](https://mlsanity.com/paper/2202.05402/) (2022)
- [Human Creativity in the Age of LLMs: Randomized Experiments on Divergent and Convergent Thinking](https://mlsanity.com/paper/2410.03703/) (2024)
- [How AI Impacts Skill Formation](https://mlsanity.com/paper/2601.20245/) (2026)
- [AI, Help Me Thinkunicodex2014but for Myself: Assisting People in Complex Decision-Making by Providing Different Kinds of Cognitive Support](https://mlsanity.com/paper/2504.06771/) (2025)
- [DBox: Scaffolding Algorithmic Programming Learning through Learner-LLM Co-Decomposition](https://mlsanity.com/paper/2502.19133/) (2025)
- [AI Assistance Reduces Persistence and Hurts Independent Performance](https://mlsanity.com/paper/2604.04721/) (2026)
- [Enhancing Critical Thinking in Generative AI Search with Metacognitive Prompts](https://mlsanity.com/paper/2505.24014/) (2025)
- [A validity-guided workflow for robust large language model research in psychology](https://mlsanity.com/paper/2507.04491/) (2025)
- [Understanding, Protecting, and Augmenting Human Cognition with Generative AI: A Synthesis of the CHI 2025 Tools for Thought Workshop](https://mlsanity.com/paper/2508.21036/) (2025)
- [More AI Assistance Reduces Cognitive Engagement: Examining the AI Assistance Dilemma in AI-Supported Note-Taking](https://mlsanity.com/paper/2509.03392/) (2025)
- [Exploring the Potential of Metacognitive Support Agents for Human-AI Co-Creation](https://mlsanity.com/paper/2506.12879/) (2025)
- [AI Meets the Classroom: When Do Large Language Models Harm Learning?](https://mlsanity.com/paper/2409.09047/) (2024)
- [Personalized Exercise Recommendation with Semantically-Grounded Knowledge Tracing](https://mlsanity.com/paper/2507.11060/) (2025)
- [Partnering with Generative AI: Experimental Evaluation of Human-Led and Model-Led Interaction in Human-AI Co-Creation](https://mlsanity.com/paper/2510.23324/) (2025)
- [Reporting and Reviewing LLM-Integrated Systems in HCI: Challenges and Considerations](https://mlsanity.com/paper/2602.05128/) (2026)
- [Improving Student-AI Interaction Through Pedagogical Prompting: An Example in Computer Science Education](https://mlsanity.com/paper/2506.19107/) (2025)
- [When Scaffolding Breaks: Investigating Student Interaction with LLM-Based Writing Support in Real-Time K-12 EFL Classrooms](https://mlsanity.com/paper/2512.05506/) (2025)
- [LLM Chatbots in High School Programming: Exploring Behaviors and Interventions](https://mlsanity.com/paper/2511.18985/) (2025)
- [Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build](https://mlsanity.com/paper/2605.21629/) (2026)

## Abstract (arXiv)

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 2×2 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.