# Performance and Metacognition Disconnect when Reasoning in Human-AI Interaction

arXiv: https://arxiv.org/abs/2409.16708 · HTML: https://mlsanity.com/paper/2409.16708/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2024.

## Built on by (most cited first, 11 of 11)

- [Understanding Critical Thinking in Generative Artificial Intelligence Use: Development, Validation, and Correlates of the Critical Thinking in AI Use Scale](https://mlsanity.com/paper/2512.12413/) (2025)
- [Metacognition in LLMs: Foundations, Progress, and Opportunities](https://mlsanity.com/paper/2607.11881/) (2026)
- [Beyond the Steeper Curve: AI-Mediated Metacognitive Decoupling and the Limits of the Dunning-Kruger Metaphor](https://mlsanity.com/paper/2603.29681/) (2026)
- [Agentivism: a learning theory for the age of artificial intelligence](https://mlsanity.com/paper/2604.07813/) (2026)
- [Dead Cognitions: A Census of Misattributed Insights](https://mlsanity.com/paper/2604.10288/) (2026)
- [Co-Writing with AI: An Empirical Study of Diverse Academic Writing Workflows](https://mlsanity.com/paper/2604.25389/) (2026)
- [Access Timing as Scaffolding: A Reinforcement Learning Approach to GenAI in Education](https://mlsanity.com/paper/2605.15850/) (2026)
- [SCAN: A Decision-Making Framework for Effective Task Allocation with Generative AI](https://mlsanity.com/paper/2606.15601/) (2026)
- [Can Legal AI Know When It Is Wrong? And Do Students Know When It Is?](https://mlsanity.com/paper/2608.21089/) (2026)
- [Available but Unclaimed: An Empirical Study of Human-AI Synergy](https://mlsanity.com/paper/2609.16793/) (2026)
- [Structured pre-generation elicitation versus single-shot prompting in AI-assisted enterprise decision-making: a randomised online experiment](https://mlsanity.com/paper/2610.09593/) (2026)

## Abstract (arXiv)

Optimizing human-AI interaction requires users to reflect on their own performance critically. Our paper examines whether people using AI to complete tasks can accurately monitor how well they perform. In Study 1, participants (N = 246) used AI to solve 20 logical problems from the Law School Admission Test. While their task performance improved by three points compared to a norm population, participants overestimated their performance by four points. Interestingly, higher AI literacy was linked to less accurate self-assessment. Participants with more technical knowledge of AI were more confident but less precise in judging their own performance. Using a computational model, we explored individual differences in metacognitive accuracy and found that the Dunning-Kruger effect, usually observed in this task, ceased to exist with AI. Study 2 (N = 452) replicates these findings. We discuss how AI levels metacognitive performance and consider consequences of performance overestimation for interactive AI systems enhancing cognition.