# Beyond "ChatGPT Can Make Mistakes": Designing Interventions to Support Metacognitive Monitoring in AI-Assisted Work

arXiv: https://arxiv.org/abs/2609.17065 · HTML: https://mlsanity.com/paper/2609.17065/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 105 references that are in the map.
- First posted on arXiv 15 Sep 2026.

## Builds on (most cited first, 29 of 29)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance](https://mlsanity.com/paper/2006.14779/) (2020)
- [Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making](https://mlsanity.com/paper/2001.02114/) (2020)
- [Questioning the AI: Informing Design Practices for Explainable AI User Experiences](https://mlsanity.com/paper/2001.02478/) (2020)
- [The Metacognitive Demands and Opportunities of Generative AI](https://mlsanity.com/paper/2312.10893/) (2023)
- ["Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humans](https://mlsanity.com/paper/2001.05871/) (2020)
- [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)
- ["I'm Not Sure, But...": Examining the Impact of Large Language Models' Uncertainty Expression on User Reliance and Trust](https://mlsanity.com/paper/2405.00623/) (2024)
- [Do People Engage Cognitively with AI? Impact of AI Assistance on Incidental Learning](https://mlsanity.com/paper/2202.05402/) (2022)
- [In Search of Verifiability: Explanations Rarely Enable Complementary Performance in AI-Advised Decision Making](https://mlsanity.com/paper/2305.07722/) (2023)
- [Need Help? Designing Proactive AI Assistants for Programming](https://mlsanity.com/paper/2410.04596/) (2024)
- [Knowing About Knowing: An Illusion of Human Competence Can Hinder Appropriate Reliance on AI Systems](https://mlsanity.com/paper/2301.11333/) (2023)
- [Fostering Appropriate Reliance on Large Language Models: The Role of Explanations, Sources, and Inconsistencies](https://mlsanity.com/paper/2502.08554/) (2025)
- [Beyond the Chat: Executable and Verifiable Text-Editing with LLMs](https://mlsanity.com/paper/2309.15337/) (2023)
- [Effective Human-AI Teams via Learned Natural Language Rules and Onboarding](https://mlsanity.com/paper/2311.01007/) (2023)
- [Ironies of Generative AI: Understanding and mitigating productivity loss in human-AI interactions](https://mlsanity.com/paper/2402.11364/) (2024)
- [Contrastive Explanations That Anticipate Human Misconceptions Can Improve Human Decision-Making Skills](https://mlsanity.com/paper/2410.04253/) (2024)
- ["AI enhances our performance, I have no doubt this one will do the same": The Placebo effect is robust to negative descriptions of AI](https://mlsanity.com/paper/2309.16606/) (2023)
- [Causal Evidence that Language Models use Confidence to Drive Behavior](https://mlsanity.com/paper/2603.22161/) (2026)
- [Enhancing Critical Thinking in Generative AI Search with Metacognitive Prompts](https://mlsanity.com/paper/2505.24014/) (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)
- [Beyond Accuracy: How AI Metacognitive Sensitivity improves AI-assisted Decision Making](https://mlsanity.com/paper/2507.22365/) (2025)
- [The AI Memory Gap: Users Misremember What They Created With AI or Without](https://mlsanity.com/paper/2509.11851/) (2025)
- [Exploration vs. Fixation: Scaffolding Divergent and Convergent Thinking for Human-AI Co-Creation with Generative Models](https://mlsanity.com/paper/2512.18388/) (2025)
- [Boosting metacognition in entangled human-AI interaction to navigate cognitive-behavioral drift](https://mlsanity.com/paper/2602.01959/) (2026)
- [LearnMate^2: Design and Evaluation of an LLM-powered Personalized and Adaptive Support System for Online Learning](https://mlsanity.com/paper/2605.06257/) (2026)
- [Explaining Too Much? Understanding How Large Language Model Reasoning Traces Influence Performance and Metacognition](https://mlsanity.com/paper/2605.25856/) (2026)
- [When AI Is Wrong on Purpose: How Students Respond to Buggy GenAI Code](https://mlsanity.com/paper/2607.05068/) (2026)

## Abstract (arXiv)

AI assistance places a metacognitive demand on users, who must judge their own competence and the system's. Yet designers lack comparative evidence on which interventions to choose, where to place them, and how to tell whether they worked. We elicited 30 interventions from 11 experts and, with prior work, organized them into a design space of time (when an intervention acts), level (whose competence is judged), and source (who supplies the monitoring cue). A between-subjects experiment (N = 917; 12 planning-and-organizing problems) compared a per-task reliability card, contrasting replies, pause points, and post-problem reflection against a baseline LLM assistant. Reliability cards and contrasting replies reduced estimation error and overconfidence and increased aggregate confidence discrimination. No task-performance improvement or average within-item discrimination gain was established. We contribute a shared vocabulary, a design space, and evidence that measured monitoring and task performance are separable design targets.