Metacognition and Confidence Dynamics in Advice Taking from Generative AI
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Abstract (arXiv)
Generative Artificial Intelligence (GenAI) can aid humans in a wide range of tasks, but its effectiveness critically depends on users being able to evaluate the accuracy of GenAI outputs and their own expertise. Here we asked how confidence in self and GenAI contributes to decisions to seek and rely on advice from GenAI ('prospective confidence'), and how advice-taking in turn shapes this confidence ('retrospective confidence'). In a novel paradigm involving text generation, participants formulated plans for events, and could request advice from a GenAI (Study 1; N=200) or were randomly assigned to receive advice (Study 2; N=300), which they could rely on or ignore. Advice requests in Study 1 were related to higher prospective confidence in GenAI and lower confidence in self. Advice-seekers showed increased retrospective confidence in GenAI, while those who declined advice showed increased confidence in self. Random assignment in Study 2 revealed that advice exposure increases confidence in GenAI and in self, suggesting that GenAI advice-taking causally boosts retrospective confidence. These results were mirrored in advice reliance, operationalised as the textual similarity between GenAI advice and participants' responses, with reliance associated with increased retrospective confidence in both GenAI and self. Critically, participants who chose to obtain/rely on advice provided more detailed responses (likely due to the output's verbosity), but failed to check the output thoroughly, missing key information. These findings underscore a key role for confidence in interactions with GenAI, shaped by both prior beliefs about oneself and the reliability of AI, and context-dependent exposure to advice.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Oct 2025.
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Citing papers per year: 2026: 3.
3 papers.
- 2026Explaining Too Much? Understanding How Large Language Model Reasoning Traces Influence Performance and Metacognitioncited by 1
- 2026Metacognition in LLMs: Foundations, Progress, and Opportunitiescited by 1
- 2026Know When to Trust the Skill: Delayed Appraisal and Epistemic Vigilance for Single-Agent LLMscited by 0
- 2026Metacognition in LLMs: Foundations, Progress, and Opportunities13 Jul 2026
- 2026Explaining Too Much? Understanding How Large Language Model Reasoning Traces Influence Performance and Metacognition25 May 2026
- 2026Know When to Trust the Skill: Delayed Appraisal and Epistemic Vigilance for Single-Agent LLMs17 Apr 2026