Performance and Metacognition Disconnect when Reasoning in Human-AI Interaction
Journal:
arXiv
Published Date:
Sep 25, 2024
Abstract
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.
