Confidence Without Competence in AI-Assisted Knowledge Work
This addresses the issue of confidence without competence in AI-assisted knowledge work for students, offering incremental design improvements to mitigate overreliance on LLMs.
The study tackled the problem of overconfidence and shallow learning when students use LLMs by designing and testing alternative interaction modes, finding that guided hints achieved the largest learning gains without increasing frustration, while future-self explanations best aligned perceived and actual understanding.
Large Language Models (LLMs) are widely used by students, yet their tendency to provide fast and complete answers may discourage reflection and foster overconfidence. We examined how alternative LLM interaction designs support deeper thinking without excessively increasing cognitive burden. We conducted a two-phase mixed-methods study. In Phase 1, interviews with 16 Gen Z students informed the design of Deep3, a web-based system with three interaction modes: \emph{a)} future-self explanations, \emph{b)} contrastive learning, and \emph{c)} guided hints. In Phase 2, we evaluated Deep3 with 85 participants across two learning tasks. We found that a standard single-agent baseline produced high perceived understanding despite the lowest objective learning. In contrast, future-self explanations imposed higher cognitive workload yet yielded the closest alignment between perceived and actual understanding, while guided hints achieved the largest learning gains without a proportional increase in frustration. These findings show that effort, confidence, and learning systematically diverge in LLM-supported work.