Abstract:Large language models (LLMs) have the potential to boost human productivity by speeding up task completion -- provided users know when to offload cognitive work to them. But we do not know if users are well-calibrated in estimating these potential time savings. We conducted a preregistered large-scale behavioral study (N = 1237) to characterize mismatches between expectations and reality, with a focus on simple cognitive tasks. While actual completion times between independent completion and AI-assisted completion did not differ, participants predicted AI to be significantly faster. The same bias was not observed when imagining help from another human participant. We identify a speedup illusion where people have accurate forecasts of independent completion times but significantly underestimate AI-assisted times. Additionally, time and effort dissociate: participants reported lower subjective effort with AI despite equivalent completion times. This suggests that completion time itself is not sufficient to characterize efficiency gains.
| Comments: | Proceedings of the 48th Annual Meeting of the Cognitive Science Society |
| Subjects: | Computers and Society (cs.CY); Human-Computer Interaction (cs.HC) |
| Cite as: | arXiv:2605.23177 [cs.CY] |
| (or arXiv:2605.23177v1 [cs.CY] for this version) | |
| https://doi.org/10.48550/arXiv.2605.23177 arXiv-issued DOI via DataCite (pending registration) |
From: Sunny Yu [view email]
[v1]
Fri, 22 May 2026 02:53:12 UTC (269 KB)
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