Cognitive offloading and the speedup illusion in human-AI interaction

• URL: http://arxiv.org/abs/2605.23177v1
• Date: Fri, 22 May 2026 02:53:12 GMT
• Title: Cognitive offloading and the speedup illusion in human-AI interaction
• Abstract summary: Large language models (LLMs) have the potential to boost human productivity by speeding up task completion.<n>We conducted a large-scale behavioral study to characterize mismatches between expectations and reality.<n>We identify a speedup illusion where people have accurate forecasts of independent completion times but significantly underestimate AI-assisted times.
• Score: 39.088619511248986
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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