Yu, Cheng, Jabbar, Sucholutsky, Collins, Jurafsky & Hawkins (2026) — Stanford University / Princeton University. arXiv preprint (cs.CY, cs.HC).
Summary
Across three pre-registered studies (N=2,691), this paper documents systematic miscalibration in how people perceive their own AI usage. The authors find that people not only use AI for cognitively simple tasks even when it provides no meaningful efficiency benefit, but also systematically misperceive both how much they use AI and how much it helps them.
Key Findings
Two Forms of Miscalibration
1. Self-estimate miscalibration: People on average underestimate how often they actually use AI — they believe they use it significantly less than objective measures show.
2. Efficiency-gain illusion: People overestimate the time and effort savings that AI assistance provides, believing tasks are faster and easier with AI even when objective metrics show no difference.
The Overreliance Feedback Loop
Critically, the authors identify a session-level carryover effect: prior AI use in a session leads to further AI adoption, which in turn entrenches the miscalibration about time savings. This creates a self-reinforcing feedback loop — initial AI use begets more AI use, and the illusion of efficiency makes it harder for users to self-correct.
Connection to over-reliance
This study provides a cognitive mechanism for the over-reliance phenomenon: the efficiency-gain illusion explains why students and other users continue to reach for AI even when it demonstrably doesn't help — and in fact may harm learning outcomes, as documented in related work. The findings complement cognitive-shift-ai-education by identifying the perceptual biases that drive behavioral change.
Implications for ai-literacy
These results have direct implications for AI literacy education: users need not only technical knowledge about AI capabilities and limitations, but also metacognitive calibration about their own AI use patterns. Simply telling people that AI may not save time is insufficient — the illusion is perceptual and self-reinforcing.
Related Pages
- ai-assistance-reduces-persistence: Causal evidence (N=1,222) that brief AI assistance reduces persistence and impairs unassisted performance — rapid emergence of over-reliance effects
- cognitive-offloading-speedup-illusion — CogSci 2026 companion paper: AI speedup illusion in simple cognitive tasks
- over-reliance — The behavioral pattern this study's mechanism helps explain
- ai-literacy — Educational implications for metacognitive calibration
- cognitive-offloading — Related cognitive strategy, distinguished from passive overreliance
- student-experience — How students perceive and use AI tools
- generative-ai — The technology context
- cognitive-shift-ai-education — Complementary evidence of AI-driven behavioral change
- learning-by-chatting-genai-impact — Overestimation of AI benefits parallels ChatGPT users' diminished learning
Citation
APA: Yu, S., Cheng, M., Jabbar, A., Sucholutsky, I., Collins, K. M., Jurafsky, D., & Hawkins, R. D. (2026). The efficiency-gain illusion: People underestimate the rate of AI use and overestimate its benefits on simple tasks. arXiv:2605.22687.