The efficiency-gain illusion: People underestimate the rate of AI use and overestimate its benefits on simple tasks
For human-AI interaction researchers and system designers, this work identifies systematic miscalibrations in AI reliance on simple tasks, highlighting risks of overuse.
Across three pre-registered studies (N=2691), people frequently use AI for simple tasks even when it provides no meaningful time or effort savings, due to underestimating their own AI use and overestimating its benefits. A carryover effect entrenches this miscalibration, risking an overreliance feedback loop.
People are increasingly turning to AI assistance for simple tasks, e.g., arithmetic, spell-check, and answering simple questions. But does AI assistance actually save users time and effort? We investigate people's propensity to use AI for cognitively simple tasks and assess whether their reliance is well-calibrated. Across three pre-registered user studies (N = 2691), we find that people frequently choose to use AI even when doing so is inefficient (i.e. provides no meaningful time or effort savings). We identify systematic miscalibration at two levels: (1) a self-estimate miscalibration where people on average believe that they are using AI less than they actually are, and (2) efficiency-gain illusions where people overestimate how much time and effort savings AI use affords. We also identify a session-level carryover effect where a participant's prior AI use leads to further AI adoption and entrenches their miscalibration about time savings. Our results shed light on the mechanisms and biases underlying people's choice of whether to use AI as well as the risk of an overreliance feedback loop.