AI Assistance Reduces Persistence and Hurts Independent Performance

Abstract

People often optimize for long-term goals in collaboration: A mentor or companion doesn't just answer questions, but also scaffolds learning, tracks progress, and prioritizes the other person's growth over immediate results. In contrast, current AI systems are fundamentally short-sighted collaborators - optimized for providing instant and complete responses, without ever saying no (unless for safety reasons). What are the consequences of this dynamic? Here, through a series of randomized controlled trials on human-AI interactions (N = 1,222), we provide causal evidence for two key consequences of AI assistance: reduced persistence and impairment of unassisted performance. Across a variety of tasks, including mathematical reasoning and reading comprehension, we find that although AI assistance improves performance in the short-term, people perform significantly worse without AI and are more likely to give up. Notably, these effects emerge after only brief interactions with AI (approximately 10 minutes). These findings are particularly concerning because persistence is foundational to skill acquisition and is one of the strongest predictors of long-term learning. We posit that persistence is reduced because AI conditions people to expect immediate answers, thereby denying them the experience of working through challenges on their own. These results suggest the need for AI model development to prioritize scaffolding long-term competence alongside immediate task completion.

Figures (4)

• Figure 1: AI impairs unassisted performance and persistence. (a) Participants' mean solve rate and skip rate per problem in the order presented, with 95% confidence intervals (CIs). Dashed gray lines denote the transition between learning and test problems. Problem difficulty increased across the experiment from one-step (problems 1--4) to two-step (problems 5--8) to three-step (problems 9--12). (b) Participants' mean test solve rate and skip rate with 95% CIs across participants. Test metrics are computed by averaging performance over the final three test problems for each participant.
• Figure 2: Replication of results in Experiment 2. (a) Participants' mean solve rate and skip rate per problem in the order presented with 95% CIs. Problems increased in difficulty from one-step (problems 4-6) to two-step (problems 7-10) to three-step (problems 11-14) problems. (b) Participants' mean test solve rate and test skip rate with 95% CIs.
• Figure 3: Performance and persistence declines are concentrated among participants who obtained direct solutions from AI. (a) AI usage groups show no significant differences in solve rate or skip rate at pretest (one-way ANOVA), suggesting comparable initial skill and motivation levels. (b) Groups differ significantly at test (one-way ANOVA): participants who used AI for direct answers show the lowest solve rate and highest skip rate at test-time. (c) Participants who used AI for direct answers show decline in performance (solve rate) and increased disengagement (skip rate) relative to their own pretest performance. Other groups show similar or improved performance relative to their pretest performance.
• Figure 4: Reduced performance and persistence in reading comprehension task. (a) Participants’ mean solve rate and skip rate per problem in the order presented with 95% CI. Dashed gray lines denote transition between learning problems and test problems. (b) Participants’ mean test solve rate and test skip rate with 95% CIs computed across the participants.