AI Assistance Reduces Persistence and Hurts Independent Performance

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 (∼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.

Our results are notable on two fronts. First, while concern about AI-induced deskilling has grown, prior evidence has been largely correlational (Budzy´n et al., 2025; Gerlich, 2025) or limited to small samples (Kosmyna et al., 2025; Shen & Tamkin, 2026). Here, through a series of randomized controlled trials on human-AI interactions, we provide the first large-scale causal evidence of this effect. Second, we demonstrate how AI can result in loss of motivation and persistence. A rich body of literature in cognitive science and education has shown that the capacity to regulate effort and persist through difficulty is foundational to effective learning, and is among the strongest predictors of long-term academic achievement, workforce adaptability, and resilience (Metcalfe & Mischel, 1999; Duckworth et al., 2007; Maddux, 2009; Metcalfe, 2009; Andersson & Bergman, 2011; Bjork et al., 2011; Kapur, 2014; Guiso et al., 2016; Mooradian et al., 2016). Our results suggest that AI assistance erodes precisely these capacities. People do not merely become worse at tasks, but they also stop trying. If such effects accumulate over months and years of AI use, we may end up creating a generation of learners who have lost the disposition to struggle productively without technological support.