AI Assistance Reduces Persistence and Hurts Independent Performance

A series of randomized controlled trials on human-AI interactions (N=1,222) found that AI assistance improves short-term task performance but causes reduced persistence and worse unassisted performance, according to an arXiv paper (2604.04721, v6, revised 13 Oct 2026) submitted by Grace Liu. Across tasks including mathematical reasoning and reading comprehension, participants performed significantly worse without AI and were more likely to give up, with the authors positing that AI conditions people to expect immediate answers and denies them the experience of working through challenges. The authors argue the results add to literature on cognitive offloading and AI-induced deskilling and that model development should prioritize scaffolding long-term competence alongside immediate task completion.

Computer Science Artificial Intelligence Submitted on 6 Apr 2026 v1 https://arxiv.org/abs/2604.04721v1 , last revised 13 Oct 2026 this version, v6 Title:AI Assistance Reduces Persistence and Hurts Independent Performance View PDF https://arxiv.org/pdf/2604.04721 HTML experimental https://arxiv.org/html/2604.04721v6 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. 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 add to the growing literature on cognitive offloading and AI-induced deskilling, and suggest the need for AI model development to prioritize scaffolding long-term competence alongside immediate task completion. Submission history From: Grace Liu view email https://arxiv.org/show-email/ddcff5a0/2604.04721 Mon, 6 Apr 2026 14:43:48 UTC 3,830 KB \ v1\ https://arxiv.org/abs/2604.04721v1 Tue, 7 Apr 2026 03:41:15 UTC 3,830 KB \ v2\ https://arxiv.org/abs/2604.04721v2 Tue, 4 Aug 2026 17:21:25 UTC 3,841 KB \ v3\ https://arxiv.org/abs/2604.04721v3 Wed, 5 Aug 2026 10:45:02 UTC 3,841 KB \ v4\ https://arxiv.org/abs/2604.04721v4 Sat, 3 Oct 2026 17:03:35 UTC 3,841 KB \ v5\ https://arxiv.org/abs/2604.04721v5 v6 Tue, 13 Oct 2026 19:31:44 UTC 3,842 KB Additional Features References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs https://info.arxiv.org/labs/index.html .