AI Assistance Reduces Persistence and Hurts Independent Performance

• URL: http://arxiv.org/abs/2604.04721v2
• Date: Tue, 07 Apr 2026 03:41:15 GMT
• Title: AI Assistance Reduces Persistence and Hurts Independent Performance
• Abstract summary: We provide evidence for two key consequences of AI assistance: reduced persistence and impairment of unassisted performance.<n>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.<n>These results suggest the need for AI model development to prioritize long-term competence alongside immediate task completion.
• Score: 1.3667120179358196
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• How AI Assistance Affects Human Skill Development: A Study of Learning with Logic Puzzles [12.810110526024873]
  We examine tension in a controlled logic-puzzle experiment involving on-demand AI assistance.<n>We find that lower-cost assistance induces more frequent AI use.<n>We also find that participants who request AI assistance during the AI-access phase perform worse at the task after assistance is removed.
  arXiv  Detail & Related papers  (2026-08-24T17:46:11Z)
• The Boundaries of Automation: A Theory of Persistent Human Participation [107.84677954791773]
  We argue that human participation may persist even with highly capable AI systems.<n>In some activities, the target is not fully specified in advance but instead emerges through the interaction itself.<n>This perspective has important implications for the limits of automation.
  arXiv  Detail & Related papers  (2026-07-23T17:33:43Z)
• Cognitive offloading and the speedup illusion in human-AI interaction [39.088619511248986]
  Large language models (LLMs) have the potential to boost human productivity by speeding up task completion.<n>We conducted a large-scale behavioral study to characterize mismatches between expectations and reality.<n>We identify a speedup illusion where people have accurate forecasts of independent completion times but significantly underestimate AI-assisted times.
  arXiv  Detail & Related papers  (2026-05-22T02:53:12Z)
• The Hidden Cost of Contextual Sycophancy: an AI Literacy Intervention in Human-AI Collaboration [0.904861150954008]
  Large Language Models (LLMs) are increasingly used in educational settings as interactive tools for collaboration.<n>Sycophancy, aligning with user beliefs even when incorrect, raises concerns for learning and decision-making.<n>This study investigates how sycophantic alignment emerges in authentic multi-turn human-AI interactions.
  arXiv  Detail & Related papers  (2026-05-18T13:20:45Z)
• Critical Thinking in the Age of Artificial Intelligence: A Survey-Based Study with Machine Learning Insights [1.5223740593989443]
  This paper investigates the relationship between AI-use behavior and critical-thinking performance.<n>It shows that reduced patience and stronger dependence-related tendencies are more closely associated with lower reasoning performance than background characteristics alone.<n>It argues that effective human-AI collaboration should support reflection, verification, and sustained cognitive effort rather than substitute for them.
  arXiv  Detail & Related papers  (2026-03-18T03:22:44Z)
• Align When They Want, Complement When They Need! Human-Centered Ensembles for Adaptive Human-AI Collaboration [13.041288521972563]
  In human-AI decision making, designing AI that complements human expertise has been a natural strategy to enhance human-AI collaboration.<n>An aligned AI fosters trust yet risks reinforcing suboptimal human behavior and lowering human-AI team performance.<n>We introduce a novel human-centered adaptive AI ensemble that strategically toggles between two specialist AI models.
  arXiv  Detail & Related papers  (2026-02-23T18:22:58Z)
• Explainable AI as a Double-Edged Sword in Dermatology: The Impact on Clinicians versus The Public [46.86429592892395]
  explainable AI (XAI) addresses this by providing AI decision-making insight.<n>We present results from two large-scale experiments combining a fairness-based diagnosis AI model and different XAI explanations.
  arXiv  Detail & Related papers  (2025-12-14T00:06:06Z)
• Efficiency Without Cognitive Change: Evidence from Human Interaction with Narrow AI Systems [0.0]
  Short-term exposure to narrow AI tools enhances core cognitive abilities.<n>No significant pre-post differences emerged in standardized measures of problem solving or verbal comprehension.
  arXiv  Detail & Related papers  (2025-10-28T18:55:44Z)
• How Performance Pressure Influences AI-Assisted Decision Making [52.997197698288936]
  We show how pressure and explainable AI (XAI) techniques interact with AI advice-taking behavior.<n>Our results show complex interaction effects, with different combinations of pressure and XAI techniques either improving or worsening AI advice taking behavior.
  arXiv  Detail & Related papers  (2024-10-21T22:39:52Z)
• Comparing Zealous and Restrained AI Recommendations in a Real-World Human-AI Collaboration Task [11.040918613968854]
  We argue that careful exploitation of the tradeoff between precision and recall can significantly improve team performance. We analyze the performance of 78 professional annotators working with a) no AI assistance, b) a high-precision "restrained" AI, and c) a high-recall "zealous" AI in over 3,466 person-hours of annotation work.
  arXiv  Detail & Related papers  (2024-10-06T23:19:19Z)
• Effects of AI Feedback on Learning, the Skill Gap, and Intellectual Diversity [4.8342038441006805]
  We investigate how AI use affects three interrelated long-term outcomes: learning, skill gap, and diversity of decision strategies. We show that individuals are far more likely to seek AI feedback in situations in which they experienced success rather than failure. As a result, access to AI feedback increases, rather than decreases, the skill gap between high- and low-skilled individuals.
  arXiv  Detail & Related papers  (2024-09-27T11:44:03Z)
• Fairness in AI and Its Long-Term Implications on Society [68.8204255655161]
  We take a closer look at AI fairness and analyze how lack of AI fairness can lead to deepening of biases over time. We discuss how biased models can lead to more negative real-world outcomes for certain groups. If the issues persist, they could be reinforced by interactions with other risks and have severe implications on society in the form of social unrest.
  arXiv  Detail & Related papers  (2023-04-16T11:22:59Z)
• Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
  Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations. It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
  arXiv  Detail & Related papers  (2022-01-26T18:53:09Z)
• Trustworthy AI: A Computational Perspective [54.80482955088197]
  We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being. For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
  arXiv  Detail & Related papers  (2021-07-12T14:21:46Z)
• Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance [44.730580857733]
  Prior studies observed improvements from explanations only when the AI, alone, outperformed both the human and the best team. We conduct mixed-method user studies on three datasets, where an AI with accuracy comparable to humans helps participants solve a task. We find explanations increase the chance that humans will accept the AI's recommendation, regardless of its correctness.
  arXiv  Detail & Related papers  (2020-06-26T03:34:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.