arXiv cs.CY· Moritz Diener, Daniel Hendriks, Philipp Spitzer, Gerhard Satzger, Joshua Holstein, Wei Sun·· 11 小时前AI 评分38
研究：AI 辅助工作如何影响技能习得与保留，以及激励独立工作的付费机制
Today's Work, Tomorrow's Workforce: Incentivizing Learning in AI-Assisted Work
AI 导读
一项研究将“接受或独立解决”模型扩展到多任务序列，考察员工技能随独立练习与依赖 AI 而演变的过程。短视员工可能让新手困在低技能水平、专家技能流失，即便 AI 辅助的结果准确率仍很高；重视未来技能的战略型员工则会在 AI 更准确时仍选择独立解决。研究进一步刻画了奖励正确独立工作的付费机制，发现技能足够高后独立工作可自我维持，且支持更高的技能目标有时反而降低长期激励成本。
正文 · 原文
Abstract:At work, people develop skills through experience: novices acquire skills by completing tasks, while experts retain theirs through continued practice. AI assistance changes this process by offering workers a choice between solving tasks independently and accepting the AI's answers. While relying on AI saves immediate effort, it can also displace the practice needed to acquire and retain skills, including those required to oversee the AI. We extend an existing accept-or-solve model to a sequence of tasks in which workers' skills evolve through independent practice and reliance on AI. We compare myopic workers, who maximize immediate utility, with strategic workers, who also value their future skills. We show that novices can become trapped at low skill levels, while experts may lose skill, even as AI-assisted outcome accuracy remains high. Strategic workers, by contrast, may choose to solve independently even when AI is more accurate, recognizing the future value of acquiring and retaining their skills. We then study incentives that reward correct independent work and characterize the payments needed to encourage skill acquisition and retention. We show that independent work can become self-sustaining once workers acquire sufficient skill, eliminating the need for continued incentives. Counterintuitively, supporting a higher skill target can sometimes reduce long-run incentive costs, either by enabling self-sustaining independent work or by lowering the payments needed to maintain proficiency. Our findings thereby shift the focus from whether workers should rely on AI on individual tasks to how organizations can sustain human expertise over time, and which skills are worth investing in.
| Comments: | 30 pages, 6 figures, 6 tables. Moritz Diener and Daniel Hendriks contributed equally |
| Subjects: | Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computers and Society (cs.CY) |
| Cite as: | arXiv:2611.06346 [cs.HC] |
| (or arXiv:2611.06346v1 [cs.HC] for this version) | |
| https://doi.org/10.48550/arXiv.2611.06346 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Moritz Diener [view email]
[v1]
Wed, 4 Nov 2026 11:07:26 UTC (215 KB)
来源：arXiv cs.CY · arxiv.org