arXiv cs.AI· Moritz Diener, Daniel Hendriks, Philipp Spitzer, Gerhard Satzger, Joshua Holstein, Wei Sun·· 3 小时前AI 评分46
今日工作与明日劳动力：AI 辅助工作中如何激励技能学习
Today's Work, Tomorrow's Workforce: Incentivizing Learning in AI-Assisted Work
AI 导读
arXiv 论文将 accept-or-solve 模型扩展到多任务序列，研究 AI 辅助下员工技能的演变。研究发现：短视的新手可能被困在低技能水平，专家可能流失技能，而战略型员工即使 AI 更准确也会选择独立解题以保住未来技能。论文进一步刻画了奖励独立正确工作的激励机制，指出足够技能可让独立工作自我维持，且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.AI · arxiv.org