GenAI提升效率但加剧能力差距，关键在人机协作能力。
Generative AI and the Productivity Divide: Human-AI Complementarities in Education
- 用大模型辅助学习，效果取决于用户提问与筛选输出的能力。
- 高AI协作能力者绩效提升显著，低能力者收益微弱甚至倒退。
- 提供概念图等标准化流程可缩小差距，适合企业推广使用。
生成式人工智能（GenAI）正在重塑知识创造、处理与应用方式，但其对不同用户的影响差异尚不明确。我们开展一项随机对照实验，让参与者——类比早期职场知识工作者——通过传统资源或大型语言模型（LLM）协助自学技术领域。总体而言，获取GenAI显著提升了任务表现，但收益分布极不均衡。绩效提升无法由绩点或先验知识预测，而是由‘人工智能互动能力（AIC）’决定——即提取、筛选和验证模型输出的能力。高AIC参与者获得巨大收益；低AIC参与者仅获有限或负面边际回报。引入支架干预（概念图）后，结果方差下降，表明标准化工作流程可缓解AI赋能下的绩效不平等。研究从人机互补视角解读：GenAI虽提高平均生产力，却引入新的能力不平等维度。管理启示是，企业应将GenAI接入与简短的AIC微培训及简单标准操作流程结合，以实现价值稳定释放，避免采用不均。
原文摘要 · Abstract (English)
Generative Artificial Intelligence (GenAI) is transforming how firms create, process, and apply knowledge, yet little is known about the heterogeneity of its productivity effects across users. We report results from a randomized controlled experiment in which participants-analogs of early-career knowledge workers-were assigned to self-study a technical domain using either traditional resources or large-language-model (LLM) assistance. On average, GenAI access significantly increased task performance, but the distribution of gains was highly uneven. Improvements were not predicted by GPA or prior knowledge, but by \textit{AI Interaction Competence (AIC)} -- the ability to elicit, filter, and verify model outputs. High-AIC participants realized outsized gains; low-AIC participants saw limited or even negative marginal returns. A scaffolding intervention (conceptual maps) reduced outcome variance, indicating that standardized workflows can mitigate inequality in AI-mediated performance. We interpret these findings through the lens of human-AI complementarities: GenAI raises mean productivity while introducing a new axis of capability inequality. Managerially, firms should pair GenAI access with short AIC micro-training and simple standard operating procedures to capture value consistently and avoid uneven adoption outcomes.
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