Econ Papers Daily / 机构研究
不是更快，而是更好：生成式人工智能与秘鲁教师技能
Not Faster, but Better: Generative AI and Teacher Skill in Peru
摘要
Generative AI is being deployed across the public sectors of developing countries on the premise that it makes workers faster. This paper tests this in a randomized trial with 1,269 sixth-grade teachers across 390 public schools in Lima, Peru. A low-cost program—a one-day training, an AI license, and eight months of light-touch follow-up—raised adoption sharply yet saved teachers no time on any of their core tasks, with precisely estimated nulls throughout. The gains lay elsewhere: treated teachers wrote markedly better prompts, including on a rubric designed independently of the training, and were sharper at catching weak AI outputs. These gains were largest for older teachers, who began furthest behind, narrowing rather than widening skill gaps. Taken together, the results indicate that AI training primarily develops the skills needed to generate and evaluate high-quality AI outputs, rather than reducing the time teachers spend on their work.
中文摘要
生成式人工智能正被部署于发展中国家的公共部门，其前提是它能让工作者更快。本文通过在秘鲁利马390所公立学校的1269名六年级教师中开展的一项随机试验对此进行检验。一项低成本项目——为期一天的培训、一份人工智能许可证以及八个月的轻触式后续跟进——大幅提高了采用率，却未能在教师的任何核心任务上节省时间，且各处均得到精确估计的零效应。收益体现在其他方面：接受处理的教师写出了明显更好的提示词，包括在一份独立于培训设计的评分标准上也是如此，并且在识别薄弱的人工智能输出方面更为敏锐。这些收益在年龄较大的教师中最大，他们起初落后最多，从而缩小而非扩大了技能差距。综合来看，结果表明，人工智能培训主要培养的是生成和评估高质量人工智能输出所需的技能，而非减少教师花在工作上的时间。