NBER Working Papers · 2026 · [{"name": "Christopher Campos", "affiliation": []}, {"name": "John D. Singleton", "affiliation": []}]
中文摘要
尽管生成式人工智能工具在教育场景中的应用已迅速普及，但新出现的证据表明，其对学习的影响将取决于这种使用如何得到支持和引导。本文报告了一项针对K-12学校校长的原创性全国调查结果，该调查旨在通过政策、教师培训、学生使用指导、领导层参与以及AI赋能工具的可获得性，衡量学校对AI的制度整合程度。我们发现，AI的使用已在学校间迅速扩散，主要作为一种提高生产力的辅助工具。学生主要将AI用于作业帮助和写作，而教育工作者则主要将其用于课程规划和行政任务。教师培训、使用指导和学校政策的制定滞后于AI的普及速度。我们接下来记录了学校之间的两个扩散差距：第一，较低的AI整合程度与较高的弱势学生占比相关（弱势程度每增加一个标准差，AI整合指数得分相应降低0.07-0.11个标准差）；第二，私立学校和特许学校在AI整合指数上的得分比传统公立学校低0.23-0.44个标准差。尽管调查中的若干学校层面因素能强烈预测AI整合程度，但它们对解释上述差距作用甚微。学区规模的差异可解释公立学校之间弱势差距的大约三分之一。这些发现表明，与更高AI整合程度相关的因素，不同于缩小学校在支持和引导AI使用方面的差异所需的因素。
Abstract
Although use of generative AI tools has quickly become widespread in education settings, emerging evidence suggests that effects on learning will depend on how that use is supported and guided. This paper reports findings from an original national survey of K-12 school principals designed to measure institutional integration of AI in schools through policies, teacher training, guidance for student use, leadership engagement, and the availability of AI-enabled tools. We find that AI use has spread rapidly across schools, largely as a productivity aid. Students mainly use AI for homework help and writing, while educators primarily use it for lesson planning and administrative tasks. The development of teacher training, guidance, and school policies has lagged adoption. We next document two diffusion gaps across schools: First, lower AI integration is associated with a higher share of disadvantaged students (a one standard deviation increase in disadvantage is associated with a 0.07-0.11 SD lower score on an index of AI integration); Second, private and charter schools score 0.23-0.44 SD lower on the AI integration index than traditional public schools. Although several surveyed school-level factors strongly predict AI integration, they do little to explain these gaps. Differences in district size account for roughly one-third of the disadvantage gap between public schools. These findings suggest that the factors associated with greater AI integration differ from those needed to narrow disparities in how schools support and guide AI use.