中文摘要
生成式人工智能被誉为能够通过为每名学生提供私人辅导教师而变革教育的技术。我们提供了首批大规模实验证据之一，来自在田纳西州18所初中开展的一项为期两年的整群随机试验：被随机分配的学生在学校原有的每日数学补习课上使用可搭载人工智能辅导工具Khanmigo的可汗学院平台，Khanmigo被设置为引导学生，而非直接给出答案。被分配使用该工具使学生每学期的数学成绩提高1.3个全国百分位排名，相当于一学年提高约0.06至0.08个标准差；由此推算，完整参与一学年的效应可达0.14个标准差。这些提升与不借助人工智能的可汗学院练习所产生的提升相近。一种解释是，学生很少使用该辅导工具，而且即使使用，也很少与其开展实质性的数学对话：96%的学生至少尝试过一次Khanmigo，但处于中位数的学生仅在三分之一的练习日向其发送消息，并且在答错题目的练习环节中，只有17%的环节使用了该工具。学生实际发送的消息大多只是孤立的答案，或仅仅点击了建议提示。具有约束力的关键因素似乎是参与度：要实现人工智能辅导的潜力，需要让学生真正使用它，而不仅仅是向他们提供使用权限。
Abstract
Generative AI has been promoted as the technology that could transform education by providing every student a personal tutor. We provide some of the first large-scale experimental evidence, from a two-year cluster randomized trial in 18 Tennessee middle schools in which randomly assigned students used Khan Academy with its AI tutor, Khanmigo, configured to coach rather than give answers, during existing daily remedial mathematics sessions. Assignment raises math achievement by 1.3 national percentile ranks per term, or about 0.06 to 0.08 standard deviations over a school year; the implied effect of a full year of active participation reaches 0.14 standard deviations. These gains resemble those from Khan Academy practice without AI assistance. One explanation is that students used the tutor infrequently and, when they did, rarely engaged it in substantive mathematical dialogue: 96 percent of students tried Khanmigo at least once, but the median student messaged it on only a third of the days they practiced, and in only 17 percent of the exercise sessions in which they made a mistake. Messages that students did send were mostly bare answers or clicks on suggested prompts. The binding constraint appears to be engagement: realizing the promise of AI tutoring will require getting students to use it, not just giving them access.