The Generative AI Learning Penalty: Evidence from Chinese Secondary Education

Strömberg, David, Victor Lei and Yanhui Wu

No 21577, CEPR Discussion Papers from Centre for Economic Policy Research

Abstract: Using 30 months of panel data on 26,811 Chinese students in grades 7--12, we study how generative AI affects homework productivity and learning. The data combine monthly closed-book exams, high-school and college entrance exams, and homework scores and completion time across nine subjects. We exploit staggered AI adoption in a difference-in-differences design. AI adoption raises homework scores by 18% and reduces completion time by 30%, but lowers monthly exam scores by 20% within six months. High-stakes entrance-exam scores fall by 18 and 24%, with the full penalty emerging only after about two years. The losses are largest in social science subjects, followed by STEM and languages, and are especially large for junior students, high-achieving students, and boys. The learning losses are concentrated among roughly 80% of AI users whose behavior is consistent with homework outsourcing, as indicated by exceptionally short homework completion time coupled with high homework scores. AI users who maintain similar homework completion time as non-AI users experience small learning losses.

Keywords: Generative AI; Education; Human capital accumulation; China shock (search for similar items in EconPapers)
JEL-codes: I20 O15 O33 (search for similar items in EconPapers)
Date: 2026-06
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