cross-posted from: https://hexbear.net/post/9348639
The Generative AI Learning Penalty: Evidence from Chinese Secondary Education
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.
I hold the belief that LLMs are the final nail in the coffin of an results-oriented education model that has become outmoded. I have personally experienced cognitive dissonance between my idealised notion of education and knowledge acquisition on one hand, and on the other the reality of stress, lack of reward for going beyond “good enough” and fear of my perfectionism yet slowness making me unable to catch up.
One way I keep myself from collapsing from all the things to worry about is that I try to focus on a few things I feel passionate about: the future of education is one of them.