# Generative AI hurts test scores more than it helps in Chinese > Source: > Published: 2026-08-20 00:59:55+00:00 # Generative AI hurts test scores more than it helps in Chinese The mechanism isn't mysterious. Researchers logged every prompt. Students who asked for step-by-step solutions ("solve 3x² + 7x - 6 = 0") saw the biggest drops. Those who used the model for concept checks ("why does the discriminant tell us about roots?") held steady. The paper calls it "cognitive offloading" — when the tool does the reasoning, the brain stops building the pathway. What surprised me: the penalty persisted even after controlling for prior achievement, teacher quality, and home resources. The effect was strongest in the bottom quartile. Top students actually gained slightly (+0.04 SD), probably because they already knew how to interrogate a model instead of copying it. The study design matters. This wasn't a lab experiment with toy problems. Teachers integrated the tool into regular assignments — three problem sets per week, graded normally. The LLM ran on a local server, no internet distraction, Chinese-language interface tuned to the national curriculum. As close to "real deployment" as you'll get in published literature. Two practical takeaways for anyone building or buying ed-tech: **Prompt design is curriculum design.** The wrapper gave zero guardrails. A simple system prompt — "never give the final answer; ask a guiding question instead" — would likely flip the sign. The paper's appendix shows a pilot where they added that constraint to 200 students: penalty vanished, slight positive emerged. **Assessment must change.** If homework counts toward grades and the model solves it, you're grading the model, not the student. The schools that kept the penalty low shifted to in-class quizzes and oral explanations. One teacher told the researchers: "I stopped collecting worksheets. I ask them to teach the problem to a partner. The model can't do that for them." The paper's still in revision at *Journal of Educational Psychology*. Preprint's on SSRN if you want the full tables. But the headline holds: dropping a general-purpose LLM into a traditional homework loop backfires for the kids who need support most. The fix isn't banning the tool — it's redesigning the loop around it. [Next Technical leaders who don't leave a massive AI exhaust trail are →](/en/news/6995/) [these AI tool field notes](https://tanyan888.com/), with plenty of directly applicable cases.