这项研究戳破了“AI让学习更高效”的幻觉——做教育产品、设计学习工具的人，看完会重新思考AI辅助的边界。建议点开，了解为什么“快”不等于“学得好”。
一项基于320万条ALEKS数学学习记录的研究发现，ChatGPT出现后，学生完成AI友好型数学题（如文字题）的速度显著加快，但学习效果反而下降。研究者指出，数学学习需要通过选择表征、测试步骤、犯错和纠错的摩擦过程来构建知识，而AI直接提供路径让学生跳过了这一关键环节。高中和大学生在AI友好型题目上的时间减少尤为明显，而低年级学生变化较小。在监考测试中，学生对AI友好题目的正确率下降了约25%，表明表面上的效率提升是以牺牲长期记忆为代价的。
Students finish AI-friendly math problems faster, …
Students finish AI-friendly math problems faster, but they seem to learn less from them.
The researchers studied 3.2 million ALEKS math learning records across 10 years to see what changed after ChatGPT became available.
Finishing faster is not automatically learning more efficiently, because math practice builds knowledge through the friction of choosing a representation, testing a step, making an error, and correcting it.
When a chatbot supplies the path, the student may still submit the answer, but the mind has skipped the work that turns exposure into memory.
They compare word problems, which students can easily paste into an AI chatbot, with graph problems, which are harder to hand off because they require visual work inside the platform.
After ChatGPT, high school and college students spent much less time on the AI-friendly word problems, while younger students showed smaller or no change.
This time drop disappeared when tests were proctored, which suggests the faster work was not just students getting better or the platform changing.
The learning cost showed up later: on proctored retention questions, students became about 25% less likely to answer AI-friendly items correctly, even though they looked better on non-proctored items where AI could still help.
----
Paper Link – arxiv. org/abs/2605.21629
Paper Title: "Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build"