LLM对学习的影响取决于使用方式：替代则学得多但懂得少，辅助则理解更深。
AI Meets the Classroom: When Do Large Language Models Harm Learning?
- 按需使用：替代学习会增加知识广度但降低深度。
- 辅助提问可提升理解，但不增加覆盖范围。
- 高基础学生受益更多，可能拉大差距。
大型语言模型（LLMs）在教育中的影响存在争议。通过两项预注册且有激励的实验室实验，我们发现LLMs对整体学习效果无显著影响。探索性分析和一项实地研究显示，其影响取决于使用行为：将学习活动替换为使用LLM（如生成习题答案）的学生，虽能掌握更多主题，但理解深度下降；而以LLM辅助学习（如询问解释）的学生，理解能力提升，但主题覆盖面未增加。此外，发现LLM加剧了低基础与高基础学生之间的差距。尽管LLM具有提升学习的潜力，但其应用需根据教育场景和学生需求定制。
原文摘要 · Abstract (English)
The effect of large language models (LLMs) in education is debated: Previous research shows that LLMs can help as well as hurt learning. In two pre-registered and incentivized laboratory experiments, we find no effect of LLMs on overall learning outcomes. In exploratory analyses and a field study, we provide evidence that the effect of LLMs on learning outcomes depends on usage behavior. Students who substitute some of their learning activities with LLMs (e.g., by generating solutions to exercises) increase the volume of topics they can learn about but decrease their understanding of each topic. Students who complement their learning activities with LLMs (e.g., by asking for explanations) do not increase topic volume but do increase their understanding. We also observe that LLMs widen the gap between students with low and high prior knowledge. While LLMs show great potential to improve learning, their use must be tailored to the educational context and students' needs.
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