让学生教大模型解题，提升编程学习效果
Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education
- 学生扮演教师角色，用设计好的知识缺口问题训练大模型
- 实验显示学生成绩显著优于历史对照组
- 适合希望提升主动学习能力的计算机课程师生
尽管大型语言模型（LLMs）常被用作计算机科学教育中的虚拟助教，但这种模式易导致被动学习和过度依赖。本文提出一种新型教学范式：将学生置于教师角色，要求其指导大模型解决实际问题。为此，我们设计了包含刻意知识缺口的问题策略，并构建Socrates系统以低开销实现该方法。在本科生课程中评估表明，与历史同侪相比，该主动学习方法显著提升了学生表现。研究展示了利用大模型深化学生参与和掌握知识的可行、低成本框架。
原文摘要 · Abstract (English)
While Large Language Models (LLMs) are often used as virtual tutors in computer science (CS) education, this approach can foster passive learning and over-reliance. This paper presents a novel pedagogical paradigm that inverts this model: students act as instructors who must teach an LLM to solve problems. To facilitate this, we developed strategies for designing questions with engineered knowledge gaps that only a student can bridge, and we introduce Socrates, a system for deploying this method with minimal overhead. We evaluated our approach in an undergraduate course and found that this active-learning method led to statistically significant improvements in student performance compared to historical cohorts. Our work demonstrates a practical, cost-effective framework for using LLMs to deepen student engagement and mastery.
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