用大模型打造互动编程助教，帮学生理解概念而非直接给答案
Multimodal Programming in Computer Science with Interactive Assistance Powered by Large Language Model
- 基于DeepSeek R1构建交互式助教，通过个性化提示引导学习
- 能识别学生概念盲点并提供适切的解题思路与模板代码
- 适合编程初学者、教育技术研究者及课程设计者参考
大型语言模型聊天机器人界面使学生能够即时获得作业帮助，但若使用不当可能偏离教学目标。本研究开发并首次在大规模计算机科学入门编程课程中实施了基于DeepSeek R1的互动作业助教系统。除了知名代码编辑器中的辅助按钮外，系统还集成命令行自动评测器的反馈功能。该助教将学生作业包裹在个性化提示中，推动教育目标实现，而不直接给出答案。我们发现助教能识别学生的概念困难，并以符合教学逻辑的方式提供思路、计划和模板代码。然而，系统仍存在误判正确代码为错误，或鼓励使用虽正确但不符合课程要求方法的问题，可能导致学生陷入冗长且挫败的学习路径。针对开发与部署中的诸多问题，我们提出结论与未来改进方向。
原文摘要 · Abstract (English)
LLM chatbot interfaces allow students to get instant, interactive assistance with homework, but doing so carelessly may not advance educational objectives. In this study, an interactive homework help system based on DeepSeek R1 is developed and first implemented for students enrolled in a large computer science beginning programming course. In addition to an assist button in a well-known code editor, our assistant also has a feedback option in our command-line automatic evaluator. It wraps student work in a personalized prompt that advances our educational objectives without offering answers straight away. We have discovered that our assistant can recognize students' conceptual difficulties and provide ideas, plans, and template code in pedagogically appropriate ways. However, among other mistakes, it occasionally incorrectly labels the correct student code as incorrect or encourages students to use correct-but-lesson-inappropriate approaches, which can lead to long and frustrating journeys for the students. After discussing many development and deployment issues, we provide our conclusions and future actions.
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