用AI辅助大学数学课答疑，能快速给出贴近教材的答案。
AI Meets Mathematics Education: A Case Study on Supporting an Instructor in a Large Mathematics Class with Context-Aware AI
- 用2588条师生对话微调轻量模型，回答论坛问题。
- 答对率75.3%，36%情况比老师答案更好或相当。
- 适合想提升大班教学效率的教师与教育科技研究者。
大规模高校课程长期面临及时、可扩展教学支持的挑战。尽管生成式AI具有潜力，但其有效应用依赖于可靠性和教学一致性。我们通过与课程教师紧密合作，开展了一项关于计算导论课中人工智能辅助支持的人本案例研究。开发了一个系统，用于回答学生在讨论区提出的问题，该系统基于2,588条历史师生互动数据，对一个轻量级语言模型进行微调。模型在由五位教师标注的150个代表性问题基准测试中达到75.3%的准确率，在36%的情况下，其回答被评价为等于或优于教师原答案。部署后对学生问卷调查（N = 105）显示，学生重视回答与课程材料的一致性及即时可用性，但仍依赖教师验证以建立信任。研究强调了混合人机工作流在安全高效课程支持中的重要性。
原文摘要 · Abstract (English)
Large-enrollment university courses face persistent challenges in providing timely and scalable instructional support. While generative AI holds promise, its effective use depends on reliability and pedagogical alignment. We present a human-centered case study of AI-assisted support in a Calculus I course, implemented in close collaboration with the course instructor. We developed a system to answer students' questions on a discussion forum, fine-tuning a lightweight language model on 2,588 historical student-instructor interactions. The model achieved 75.3% accuracy on a benchmark of 150 representative questions annotated by five instructors, and in 36% of cases, its responses were rated equal to or better than instructor answers. Post-deployment student survey (N = 105) indicated that students valued the alignment of the responses with the course materials and their immediate availability, while still relying on the instructor verification for trust. We highlight the importance of hybrid human-AI workflows for safe and effective course support.
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