用大模型打造智能助教，自动批改电路分析作业并提供个性化反馈。
WIP: Large Language Model-Enhanced Smart Tutor for Undergraduate Circuit Analysis
- 基于大模型设计智能助教，支持开放题作答与作业反馈生成。
- 90.9%学生满意度，可统计题目使用频率与高频问题。
- 适合工程教育研究者及课程改革实践者参考。
本篇正在进行的研究实践论文介绍了一款面向本科电路分析课程的AI智能助教系统，用于作业评估与反馈。系统设计包含开放性问题回答与作业反馈生成模块，通过精心设计的提示词优化不同问题的响应效果。该助教已部署于微软Azure平台，并在东南部一所大型公立研究型大学的电气与计算机工程学院正式授课中使用。除了提供个性化教学与反馈外，系统还收集学生交互数据，汇总后分享给授课教师。为评估有效性，我们收集了学生反馈，其中90.9%表示满意。此外，通过对初步电路分析主题的数据子集分析，我们统计了各题目的使用频率并识别出高频提问，帮助教师实时掌握学生难点，实现更精准的课堂教学。未来工作将在2025年春季学期结束后完成完整数据分析。我们还探索该助教在更广泛工程学科中的应用潜力，包括改进提示词、图像识别方法和数据库管理策略，这些均处于持续研究阶段。
原文摘要 · Abstract (English)
This research-to-practice work-in-progress (WIP) paper presents an AI-enabled smart tutor designed to provide homework assessment and feedback for students in an undergraduate circuit analysis course. We detail the tutor's design philosophy and core components, including open-ended question answering and homework feedback generation. The prompts are carefully crafted to optimize responses across different problems. The smart tutor was deployed on the Microsoft Azure platform and is currently in use in an undergraduate circuit analysis course at the School of Electrical and Computer Engineering in a large, public, research-intensive institution in the Southeastern United States. Beyond offering personalized instruction and feedback, the tutor collects student interaction data, which is summarized and shared with the course instructor. To evaluate its effectiveness, we collected student feedback, with 90.9% of responses indicating satisfaction with the tutor. Additionally, we analyze a subset of collected data on preliminary circuit analysis topics to assess tutor usage frequency for each problem and identify frequently asked questions. These insights help instructors gain real-time awareness of student difficulties, enabling more targeted classroom instruction. In future work, we will release a full analysis once the complete dataset is available after the Spring 2025 semester. We also explore the potential applications of this smart tutor across a broader range of engineering disciplines by developing improved prompts, diagram-recognition methods, and database management strategies, which remain ongoing areas of research.
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