ChatGPT自学工程课，拿了个B，表现亮眼但仍有短板。
The Lazy Student's Dream: ChatGPT Passing an Engineering Course on Its Own
- 用真实学生模式测试，模拟低投入下LLM完成课程任务
- 总分82.24%，接近班级平均分84.99%，结构化任务表现最佳
- 适合关注AI教育应用与课程设计改革的研究者
本文系统评估了大型语言模型（LLMs）在完整学期本科控制工程课程中的表现。通过115项课程任务的评估，采用“低投入”协议模拟真实学生使用场景，涵盖自动评分选择题、复杂Python编程和长篇分析写作等多种形式。研究揭示了大模型在数学推导、编程挑战与理论理解方面的优劣。结果显示，该模型获得82.24%的成绩，接近班级均分84.99%，在结构化任务中表现突出，在开放性项目中存在明显不足。研究为应对AI发展对工程教育的影响提供了量化依据，倡导从简单禁止转向有策略地融合工具教学。附加材料包括课程大纲、试卷、设计项目及示例作答，详见项目网站：https://gradegpt.github.io。
原文摘要 · Abstract (English)
This paper presents a comprehensive investigation into the capability of Large Language Models (LLMs) to successfully complete a semester-long undergraduate control systems course. Through evaluation of 115 course deliverables, we assess LLM performance using ChatGPT under a "minimal effort" protocol that simulates realistic student usage patterns. The investigation employs a rigorous testing methodology across multiple assessment formats, from auto-graded multiple choice questions to complex Python programming tasks and long-form analytical writing. Our analysis provides quantitative insights into AI's strengths and limitations in handling mathematical formulations, coding challenges, and theoretical concepts in control systems engineering. The LLM achieved a B-grade performance (82.24\%), approaching but not exceeding the class average (84.99\%), with strongest results in structured assignments and greatest limitations in open-ended projects. The findings inform discussions about course design adaptation in response to AI advancement, moving beyond simple prohibition towards thoughtful integration of these tools in engineering education. Additional materials including syllabus, examination papers, design projects, and example responses can be found at the project website: https://gradegpt.github.io.
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