大学生用大模型调试电路，发现人机协作既高效又存短板。
Chat Debugging: An Exploratory Study of Human-AI Collaboration to Debug Analog Circuits
- 学生通过对话让大模型协助排查面包板和PCB上的故障电路。
- 大模型能提供合理建议，但对图像推理能力有限且语气过于自信。
- 适合电子工程初学者、教学研究者及人机协同调试场景参考。
本研究探索了基于大语言模型（LLMs）的聊天式调试（Chat Debugging）在模拟电路故障排查中的有效性。通过分析本科生在考试压力下对预设故障电路进行调试时自愿提交的聊天记录，发现学生采用多模态方式与模型互动，而主流大模型能提供大量领域知识和合理调试建议。然而，研究也揭示了显著的技术与技能鸿沟：大模型在二维/三维图像理解方面存在局限，常以不合理的自信语气表达观点；同时，学生普遍存在基础概念薄弱和批判性思维不足的问题。
原文摘要 · Abstract (English)
This research paper describes an exploratory study on the effectiveness of Chat Debugging: troubleshooting malfunctioning analog circuits on breadboards and printed circuit boards (PCB) by undergraduates through conversations with public-domain large language models (LLMs). Through thematic analysis of students' voluntarily shared chat logs when debugging pre-determined buggy circuits under exam and time pressure, we discovered multimodal usage patterns by students and considerable domain knowledge and sensible debugging suggestions offered by off-the-shelf LLMs. Meanwhile, we also identified major gaps in LLM technologies and students' skills during human-AI collaborative debugging, such as LLMs' limitations in 2D/3D image-based reasoning, unjustified tone of confidence, and students' deficits in fundamental concepts and critical thinking.
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