聊天调试：人机协作调试模拟电路的探索性研究
Chat Debugging: An Exploratory Study of Human-AI Collaboration to Debug Analog Circuits
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中文总结 AI 辅助
该研究通过分析本科生与开源大型语言模型（LLMs）协作调试模拟电路的聊天记录，揭示了人机协作调试的模式、LLMs的优势与不足及学生的技能短板，为优化人机协作调试提供了依据。
中文摘要 AI 辅助
本研究论文报告了一项关于聊天调试有效性的探索性研究：本科生通过与开源大型语言模型（LLMs）对话，对面包板和印刷电路板（PCB）上故障的模拟电路进行故障排查。通过对学生在考试和时间压力下调试预设故障电路时自愿分享的聊天记录进行主题分析，我们发现学生存在多模态使用模式，且现成的LLMs能提供可观的领域知识和合理的调试建议。同时，我们也识别出人机协作调试中LLM技术和学生技能的主要差距，例如LLMs在2D/3D图像推理方面的局限性、无依据的自信语气，以及学生在基础概念和批判性思维上的不足。
英文摘要
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.
发表机构
- School of Electrical and Computer Engineering（电气与计算机工程学院）
- Oklahoma State University（俄克拉荷马州立大学）
机构由 AI 辅助整理，请以论文原文为准。
