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This study investigates the use of large language models (LLMs) in assisting undergraduate students with debugging analog circuits, revealing both the strengths and weaknesses of human-AI collaboration in this context. Through thematic analysis of chat logs, the researchers identified effective multimodal interactions and valuable suggestions from LLMs, despite significant gaps in image-based reasoning and critical thinking skills among students. The findings highlight the potential of LLMs to enhance technical troubleshooting while also underscoring the need for improved educational frameworks to address students' knowledge deficits.
LLMs can provide insightful debugging suggestions, but their limitations in visual reasoning expose critical gaps in students' foundational knowledge.
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.