Chat Debugging: An Exploratory Study of Human-AI Collaboration to Debug Analog Circuits

John Hu; Andrew Ash

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arXiv · 2608.02955

Chat Debugging: An Exploratory Study of Human-AI Collaboration to Debug Analog Circuits

John Hu·Andrew Ash

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Abstract

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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John Hu, Andrew Ash. 2026-08-03. Chat Debugging: An Exploratory Study of Human-AI Collaboration to Debug Analog Circuits. https://arxiv.org/abs/2608.02955

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