Study Reveals Human-AI Collaboration Gaps in Debugging Analog Circuits
A new exploratory study investigates the effectiveness of using large language models (LLMs) in debugging malfunctioning analog circuits. The research, conducted with undergraduates, found that while LLMs offer considerable domain knowledge and sensible debugging suggestions, they exhibit limitations in image-based reasoning and an unjustified tone of confidence. The study also highlights students' deficits in fundamental concepts and critical thinking during human-AI collaborative debugging.
Key facts
- The study focuses on 'Chat Debugging' using public-domain LLMs.
- Participants were undergraduates debugging analog circuits on breadboards and PCBs.
- The study analyzed voluntarily shared chat logs under exam and time pressure.
- Multimodal usage patterns by students were discovered.
- LLMs provided considerable domain knowledge and debugging suggestions.
- Major gaps identified include LLMs' limitations in 2D/3D image-based reasoning.
- LLMs displayed an unjustified tone of confidence.
- Students showed deficits in fundamental concepts and critical thinking.
Entities
Institutions
- arXiv