WIP: Chat-Debugging: Large Language Model as a Hardware Debugging Assistant

Synthesis: Work-in-progress exploring LLMs as debugging assistants for physical hardware lab courses. Proposes 'Chat-Debugging' where students interact with an LLM to diagnose circuit faults. Aims to reduce frustration and improve debugging skill development. Initial prototype tested in an undergraduate hardware course; preliminary results suggest LLM assistance helps students identify faults faster and provides just-in-time scaffolding without giving away solutions. Large Language Models (LLMs), Scaffolding, CS Education, STEM Education, and Student Experience. Work-in-progress exploring LLMs as debugging assistants for physical hardware lab courses. Proposes 'Chat-Debugging' where students interact with an LLM to diagnose circuit faults. Aims to reduce frustration and improve debugging skill development. Initial prototype tested in an undergraduate hardware course; preliminary results suggest LLM assistance helps students identify faults faster and provides just-in-time scaffolding without giving away solutions.

What this means for practice

• Learners. Expect hardware debugging with an LLM to take multiple rounds, not one prompt: in the study, software bugs were usually resolved in a single exchange while circuit faults required repeated test-plan–result cycles shared back with the assistant.
• Learners. Treat every LLM suggestion as a hypothesis to verify against the board: the assistant offered several potential root causes per bug and proposed incorrect ones, and the mandatory real-world check is what made the interaction productive.
• Learners. Correct the assistant assertively and keep the conversation's model of the circuit accurate — it read GP15 as a power input until the student clarified, and only consistent human feedback kept its guidance usable.
• Learners. Log each test and its result in the chat so hypotheses are eliminated rather than repeated, and so a session interrupted by time constraints can be resumed without redoing measurements.

Limitations

• Evidence comes from a single fourth-year undergraduate (Daniel) using GPT-4o on authentic bugs in his own coursework; the authors state that more participants are needed to improve the validity of the results.
• Outcomes were his self-reported debugging confidence and time spent debugging, not a measured gain in debugging skill, and there was no control or comparison condition.
• It is a work-in-progress conference paper with an open-ended protocol and no standardized bug set; the authors report a planned follow-up that gives multiple students researcher-created bugs and scores final circuit performance and time quantitatively.

Citation

Andrew Ash, & John Hu (2026). WIP: Chat-Debugging: Large Language Model as a Hardware Debugging Assistant. IEEE Frontiers in Education Conference (FIE) 2026.