LLMs expressed unjustified confidence in recommendations based on visual inputs, asserting incorrect pin-connection diagnoses with high stated certainty¶

Claim · All claims
Evidence · 1 study · qualitative r2 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2029: tool and model accuracy changes quickly

Subclaims¶

q2 i? ChatGPT claimed 99% confidence in a wrong op-amp pin diagnosis and made three false statements about the PCB after a second photo. → John Hu and Andrew J. Ash 2026

Evidence¶

John Hu and Andrew J. Ash 2026¶

John Hu and Andrew J. Ash. (2026). Chat Debugging: An Exploratory Study of Human-AI Collaboration to Debug Analog Circuits. https://arxiv.org/abs/2608.02955 doi:10.48550/arxiv.2608.02955

q2 · i? · qualitative · r2

In Student C's chat log debugging the probe-setting problem (P6) with an unknown ChatGPT version, the model claimed "I'm 99% confident: wrong op-amp pins → wrong feedback loop → gain ≈ 1" while the board was correctly wired to pins 1, 2, and 3; all three follow-up statements about the PCB were false.

"Its tone of confidence is concerning, especially when it claimed, "I can mark exactly where each connection should go" after a major visual recognition error."

Discussion¶

Related Claims¶

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