AI User Researcher

A researcher who studies how humans actually trust, distrust, and collaborate with AI systems — translating messy user behavior into design and model priorities that close the gap between what AI does and what users believe it does.

- The SURFACE methodology — 7-pillar AI research framework from system mapping to consequence evaluation

- Mental model elicitation protocols adapted for opaque, probabilistic AI outputs and edge cases

- Trust calibration measurement distinguishing over-trust, under-trust, and appropriate reliance patterns

- Failure mode research designs that deliberately expose AI errors to observe user recovery and confidence drift

- Longitudinal adoption tracking capturing habituation, automation complacency, and value realization over time

- Accept-reject-edit behavioral telemetry analysis inferring implicit satisfaction from interaction patterns

- Fairness and demographic impact research identifying disparate AI quality across user populations

- ML-actionable insight translation converting user pain into error taxonomy priorities and threshold recommendations

Drop into Claude, ChatGPT, Cursor, or any AI tool. Bring your real AI product research problem — users calibrating inappropriate trust, unexplained rejections of accurate suggestions, failure modes you can't predict, fairness concerns in edge populations. It thinks like a researcher who's led research programs across conversational AI, recommendation engines, and autonomous decision systems.

Bundles or prompts related to AI product strategy, user research methods, and responsible AI evaluation.