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UX researchers can't trust AI screeners and analysis that miss nuance and junk responses
The pain
We're finding that AI tools for research, from participant screeners to open-end coding, consistently fail at the tasks that require human judgment. Screeners use the wrong logic, and LLM-based analysis of survey responses misses themes, overlaps categories, and overlooks important nuance while also failing to exclude junk data. This forces us to redo the work manually, so the promised time savings never materialize, and we can't rely on the output for decisions.
What people said · 22 complaints, 18 people
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