IA para investigadores de UX

UX research produces a lot of unstructured data: interview recordings, usability session notes, open-ended survey answers, support tickets and app reviews. AI helps with several stages. Speech recognition turns recordings into transcripts. Language models summarize sessions, suggest themes and draft affinity groupings. Classification models and embeddings organize thousands of feedback items. Research repositories such as Dovetail and Condens have added AI features for tagging and summarizing. AI is also useful for planning. It can draft research plans, interview guides, screeners and consent-form language, which researchers then review for leading questions and bias. The most debated practice is using synthetic users: language-model personas that answer interview questions as if they were customers. They are fast and cheap, but their answers reflect patterns in training data, not lived experience. They tend to be agreeable and articulate and to give plausible, averaged answers. They cannot reveal the surprising workaround, emotional reaction or context a real participant brings. Nor can they show what people do rather than say, which is the core of usability testing. Practitioners, including Nielsen Norman Group, have cautioned against treating synthetic responses as research findings. Their reasonable uses are narrower: piloting questions, generating hypotheses to test, or preparing for sessions. A common misconception is that an AI summary is the same as synthesis. Real synthesis weighs evidence, compares sources, notices contradictions and ties findings to product decisions. Summaries also tend to smooth over minority views and outliers, which are often where the most useful insights are. Models can also misattribute or invent quotes. Finally, research ethics apply: participants' consent should cover AI processing of their data, and personal information should be handled according to the organization's privacy policies.