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How UX researchers can partner with (and not be replaced by) AI [Advancing Research Community Workshop Series]
This video is featured in the AI Trends in User Research playlist.
Summary
Four of your research colleagues discussed and defended their respective positions (below) on the impact of AI on user research. Participants engaged in a discussion and Q&A, facilitated by Dr. Jamika D. Burge. “AI has the potential to be the researcher's best friend, by doing all the heavy lifting associated with analysis - but it also has the potential to cause unimaginable damage”. – Nick Fine “Researchers absolutely must learn to create AI prompts. Not only will prompt engineering become an essential, required research skill, but it will also offer a much-needed opportunity to rethink our role as facilitators of change.” – Alexandra Jayeun Lee Soon, AI will be able to utilize the participant's feedback as a prompt to create RITE design variations on the fly, offering the researcher multiple flow options organically and in real time, which will radically transform our research practice.” – Greg Nudelman “UX Researchers can reinvent themselves as “delightful ethicists” who oversee ethics on critical issues when generative AI supplies abundant solutions without providing the why." – Bo Wang
Key Insights
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AI poses both great risks and opportunities for UX research, with validity and bias being central concerns.
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Inexperienced researchers relying on AI outputs without critical judgment risk spreading inaccurate insights.
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Prompt engineering is emerging as a fundamental skill for UX researchers, similar to how coding became essential for designers.
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AI can automate repetitive design tasks and rapid prototyping, enabling faster iteration and freeing researchers for higher-level strategy.
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Moderated usability testing will not be fully replaced by AI due to the irreplaceable nature of human empathy and adaptive interaction.
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UX researchers should evolve into 'delightful ethicists' leading ethical AI practices and guarding against 'dark AI' and synthetic users.
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Ethics in AI extends beyond moral principles to everyday product decisions and protecting users from harm.
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Bias is inherent in AI training data and tools; researchers must call out, understand, and address such bias explicitly.
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Demonstrating tangible business value by linking user research to outcomes remains essential for maintaining researcher relevance.
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Collective learning across disciplines and broadening AI literacy within the research community is critical to navigate AI’s impact.
Notable Quotes
"AI has as much capacity to do as much harm as it does good."
"It takes an experienced researcher to be able to wrangle AI prompts to have any chance of getting good work."
"Learning prompt engineering will make us better researchers in the same way coding helped designers."
"The best large language model is right under your nose: the human brain with 300,000 years of ancestral development."
"AI gives us superpowers to do rapid iterative testing and evaluation much faster than before."
"We should reinvent UX researchers to be delightful ethicists leading ethical concerns in AI adoption."
"AI cannot do empathy; moderated usability testing requires the human ability to pivot and connect."
"AI is biased by design; assume bias is in there, call it out, and then address it."
"User research's value lies in linking user needs and pain points directly to business profit and loss."
"If we don’t advocate for understanding AI, more decisions will be made without researchers, leading to unjust outcomes."
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