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The Emerging Use of GenAI for UX Research in Software Development: Challenges and Opportunities
arXiv:2512.15944v1 Announce Type: new
Abstract: The growing adoption of generative AI (GenAI) is reshaping how user experience (UX) research teams conduct qualitative research in software development, creating opportunities to streamline the production of qualitative insights. This paper presents findings from two user studies examining how current practices are challenged by GenAI and offering design implications for future AI assistance. Semi-structured interviews with 21 UX researchers, product managers, and designers reveal challenges of aligning AI capabilities with the interpretive, collaborative nature of qualitative research and tensions between roles. UX researchers expressed limited trust in AI-generated results, while product managers often overestimated AI capabilities, amplifying organizational pressures to accelerate research within agile workflows. In a second study, we validated an AI analysis approach more closely aligned with human analysis processes to address trust issues bottoms-up. We outline interaction patterns and design guidelines for responsibly integrating AI into software development cycles.
Abstract: The growing adoption of generative AI (GenAI) is reshaping how user experience (UX) research teams conduct qualitative research in software development, creating opportunities to streamline the production of qualitative insights. This paper presents findings from two user studies examining how current practices are challenged by GenAI and offering design implications for future AI assistance. Semi-structured interviews with 21 UX researchers, product managers, and designers reveal challenges of aligning AI capabilities with the interpretive, collaborative nature of qualitative research and tensions between roles. UX researchers expressed limited trust in AI-generated results, while product managers often overestimated AI capabilities, amplifying organizational pressures to accelerate research within agile workflows. In a second study, we validated an AI analysis approach more closely aligned with human analysis processes to address trust issues bottoms-up. We outline interaction patterns and design guidelines for responsibly integrating AI into software development cycles.
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