Large Language Models in Qualitative Research: Uses, Tensions, and Intentions
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3706598.3713120.pdf
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Author(s)
Schroeder, Hope
Randazzo, Casey
Mimno, David
Schoenebeck, Sarita
Le Quéré, Marianne Aubin
Date Issued
April 25, 2025
Publisher
ACM|CHI Conference on Human Factors in Computing Systems
Citation
Hope Schroeder, Marianne Aubin Le Quéré, Casey Randazzo, David Mimno, and Sarita Schoenebeck. 2025. Large Language Models in Qualitative Research: Uses, Tensions, and Intentions. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI '25). Association for Computing Machinery, New York, NY, USA, Article 481, 1–17.
Version
Final published version
Abstract
Qualitative researchers use tools to collect, sort, and analyze their
data. Should qualitative researchers use large language models
(LLMs) as part of their practice? LLMs could augment qualitative
research, but it is unclear if their use is appropriate, ethical, or
aligned with qualitative researchers’ goals and values. We interviewed twenty qualitative researchers to investigate these tensions.
Many participants see LLMs as promising interlocutors with attractive use cases across the stages of research, but wrestle with their
performance and appropriateness. Participants surface concerns
regarding the use of LLMs while protecting participant interests,
and call attention to an urgent lack of norms and tooling to guide
the ethical use of LLMs in research. We document the rapid and
broad adoption of LLMs across surfaces, which can interfere with
intentional use vital to qualitative research. We use the tensions
surfaced by our participants to outline recommendations for researchers considering using LLMsin qualitative research and design
principles for LLM-assisted qualitative research tools.
Description
CHI ’25, Yokohama, Japan
MIT Department
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1145/3706598.3713120
