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Large Language Models in Qualitative Research: Uses, Tensions, and Intentions

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arxiv 2410.07362 v2 pith:3SHWEKAA submitted 2024-10-09 cs.HC

classification cs.HC
keywords qualitativellmsresearchresearchersparticipantstensionsacrossethical
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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 LLMs in qualitative research and design principles for LLM-assisted qualitative research tools.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Mitigating Trauma in Qualitative Research Infrastructure: Roles for Machine Assistance and Trauma-Informed Design

    cs.HC 2024-12 accept novelty 6.0 of 10

    A formative design study shows how trauma-informed computing could reshape qualitative coding tools, and argues for safety-as-enablement.

  2. Applications and Implications of Large Language Models in Qualitative Analysis: A New Frontier for Empirical Software Engineering

    cs.SE 2024-12 conditional novelty 4.0 of 10

    A systematic mapping study of 20 papers shows LLMs are mainly used for coding and thematic analysis, with efficiency benefits but reliability, nuance, and privacy limitations.

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