LLMs in Qualitative Research: Opportunities, Limitations, and Practical Considerations
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866916017885675520 |
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| author | Salgado, Henry Kendall, Meagan R. Ceberio, Martine Strong, Alexandra Coso |
| author_facet | Salgado, Henry Kendall, Meagan R. Ceberio, Martine Strong, Alexandra Coso |
| contents | This paper examines the opportunities, limitations, and practical considerations associated with the use of large language models (LLMs) in qualitative research. Drawing on a multidisciplinary perspective that combines expertise in qualitative methods and explainable AI, the paper argues that responsible integration of LLMs into qualitative workflows requires researchers to engage critically with a curated set of technical parameters, that is, context window constraints, temperature and top-p sampling settings, user and system prompt design, and model documentation in the form of system cards. The paper situates these considerations within the epistemological commitments of qualitative research, including reflexivity, positionality, and interpretive judgment, and discusses how the opacity of contemporary LLMs differs from earlier natural language processing tools such as topic models and lexicon-based sentiment analyzers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_16538 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | LLMs in Qualitative Research: Opportunities, Limitations, and Practical Considerations Salgado, Henry Kendall, Meagan R. Ceberio, Martine Strong, Alexandra Coso Human-Computer Interaction Computation and Language This paper examines the opportunities, limitations, and practical considerations associated with the use of large language models (LLMs) in qualitative research. Drawing on a multidisciplinary perspective that combines expertise in qualitative methods and explainable AI, the paper argues that responsible integration of LLMs into qualitative workflows requires researchers to engage critically with a curated set of technical parameters, that is, context window constraints, temperature and top-p sampling settings, user and system prompt design, and model documentation in the form of system cards. The paper situates these considerations within the epistemological commitments of qualitative research, including reflexivity, positionality, and interpretive judgment, and discusses how the opacity of contemporary LLMs differs from earlier natural language processing tools such as topic models and lexicon-based sentiment analyzers. |
| title | LLMs in Qualitative Research: Opportunities, Limitations, and Practical Considerations |
| topic | Human-Computer Interaction Computation and Language |
| url | https://arxiv.org/abs/2605.16538 |