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

arXiv: https://arxiv.org/abs/2410.07362 · HTML: https://mlsanity.com/paper/2410.07362/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2024.

## Built on by (most cited first, 21 of 21)

- [LOGOS: LLM-driven End-to-End Grounded Theory Development and Schema Induction for Qualitative Research](https://mlsanity.com/paper/2509.24294/) (2025)
- [Show or Tell? Modeling the evolution of request-making in Human-LLM conversations](https://mlsanity.com/paper/2508.01213/) (2025)
- [AI Annotation Orchestration: Evaluating LLM verifiers to Improve the Quality of LLM Annotations in Learning Analytics](https://mlsanity.com/paper/2511.09785/) (2025)
- [Reflexis: Supporting Reflexivity and Rigor in Collaborative Qualitative Analysis through Design for Deliberation](https://mlsanity.com/paper/2601.15445/) (2026)
- [Arti-"fickle" Intelligence: Using LLMs as a Tool for Inference in the Political and Social Sciences](https://mlsanity.com/paper/2504.03822/) (2025)
- [Prosocial Behavior Detection in Player Game Chat: From Aligning Human-AI Definitions to Efficient Annotation at Scale](https://mlsanity.com/paper/2508.05938/) (2025)
- [Agent-as-Peer-Debriefer: A Multi-Agent Framework with Perspective-Based Refinement for Qualitative Analysis](https://mlsanity.com/paper/2605.24600/) (2026)
- [Collaborative Participatory Research with LLM Agents in South Asia: An Empirically-Grounded Methodological Initiative and Agenda from Field Evidence in Sri Lanka](https://mlsanity.com/paper/2411.08294/) (2024)
- [Empowering Computing Education Researchers Through LLM-Assisted Content Analysis](https://mlsanity.com/paper/2508.18872/) (2025)
- [Can machines perform a qualitative data analysis? Reading the debate with Alan Turing](https://mlsanity.com/paper/2512.04121/) (2025)
- [Bridging Human Interpretation and Machine Representation: A Landscape of Qualitative Data Analysis in the LLM Era](https://mlsanity.com/paper/2601.11739/) (2026)
- [A conceptual framework for ideology in online discourse beyond the left and right](https://mlsanity.com/paper/2603.18945/) (2026)
- [LLMs for Qualitative Data Analysis Fail on Security-specificComments in Human Experiments](https://mlsanity.com/paper/2604.10834/) (2026)
- [How Researchers Navigate Accountability, Transparency, and Trust When Using AI Tools in Early-Stage Research: A Think-Aloud Study](https://mlsanity.com/paper/2604.23136/) (2026)
- [Framing Migration News with LLMs: Structured CoT as a Support for Human Interpretation](https://mlsanity.com/paper/2606.03761/) (2026)
- [Will Scaling Improve Social Simulation with LLMs?](https://mlsanity.com/paper/2607.02464/) (2026)
- [Validating LLMs in social science: Epistemic threats and emerging norms](https://mlsanity.com/paper/2607.07915/) (2026)
- [Value-Sensitive Delegation in Everyday AI Agent Use: Evidence from OpenClaw](https://mlsanity.com/paper/2609.22067/) (2026)
- [Can Vision-Language Models Analyze Human-Centered Video? Mapping Model Capabilities and Human-AI Collaborative Workflows](https://mlsanity.com/paper/2609.27327/) (2026)
- [Looking Complete Is Not Being Grounded: Evaluating LLM Sitreps Against Human-Authored Situation Reports](https://mlsanity.com/paper/2610.18762/) (2026)
- [From Qualitative Text to Integrated Evidence: The Parallel-Then-Integrated (PTI) Procedure](https://mlsanity.com/paper/2610.19164/) (2026)

## Abstract (arXiv)

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.