LLMs in Qualitative Research: Opportunities, Limitations, and Practical Considerations

2026-05-15

AI Summary

This study addresses the responsible integration of large language models (LLMs) into qualitative research workflows while upholding core epistemological principles such as reflexivity, situatedness, and interpretive judgment. By bridging qualitative methodology with explainable AI through an interdisciplinary lens, it systematically aligns key LLM technical parameters—including context window, temperature, top-p sampling, prompt design, and system cards—with the epistemological foundations of qualitative inquiry. The work demonstrates how LLMs differ fundamentally from traditional NLP tools in terms of transparency and interpretability. It further proposes a practical framework for qualitative researchers that explicitly links technical configurations to research ethics and methodological rigor, thereby advancing a critical and responsible synthesis of AI technologies with humanities and social science methodologies.

Abstract

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.

Problem

• large language models
• qualitative research
• responsible integration
• epistemological commitments
• model opacity

Innovation

• large language models
• qualitative research
• explainable AI
• system cards
• reflexivity