“Conversing” With Qualitative Data: Enhancing Qualitative Research Through Large Language Models (LLMs)

Abstract

Large Language Models (LLMs) are revolutionizing how qualitative researchers can work with textual data. Rather than relying only on codebooks or manual line-by-line analysis, scholars can “converse” with their materials by asking targeted questions, probing for contextual insights, and refining theoretical connections. This dialogue-like process speeds up traditional tasks—transcription, coding, theme identification—while sparking broader possibilities for exploration. Researchers prompt the LLM to surface recurring patterns, detect subtle shifts in tone, or suggest new interpretive angles. These capabilities neither diminish the centrality of human expertise nor replace the analytical depth that defines qualitative research. Instead, they expand the researcher’s toolkit, allowing more time for theoretical reflection and rich meaning-making. The LLM becomes an active partner: it quickly identifies connections that might take days or weeks of manual work, yet the scholar remains responsible for selecting which prompts to use, verifying the outputs, and situating them within appropriate conceptual frameworks. Ethical considerations—such as data security and bias—demand careful oversight and transparent reporting, emphasizing the importance of maintaining scholarly integrity. Taken together, LLM-facilitated analysis offer a promising avenue to enhance qualitative research, leveraging both computational efficiency and the nuanced, reflexive insights at the heart of interpretive inquiry.

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