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Abstract Qualitative coding, or content analysis, is more than just labeling text: it is a reflexive interpretive practice that shapes research questions, refines theoretical insights, and illuminates subtle social dynamics. As large language models (LLMs) become increasingly adept at nuanced language tasks, questions arise about whether—and how—they can assist in large-scale coding without eroding the interpretive depth that distinguishes qualitative analysis from traditional machine learning and other quantitative approaches to natural language processing. In this paper, we present a hybrid approach that preserves hermeneutic value while incorporating LLMs to scale the application of codes to large data sets that are impractical for manual coding. Our workflow retains the traditional cycle of codebook development and refinement, adding an iterative step to adapt definitions for machine comprehension, before ultimately replacing manual with automated text categorization. We demonstrate how to rewrite code descriptions for LLM-interpretation, as well as how structured prompts and prompting the model to explain its coding decisions (chain-of-thought) can substantially improve fidelity. Empirically, our case study of socio-historical codes highlights the promise of frontier AI language models to reliably interpret paragraph-long passages representative of a humanistic study. Throughout, we emphasize ethical and practical considerations, preserving space for critical reflection, and the ongoing need for human researchers’ interpretive leadership. These strategies can guide both traditional and computational scholars aiming to harness automation effectively and responsibly—maintaining the creative, reflexive rigor of qualitative coding while capitalizing on the efficiency afforded by LLMs.
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Artificial intelligence in qualitative analysis: a practical guide and reflections based on results from using GPT to analyze interview data in a substance use program
Language-based text provide valuable insights into people’s lived experiences. While traditional qualitative analysis is used to capture these nuances, new paradigms are needed to scale qualitative research effectively. Artificial intelligence presents an unprecedented opportunity to expand the sale of analysis for obtaining such nuances. The study tests the application of GPT-4—a large language modeling—in qualitative data analysis using an existing set of text data derived from 60 qualitative interviews. Specifically, the study provides a practical guide for social and behavioral researchers, illustrating core elements and key processes, demonstrating its reliability by comparing GPT-generated codes with researchers’ codes, and evaluating its capacity for theory-driven qualitative analysis. The study followed a three-step approach: (1) prompt engineering, (2) reliability assessment by comparison of GPT-generated codes with researchers’ codes, and (3) evaluation of theory-driven thematic analysis on psychological constructs. The study underscores the utility of GPT’s capabilities in coding and analyzing text data with established qualitative methods while highlighting the need for qualitative expertise to guide GPT applications. Recommendations for further exploration are also discussed.
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- Award ID(s):
- 2449011
- PAR ID:
- 10590730
- Publisher / Repository:
- Springer Nature
- Date Published:
- Journal Name:
- Quality & Quantity
- ISSN:
- 0033-5177
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1007/s11135-025-02066-1
