Thematic analysis with open-source generative AI and machine learning: A new method for inductive qualitative codebook development
Summary
This paper introduces a novel method for developing qualitative codebooks using open-source generative AI and machine learning techniques, enabling more systematic and reproducible inductive thematic analysis.
Findings from this paper
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An open-model workflow for thematic analysis found nearly all the themes planted in synthetic test data
When the lab planted known themes in three synthetic datasets, the GATOS workflow produced a good match for 166 of 187 planted sub-themes, a partial match for 18 more, and missed 3.
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Cite this paper
Katz, A., Fleming, G. C., & Main, J. B. (2026). Thematic analysis with open-source generative AI and machine learning: A new method for inductive qualitative codebook development. Humanities and Social Sciences Communications. https://doi.org/10.1057/s41599-026-06508-5
BibTeX
@article{katz2026thematic,
title = {Thematic analysis with open-source generative AI and machine learning: A new method for inductive qualitative codebook development},
author = {Katz, Andrew and Fleming, Gabriella Coloyan and Main, Joyce B.},
journal = {Humanities and Social Sciences Communications},
year = {2026},
doi = {10.1057/s41599-026-06508-5}
}