Doing Thematic Analysis in the Age of Generative AI: Practices, Ethics and Reflexivity

While the extant research has provided a recipe for researchers to undertake thematic analysis (TA) in a theoretically and methodologically sound way, there has not yet been sufficient research to map out TA in the age of generative artificial intelligence (Gen AI). Building on and refining my 2020 article Applying thematic analysis to education: A hybrid approach to interpreting data in practitioner research published in International Journal of Qualitative Methods , which provides an example of thorough, end-to-end manual TA in a practitioner inquiry, this paper presents a follow-up example of how human-ChatGPT can work side by side in undertaking a non-positivist, Big Q reflexive TA. Particular attention is given to how ChatGPT can expedite data transcription, code generation, theme development, interpretation and proofreading throughout the research process, while enabling human researchers to articulate their reflexivity, mitigate algorithmic bias, produce nuanced interpretations and uphold research ethics. Following the six phases of reflexive TA outlined by Braun and Clarke, this paper opens up possibilities for AI-assisted TA research and invites reflection on what constitutes ‘good TA’ practices in working together with nonhuman entities in qualitative inquiry.