Despite an emerging body of scholarship on applying generative AI (GenAI) to qualitative data analysis, this area remains underdeveloped. This article evaluates how GenAI can support thematic analysis using a publicly available interview dataset from Lumivero. It introduces Guided AI Thematic Analysis (GAITA), an adaptation of King et al.’s (2018) Template Analysis. This framework positions researchers as a reflexive instrument and intellectual leader while thoroughly guiding GPT-4 in four stages: data familiarization; preliminary coding; template formation and finalization; and theme development. Additionally, the article proposes the ACTOR framework, a simple approach to combining different effective prompting techniques when working with GenAI for qualitative research purposes. Findings reveal GenAI’s capacity for analyzing the data, generating codes, subcodes, clusters, and themes, along with its adaptive learning and interactive assistance in organizing unstructured data and developing trustworthiness. However, this model has some key limitations in terms of its restricted context window for processing large datasets, its inconsistent outputs requiring multiple prompt attempts, the need to move across workspaces, and the lack of relevant training data for qualitative research purposes.

This collaborative autoethnography (CAE) by five emerging disaster researchers explores the transformative role of Bourdieu's theories in the realm of…