Frankenstein, thematic analysis and generative artificial intelligence: Quality appraisal methods and considerations for qualitative research - Tanisha Jowsey; Peta Stapleton; Shawna Campbell; Alexandra Davidson; Cher McGillivray; Isabella Maugeri; Megan Lee; Justin Keogh (PLoS ONE, 2025)
Objective To determine accuracy and efficiency of using generative artificial intelligence (GenAI) to undertake thematic analysis. Introduction With the increasing use of GenAI in data analysis, testing the reliability and suitability of using GenAI to conduct qualitative data analysis is needed. We propose a method for researchers to assess reliability of GenAI outputs using deidentified qualitative datasets. Methods We searched three databases (United Kingdom Data Service, Figshare, and Google Scholar) and five journals (PlosOne, Social Science and Medicine, Qualitative Inquiry, Qualitative Research, Sociology Health Review) to identify studies on health-related topics, published prior to whereby: humans undertook thematic analysis and published both their analysis in a peer-reviewed journal and the associated dataset. We prompted a closed system GenAI (Microsoft Copilot) to undertake thematic analysis of these datasets and analysed the GenAI outputs in comparison with human outputs. Measures include time (GenAI only), accuracy, overlap with human analysis, and reliability of selected data and quotes. Results Five studies were identified that met our inclusion criteria. The themes identified by human researchers and Copilot showed minimal overlap, with human researchers often using discursive thematic analyses (40%) and Copilot focusing on thematic analysis (100%). Copilot’s outputs often included fabricated quotes (58% SD = 45%) and none of the Copilot outputs provided participant spread by theme. Additionally, Copilot’s outputs primarily drew themes and quotes from the first 2-3 pages of textual data, rather than from the entire dataset. Human researchers provided broader representation and accurate quotes (79% quotes were correct, SD = 27%). Conclusions Based on these results, we cannot recommend the current version of Copilot for undertaking thematic analyses. This study raises concerns about the validity of both human-generated and GenAI-generated qualitative data analysis and reporting.