The Role of Generative Artificial Intelligence in the Analysis of Qualitative Data Compared With Human-Led Analysis

Introduction The use of generative artificial intelligence (GenAI) has been widely adopted across multiple fields and is beginning to be integrated into research, specifically in qualitative and mixed-methods designs. Currently, GenAI can be used for data familiarization and analysis. However, approaches that integrate GenAI with human analysis are still relatively new, and no studies in medical education have explored this approach. The overarching purpose of this study is to compare GenAI-led and human-led thematic analyses of qualitative data and to explore strategies that can enhance GenAI-led thematic analysis, thereby providing insights into how GenAI and human-led analyses can complement each other. Methods A GenAI platform (Microsoft 365 Copilot; Microsoft Corporation, Redmond, Washington, USA) was used to conduct reflexive thematic analysis and generate themes through a qualitative research dataset that includes 23 interview transcripts, whereby data were collected in 2024. The GenAI analysis was conducted through an iterative process of exploring the functions of Copilot, optimizing data input, and investigating prompting strategies. The quality of the GenAI analysis was explored by comparing its output to the human-led analysis. Results Overall, we found that, through effective prompting strategies, Copilot was able to create a thematic table, providing a comprehensive view and summary of the data. However, at times, Copilot could not use the entirety of a large prompt. Additionally, through examining the Copilot-generated and human-generated codebooks, it was found that Copilot took a more interpretive analytical approach compared to the human-led analysis, which utilized a qualitative descriptive approach. Conclusion In conclusion, since the use of GenAI to support qualitative analysis is new, we caution readers to explore the functions of the GenAI platform they use and understand the prompting strategies that yield the optimal analytical approach and output for their objectives. Specifically, it is important to reflect on the types of qualitative analysis that GenAI can support and to consider reflexivity and potential biases throughout the research process.