Comparative Reflections on Human-Driven and Generative Artificial Intelligence-Assisted Thematic Analysis: A Collaborative Autoethnography

Abstract

Thematic analysis is a well known qualitative analytic method, usually driven by a human researcher to analyze qualitative data. However, in the current age of Generative Artificial Intelligence (GAI) technologies revolution, analyzing qualitative data is evolving. Many research studies have explored the potential of GAI to conduct qualitative data analysis. However, limited studies have explored the collaborative autoethnography qualitative approach in understanding the expectations, challenges and future insights based on two researchers’ personal reflections of using manual approach as well as obtaining support from GAI in analysing data using thematic approach. These reflections are not mutually exclusive but interplay to assist both researchers to understand the dynamics of analysing qualitative data. The study revealed that manual thematic analysis provided in-depth, context-rich insights, capturing cultural and contextual nuances, whereas the GAI-assisted approach offered efficiency and scalability but lacked interpretative depth. Additionally, challenges such as time constraints in manual analysis and prompt variability in GAI-assisted methods were identified, highlighting the need for hybrid approaches to enhance research efficacy. These findings contribute to the research methodologies literature in filling an empirical gap to elevate research efficacy and outcomes as well as present practical implications.

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