Thematic Analysis in an Artificial Intelligence-Driven Context: A Stage-by-Stage Process

Although a substantial body of research has explored the application of thematic analysis within qualitative enquiry, considerable variation remains regarding how scholars might operationalise the method. More importantly, the advent of generative artificial intelligence (AI) has introduced additional complexity to the expanding corpus of secondary data. The volume and evolving characteristics of AI-generated content necessitate a critical re-evaluation of existing analytical frameworks. In response to these emerging challenges, this study develops a carefully constructed and theoretically grounded thematic analysis framework to support researchers in conducting analysis within an AI-driven context. Drawing on a previous framework, it highlights the flexibility of thematic analysis and its capacity to generate rich, contextually grounded insights, particularly in the synthesis of existing knowledge through secondary data analysis. This paper proposes the RIPES (Reflexivity, Interpretation, Procedural consistency, Evaluation, and Situatedness) model, outlines the key stages of conducting thematic analysis, examines its application to secondary data, and evaluates both the benefits and the challenges associated with this approach in qualitative enquiry. It aims to assist researchers and practitioners in critically engaging with both primary and secondary data while maintaining methodological integrity within an increasingly technologically mediated research environment.