Author

June 10 2025

UKMC and Canterbury Christ Church University Collaborate on Ground-breaking Research Exploring Artificial Intelligence in Thematic Analysis

We are proud to share that Dr Muhammad Naeem (Dean-UKMC) has co-authored a pioneering paper alongside colleagues from our esteemed partner institution, Canterbury Christ Church University, Tracy Smith and Dr Lorna Thomas. The study, published in the International Journal of Qualitative Methods (SAGE), tackles one of the most exciting and rapidly evolving topics in research today: the use of Artificial Intelligence in qualitative research, with a specific focus on thematic analysis using GenAI.

Titled "Thematic Analysis and Artificial Intelligence: A Step-by-Step Process for Using ChatGPT in Thematic Analysis", this open-access paper provides a structured and practical toolkit for integrating generative AI into the heart of qualitative research. As academic and industry conversations increasingly turn toward AI's potential to transform research methods, this work offers both theoretical depth and hands-on utility for scholars and students navigating the evolving landscape of AI-enhanced methodologies.

The publication introduces an innovative six-step process for conducting systematic thematic analysis using ChatGPT, offering a rigorous and replicable approach rooted in qualitative research theory. It highlights how AI can enhance transparency, reduce bias, and streamline analysis of large datasets. Importantly, it showcases a successful collaborative research output between UK Management College and Canterbury Christ Church University, strengthening their academic partnership.

As part of UKMC’s ongoing mission to lead in research innovation, this collaboration not only advances methodological knowledge but also serves as a benchmark for responsible, critical use of AI in qualitative scholarship.

This is an exciting moment for UKMC and CCCU, and we extend our warmest congratulations to the authors on this influential contribution to the field.

We invite colleagues, students, and academic partners to access the full paper via the following link: https://doi.org/10.1177/16094069251333886