Human–AI Collaboration in Reflexive Thematic Analysis: A Case Study

• In Event: Virtual Poster Session
  In Virtual iPresentation Only: SIG-Qualitative Research Virtual Poster Session

Abstract

This study explores the role of ChatGPT in supporting reflexive thematic analysis within institutional research by comparing AI-assisted and manual approaches. Using focus group interviews with forty-eight instructors of first-year undergraduate students as a case study, we applied Braun and Clarke’s six-phase framework to guide both the analysis and interpretation. Drawing on Naeem et al.’s (2025) systematic AI-driven model, we developed tailored prompt strategies to maintain researcher reflexivity and contextual nuance. ChatGPT served as a tool to augment, rather than replace, human analytic judgment in theme development. Findings reveal convergences and divergences between AI-assisted and manual outcomes, highlighting implications for methodological rigor, transparency, and capacity-building in institutional qualitative research.

Authors

• Yao Yang, Purdue University

• Daniel Carpenter, Purdue University

• Katie H Dufault, Purdue University

• Craig Johnson, Purdue University

• Anne Weiss, Purdue University