Discipline-Driven AI: Training a GPT to Qualitatively Code for Technical and Professional Communication Research
Amber Hedquist, Heidi Willers, Mark A. Hannah · 2024
This project reports on the process used to train an AI model for qualitative coding and lessons learned for AI utilization in discipline-specific qualitative research. Interdisciplinary scholars are assessing the role of AI in qualitative coding processes, analyzing tools such as Cody [1], PaTAT [2] and ChatGPT3 [3]. Early findings suggest that emerging AI technology may contribute to qualitative coding by providing new insights [2] and increasing intercoder reliability [1], among other benefits. Though important, the sustained attention to the outcomes of AI-assisted coding provides little insight into the processes, successes, and failures during the training process. By focusing on process, researchers can better formulate strategies for AI-assisted coding. This project highlights researchers’ experiences training an AI with a disciplinary-specific knowledge base to support qualitative coding.