The Feasibility and Comparability of Using Artificial Intelligence for Qualitative Data Analysis in Equity-Focused Research

Abstract

In this essay, we explored the feasibility of utilizing artificial intelligence (AI) for qualitative data analysis in equity-focused research. Specifically, we compare thematic analyses of interview transcripts conducted by human coders with those performed by GPT-3 using a zero-shot chain-of-thought prompting strategy. Our results suggest that the AI model, when provided with suitable prompts, can proficiently perform thematic analysis, demonstrating considerable comparability with human coders. Despite potential biases inherent in its training data, the model was able to analyze and interpret the data through social justice perspectives. We discuss the applications of integrating AI into qualitative research, provide code snippets illustrating the use of GPT models, and highlight unresolved questions to encourage further dialogue in the field.