Optimizing Generative AI's Accuracy and Transparency in Inductive Thematic Analysis: A Human-AI Comparison
This work addresses the need for transparent and accurate AI tools in qualitative research, though it is incremental as it applies existing methods to a new domain.
This study tackled the problem of evaluating generative AI's performance in inductive thematic analysis by comparing GPT-4 Turbo to human coders, finding that it closely resembles human coding accuracy but offers more generalized interpretations.
This study highlights the transparency and accuracy of GenAI's inductive thematic analysis, particularly using GPT-4 Turbo API integrated within a stepwise prompt-based Python script. This approach ensured a traceable and systematic coding process, generating codes with supporting statements and page references, which enhanced validation and reproducibility. The results indicate that GenAI performs inductive coding in a manner closely resembling human coders, effectively categorizing themes at a level like the average human coder. However, in interpretation, GenAI extends beyond human coders by situating themes within a broader conceptual context, providing a more generalized and abstract perspective.