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arXiv · 2503.16485

Optimizing Generative AI's Accuracy and Transparency in Inductive Thematic Analysis: A Human-AI Comparison

Matthew Nyaaba·Min SungEun·Mary Abiswin Apam·Kwame Owoahene Acheampong·Emmanuel Dwamena
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Abstract

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.

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Matthew Nyaaba, Min SungEun, Mary Abiswin Apam, Kwame Owoahene Acheampong, Emmanuel Dwamena. 2025-03-08. Optimizing Generative AI's Accuracy and Transparency in Inductive Thematic Analysis: A Human-AI Comparison. https://arxiv.org/abs/2503.16485

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