Optimizing Generative AI's Accuracy and Transparency in Inductive Thematic Analysis: A Human-AI Comparison
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 0 of its 16 references that are in the map. First posted on arXiv 8 Mar 2025.
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Citing papers per year: 2025: 1; 2026: 2.
3 papers.
- 2025AI tutoring can safely and effectively support students: An exploratory RCT in UK classroomscited by 3
- 2026Bridging Human Interpretation and Machine Representation: A Landscape of Qualitative Data Analysis in the LLM Eracited by 0
- 2026How AI Coders Discuss, Disagree, and Reach Consensus: Challenges and Opportunities for LLM-Based Qualitative Codingcited by 0
- 2026How AI Coders Discuss, Disagree, and Reach Consensus: Challenges and Opportunities for LLM-Based Qualitative Coding10 Sep 2026
- 2026Bridging Human Interpretation and Machine Representation: A Landscape of Qualitative Data Analysis in the LLM Era16 Jan 2026
- 2025AI tutoring can safely and effectively support students: An exploratory RCT in UK classrooms29 Dec 2025