# Redefining Qualitative Analysis in the AI Era: Utilizing ChatGPT for Efficient Thematic Analysis

arXiv: https://arxiv.org/abs/2309.10771 · HTML: https://mlsanity.com/paper/2309.10771/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2023.

## Built on by (most cited first, 16 of 16)

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- [How AI Coders Discuss, Disagree, and Reach Consensus: Challenges and Opportunities for LLM-Based Qualitative Coding](https://mlsanity.com/paper/2609.11109/) (2026)
- [From Qualitative Text to Integrated Evidence: The Parallel-Then-Integrated (PTI) Procedure](https://mlsanity.com/paper/2610.19164/) (2026)

## Abstract (arXiv)

AI tools, particularly large-scale language model (LLM) based applications such as ChatGPT, have the potential to simplify qualitative research. Through semi-structured interviews with seventeen participants, we identified challenges and concerns in integrating ChatGPT into the qualitative analysis process. Collaborating with thirteen qualitative researchers, we developed a framework for designing prompts to enhance the effectiveness of ChatGPT in thematic analysis. Our findings indicate that improving transparency, providing guidance on prompts, and strengthening users' understanding of LLMs' capabilities significantly enhance the users' ability to interact with ChatGPT. We also discovered and revealed the reasons behind researchers' shift in attitude towards ChatGPT from negative to positive. This research not only highlights the importance of well-designed prompts in LLM applications but also offers reflections for qualitative researchers on the perception of AI's role. Finally, we emphasize the potential ethical risks and the impact of constructing AI ethical expectations by researchers, particularly those who are novices, on future research and AI development.