# When Qualitative Research Meets Large Language Model: Exploring the Potential of QualiGPT as a Tool for Qualitative Coding

arXiv: https://arxiv.org/abs/2407.14925 · HTML: https://mlsanity.com/paper/2407.14925/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2024.

## Built on by (most cited first, 4 of 4)

- ['Simulacrum of Stories': Examining Large Language Models as Qualitative Research Participants](https://mlsanity.com/paper/2409.19430/) (2024)
- [Zero-shot Emotion Annotation in Facial Images Using Large Multimodal Models: Benchmarking and Prospects for Multi-Class, Multi-Frame Approaches](https://mlsanity.com/paper/2502.12454/) (2025)
- [LLM-Assisted Automated Deductive Coding of Dialogue Data: Leveraging Dialogue-Specific Characteristics to Enhance Contextual Understanding](https://mlsanity.com/paper/2504.19734/) (2025)
- [Automated Thematic Analysis for Clinical Qualitative Data: Iterative Codebook Refinement with Full Provenance](https://mlsanity.com/paper/2603.08989/) (2026)

## Abstract (arXiv)

Qualitative research, renowned for its in-depth exploration of complex phenomena, often involves time-intensive analysis, particularly during the coding stage. Existing software for qualitative evaluation frequently lacks automatic coding capabilities, user-friendliness, and cost-effectiveness. The advent of Large Language Models (LLMs) like GPT-3 and its successors marks a transformative era for enhancing qualitative analysis. This paper introduces QualiGPT, a tool developed to address the challenges associated with using ChatGPT for qualitative analysis. Through a comparative analysis of traditional manual coding and QualiGPT's performance on both simulated and real datasets, incorporating both inductive and deductive coding approaches, we demonstrate that QualiGPT significantly improves the qualitative analysis process. Our findings show that QualiGPT enhances efficiency, transparency, and accessibility in qualitative coding. The tool's performance was evaluated using inter-rater reliability (IRR) measures, with results indicating substantial agreement between human coders and QualiGPT in various coding scenarios. In addition, we also discuss the implications of integrating AI into qualitative research workflows and outline future directions for enhancing human-AI collaboration in this field.