# QualiGPT: GPT as an easy-to-use tool for qualitative coding

arXiv: https://arxiv.org/abs/2310.07061 · HTML: https://mlsanity.com/paper/2310.07061/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2023.

## Built on by (most cited first, 8 of 8)

- [GPT for Games: A Scoping Review (2020-2023)](https://mlsanity.com/paper/2404.17794/) (2024)
- [From Prompts to Propositions: A Logic-Based Lens on Student-LLM Interactions](https://mlsanity.com/paper/2504.18691/) (2025)
- [Leveraging Large Language Models (LLMs) to Support Collaborative Human-AI Online Risk Data Annotation](https://mlsanity.com/paper/2404.07926/) (2024)
- [Augmenting Image Annotation: A Human-LMM Collaborative Framework for Efficient Object Selection and Label Generation](https://mlsanity.com/paper/2503.11096/) (2025)
- [TALLMesh: a simple application for performing Thematic Analysis with Large Language Models](https://mlsanity.com/paper/2504.13892/) (2025)
- [Human-AI Narrative Synthesis to Foster Shared Understanding in Civic Decision-Making](https://mlsanity.com/paper/2509.19643/) (2025)
- [Beyond Tokens in Language Models: Interpreting Activations through Text Genre Chunks](https://mlsanity.com/paper/2511.16540/) (2025)
- [How AI Coders Discuss, Disagree, and Reach Consensus: Challenges and Opportunities for LLM-Based Qualitative Coding](https://mlsanity.com/paper/2609.11109/) (2026)

## Abstract (arXiv)

Qualitative research delves deeply into individual complex perspectives on technology and various phenomena. However, a meticulous analysis of qualitative data often requires a significant amount of time, especially during the crucial coding stage. Although there is software specifically designed for qualitative evaluation, many of these platforms fall short in terms of automatic coding, intuitive usability, and cost-effectiveness. With the rise of Large Language Models (LLMs) such as GPT-3 and its successors, we are at the forefront of a transformative era for enhancing qualitative analysis. In this paper, we introduce QualiGPT, a specialized tool designed after considering challenges associated with ChatGPT and qualitative analysis. It harnesses the capabilities of the Generative Pretrained Transformer (GPT) and its API for thematic analysis of qualitative data. By comparing traditional manual coding with QualiGPT's analysis on both simulated and actual datasets, we verify that QualiGPT not only refines the qualitative analysis process but also elevates its transparency, credibility, and accessibility. Notably, compared to existing analytical platforms, QualiGPT stands out with its intuitive design, significantly reducing the learning curve and operational barriers for users.
