# Large Language Model for Qualitative Research -- A Systematic Mapping Study

arXiv: https://arxiv.org/abs/2411.14473 · HTML: https://mlsanity.com/paper/2411.14473/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 18 Nov 2024.

## Builds on (most cited first, 7 of 7)

- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [Extracting Accurate Materials Data from Research Papers with Conversational Language Models and Prompt Engineering](https://mlsanity.com/paper/2303.05352/) (2023)
- [CollabCoder: A Lower-barrier, Rigorous Workflow for Inductive Collaborative Qualitative Analysis with Large Language Models](https://mlsanity.com/paper/2304.07366/) (2023)
- [Scalable Qualitative Coding with LLMs: Chain-of-Thought Reasoning Matches Human Performance in Some Hermeneutic Tasks](https://mlsanity.com/paper/2401.15170/) (2024)
- [Successful Combination of Database Search and Snowballing for Identification of Primary Studies in Systematic Literature Studies](https://mlsanity.com/paper/2307.02612/) (2023)
- [Exploring Qualitative Research Using LLMs](https://mlsanity.com/paper/2306.13298/) (2023)

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

- [LOGOS: LLM-driven End-to-End Grounded Theory Development and Schema Induction for Qualitative Research](https://mlsanity.com/paper/2509.24294/) (2025)
- [Large Language Models' Varying Accuracy in Recognizing Risk-Promoting and Health-Supporting Sentiments in Public Health Discourse: The Cases of HPV Vaccination and Heated Tobacco Products](https://mlsanity.com/paper/2507.04364/) (2025)
- [Multi-LLM Thematic Analysis with Dual Reliability Metrics: Combining Cohen's Kappa and Semantic Similarity for Qualitative Research Validation](https://mlsanity.com/paper/2512.20352/) (2025)
- [Antipatterns in AI-assisted Qualitative Data Analysis: A Catalog of Temptations and Pitfalls for Software Engineering Researchers](https://mlsanity.com/paper/2608.27927/) (2026)

## Abstract (arXiv)

The exponential growth of text-based data in domains such as healthcare, education, and social sciences has outpaced the capacity of traditional qualitative analysis methods, which are time-intensive and prone to subjectivity. Large Language Models (LLMs), powered by advanced generative AI, have emerged as transformative tools capable of automating and enhancing qualitative analysis. This study systematically maps the literature on the use of LLMs for qualitative research, exploring their application contexts, configurations, methodologies, and evaluation metrics. Findings reveal that LLMs are utilized across diverse fields, demonstrating the potential to automate processes traditionally requiring extensive human input. However, challenges such as reliance on prompt engineering, occasional inaccuracies, and contextual limitations remain significant barriers. This research highlights opportunities for integrating LLMs with human expertise, improving model robustness, and refining evaluation methodologies. By synthesizing trends and identifying research gaps, this study aims to guide future innovations in the application of LLMs for qualitative analysis.