Large Language Model for Qualitative Research: A Systematic Mapping Study

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 eme...

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Bibliographic Details
Published in:2025 IEEE/ACM International Workshop on Methodological Issues with Empirical Studies in Software Engineering (WSESE) pp. 48 - 55
Main Authors: Barros, Caua Ferreira, Azevedo, Bruna Borges, Graciano Neto, Valdemar Vicente, Kassab, Mohamad, Kalinowski, Marcos, Do Nascimento, Hugo Alexandre D., Bandeira, Michelle C.G.S.P.
Format: Conference Proceeding
Language:English
Published: IEEE 03.05.2025
Subjects:
Education
Large Language Model
Large language models
LLM
Medical services
Prompt engineering
Qualitative Analysis
Qualitative Research
Robustness
Social sciences
Soft sensors
Software engineering
Systematics
Technological innovation
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Summary: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.
DOI:10.1109/WSESE66602.2025.00015