Large Language Models to Support Qualitative Analyses: An Extended Systematic Mapping Study

Authors:
Preprint
Publisher website
Google Scholar
Abstract
The exponential growth of heterogeneous textual data within modern sociotechnical ecosystems—such as healthcare, education, technology, and the social sciences—has surpassed the capacity of traditional qualitative analysis methods to remain agile and precise when synthesizing evidence, analyzing software requirements, or mining issue repositories. Large Language Models (LLMs), driven by recent advances in Artificial Intelligence (AI), have emerged as transformative tools capable of automating and enhancing qualitative analysis. This study extends a previously published systematic mapping of the literature that maps and evaluates the use of LLMs in qualitative research. We extend the prior study by increasing the number of included studies by means of a snowballing process, exploring their application contexts, configurations, methodologies, and evaluation metrics. The original study analyzed eight papers, from which six were used as seeds in the snowballing process, leading to the identification and analysis of 38 additional studies, resulting in a total of 44 included papers. The results reveal that, beyond being applied across diverse fields, LLMs demonstrate the ability to reduce the effort traditionally required in tasks such as coding, categorization, and theme extraction, with 56% of studies reporting performance as either strictly equal (45%) or superior (11%) to manual analysis. However, challenges such as reliance on prompt engineering, occasional inaccuracies, and contextual limitations remain as barriers to their broader use. This research highlights opportunities to integrate LLMs with human expertise to improve model robustness and refine evaluation methodologies.