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.

本研究解决了传统定性分析方法在面对快速增长的文本数据时效率低下和主观性强的问题。通过系统性文献映射，揭示了大型语言模型（LLMs）在定性研究中的应用场景、方法和评估指标，发现LLMs有潜力自动化传统人工密集的定性过程，但仍面临提示工程依赖、偶尔不准确和上下文限制等挑战。该研究强调了结合人类专业知识和改进评估方法的机会，以推动LLMs在定性分析中的进一步应用。

大型语言模型在定性研究中的应用——系统性映射研究