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大语言模型在质性研究中的应用探索

Exploring the Application of Large Language Models in Qualitative Research

中文摘要英文摘要

大语言模型(LLMs)在质性研究中有巨大的应用潜力,尤其是在数据编码和解释建构两个关键环节中的应用。在数据编码过程中,LLMs能够以事件或主题为核心,在保留情境信息和概念关系完整性方面具有优势,并能兼顾事件结构的整体性与分析性两方面的需求。在解释建构过程中,LLMs能够辅助研究者进行整体回顾、探索深层动机、结合社会文化背景、实现有洞察的解读与建构。文章通过示例呈现了LLMs在质性研究中的应用框架与操作方法,为研究者提供了快速掌握的操作指南。文章还提出未来的研究愿景,包括如何在不同研究范式中拓展LLMs的应用,以及LLMs参与质性研究的适用性与有效性。总体上,LLMs在快速整合大量信息、提供多角度分析方面展现出显著优势,但研究者自身在质性研究过程中依然起着关键作用。

Large Language Models (LLMs) demonstrate significant potential in qualitative research, particularly in the critical processes of data coding and interpretive construction. In data coding, LLMs excel at preserving contextual information and conceptual relationships while focusing on events or themes, balancing both holistic and analytical needs of event structure analysis. During interpretive construction, LLMs can assist researchers in comprehensive review, exploration of underlying motivations, integration of socio-cultural contexts, and insightful interpretation and construction. This article presents an application framework and operational methods for LLMs in qualitative research through examples, providing researchers with a quick-start guide. The article also proposes future research directions, including expanding LLM applications across different research paradigms and examining the applicability and effectiveness of LLMs in qualitative research. Overall, while LLMs show notable advantages in rapidly integrating large amounts of information and offering multi-perspective analyses, researchers maintain a crucial role in the qualitative research process.

黄浩宇、苏显懿、邱惠林、颜文靖、蒋柯

信息传播、知识传播科学、科学研究计算技术、计算机技术

大语言模型(LLMs)质性研究数据编码解释建构人机协作

Large Language Models (LLMs)qualitative researchdata codinginterpretation constructionhuman-AI collaboration.

黄浩宇,苏显懿,邱惠林,颜文靖,蒋柯.大语言模型在质性研究中的应用探索[EB/OL].(2024-10-24)[2025-08-16].https://chinaxiv.org/abs/202410.00153.点此复制

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