现代教育技术2026，Vol.36Issue(5)：27-37,11.DOI:10.3969/j.issn.1009-8097.2026.05.003
质性-量化证据的整合:基于大语言模型的混合研究框架
Integration of Qualitative-Quantitative Evidence:A Hybrid Research Framework Based on Large Language Models
摘要
Abstract
Against the backdrop of the continuous evolution of research paradigms driven by artificial intelligence for Science(AI4S),research practice increasingly relies on multi-source data and interpretable evidence chains.In mixed-methods research,qualitative materials and quantitative data can hardly be coherently integrated within the same logical chain,which tends to results in disconnection between statistical results and situational experiences,leaving evidence integration at a formal level.To address this issue,this paper proposed a qualitative-quantitative integration framework consisting of an upward pathway,a downward pathway,and a quality-control mechanism by leveraging the capabilities of large language models(LLMs)in semantic representation and situational understanding.The framework established interpretable connections between quantitative structures and qualitative materials through upward and downward analysis pathways,supplemented by a quality control mechanism to ensure the robustness of the reasoning process.To verify the feasibility and explanatory power of the framework,this paper tested it with real data from teacher collaboration processes.The results showed that the framework can facilitate mutual validation across data types within a unified representational space,providing a systematic technical path to break through the limitation of evidence juxtaposition in mixed-methods research and laying a foundation for the integrative innovation of social science research methods in the AI4S era..关键词
大语言模型/人工智能科学/混合研究方法/融合框架/教师协作Key words
large language models/AI for Science(AI4S)/mixed-methods research/integrative framework/teacher collaboration分类
社会科学引用本文复制引用
卢淑怡,周春红,靳旭莹..质性-量化证据的整合:基于大语言模型的混合研究框架[J].现代教育技术,2026,36(5):27-37,11.基金项目
本文为2023年度全国教育科学规划一般课题"基于大语言模型的青少年人工智能教育研究"(项目编号:BCA230276)的阶段性研究成果. （项目编号:BCA230276）
