Integration of Qualitative-Quantitative Evidence: A Hybrid Research Framework Based on Large Language Models

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..