A Study on a Multimodal Agentic RAG Framework for Integrating Multi-Source Heterogeneous Information in Higher Education Institutions

Against the backdrop of digital transformation in higher education, campus information is often dispersed across independent platforms, creating fragmented access and retrieval barriers. To address these challenges, this study proposes a multimodal agentic RAG framework for integrating multi-source, heterogeneous university information through a unified natural language interaction portal. Compared with basic RAG architectures, the framework introduces three improvements: (1) vision–language models convert unstructured visual content into indexable textual evidence; (2) BM25 sparse retrieval, dense-vector retrieval, and real-time online retrieval jointly support keyword matching, semantic retrieval, and up-to-date information access; and (3) agent-based dynamic routing adaptively schedules retrieval tools according to query intent and evidence sufficiency, reducing redundant calls. Experiments show that the proposed framework achieves higher overall performance in answer accuracy, factual consistency, evidence coverage, and composite score than general-purpose large language models, traditional single-path RAG, and representative advanced RAG methods including Self-RAG, Adaptive-RAG, and VisRAG. Ablation studies further validate the contributions of the core modules. Overall, the framework provides a low-disruption solution for unified information retrieval and natural language interaction in higher education scenarios.