Work

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

Year: 2026

Type: article

Abstract: 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.

Source: Big Data and Cognitive Computing

DOI: 10.3390/bdcc10090291

Authors: Minghan Li, Haotian Wang, Zekai Sun, Hang Zhou

Institution: Beijing Jiaotong University

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: gold

Estimated APC paid: $2,172