🤖 AI Summary
To address the heterogeneity and fragmentation of medical educational resources—which impedes seamless integration with Learning Management Systems (LMS)—this paper proposes a medical knowledge-driven multimodal content structuring and mapping framework. It enables, for the first time, fully automated conversion of heterogeneous digital medical resources (e.g., medical images, scholarly literature, clinical records) into SCORM/AICC-compliant e-learning packages. The method integrates medical ontology modeling, rule-guided template instantiation, NLP-based entity-relation extraction, and XSLT-based packaging to ensure semantic alignment and automatic generation of pedagogically sound instructional logic. Evaluated across three medical schools, the framework achieves a 12× improvement in course package generation efficiency, 100% LMS compatibility, and a 76% reduction in post-generation instructor editing time. This work establishes a reusable, high-fidelity, and standards-compliant automation paradigm for digital medical education content production.