Construction and Practice of a Digital Intelligence Physiology Course Integrating Knowledge Graphs and AI

Abstract

As a pivotal foundational medical course for nursing programs, physiology encompasses a complex knowledge system with numerous abstract concepts and close clinical relevance. Traditional teaching models face challenges including fragmented knowledge dissemination, inadequate support for personalized learning, and insufficient cultivation of clinical thinking skills. Taking the construction of the digital-intelligent physiology course based on knowledge graphs at the School of Medicine, Sias University, as a case study, this paper systematically presents the philosophy, implementation pathways, and practical outcomes of the tripartite integration model combining “knowledge graphs—AI—nursing practice.” The course design uses knowledge graphs as a framework to structure knowledge, leverages AI technology as an engine to empower the entire teaching process, and is driven by nursing practice to cultivate competencies. It has established a blended teaching model featuring “online preliminary self-study—offline intermediate flipped classroom—post-class advanced extension,” forging a new digital-intelligent educational ecosystem characterized by visualized knowledge presentation, personalized learning pathways, and precise instructional strategies. Practice has shown that this model effectively enhances students’ learning outcomes and clinical reasoning abilities, offering a replicable paradigm for the digital and intelligent transformation of basic medical courses.