Research on Optimizing Teaching Models for Higher Education in Traditional Chinese Medicine through Hybrid Intelligent Systems

However, in modern education, learning advanced knowledge, theory, and technology is a development trend that traditional Chinese medicine education needs to fully adapt to, and will become the basis to optimize the teaching model. The hybrid intelligence system of this paper based on knowledge reasoning and adaptive learning technology provides the teaching mode optimization for TCM higher education, and provides a reference for the personalization and flexibility of education. First, in proposed model use knowledge reasoning technology of analysis student learning progress and knowledge mastery by establishing the knowledge graph and expert reasoning mechanism of traditional Chinese medicine, further derived suitable for student's learning path. Artificial intelligence technology can be based on students' individual differences, in a complex knowledge system of reasoning and automatically adjust the content and difficulty of learning, so as to better meet the learning needs of students. 2. Adaptive learning technology employs smart algorithms to monitor students' learning behaviors and performance in real time and dynamically update teaching strategies and learning content. The system also recommends learners certain learning resources automatically, ensure that learners can make learning at the best pace to maximize learning result according to their performance and students' feedback. The experimental results indicate that the proposed model is effective in enhancing students' engagement, knowledge retention, and learning efficiency.