THEORETICAL EXPLORATION ON THE REFORM OF TRADITIONAL CHINESE MEDICINE DIAGNOSTICS TEACHING EMPOWERED BY ARTIFICIAL INTELLIGENCE

Abstract

As a core compulsory course for Traditional Chinese Medicine (TCM) majors, TCM Diagnostics serves as a critical link connecting basic TCM theories and clinical practice, whose teaching quality directly determines the core competence of TCM talent cultivation. Currently, TCM diagnostics teaching faces practical dilemmas including abstract theoretical knowledge, insufficient practical scenarios, lack of personalized teaching, and rigid evaluation systems, which restrict the quality and efficiency of TCM talent cultivation. With its core advantages in data processing, intelligent simulation, and personalized adaptation, artificial intelligence (AI) technology demonstrates inherent consistency with the core thinking of TCM diagnostics such as "syndrome differentiation and treatment" and "holistic concept", providing a brand-new technological path for solving teaching difficulties and promoting teaching reform. Based on constructivist learning theory, embodied cognition theory and precision teaching theory, this paper systematically analyzes the theoretical foundation and internal logic of AI-empowered TCM diagnostics teaching reform, examines the existing problems in current TCM diagnostics teaching, explores the application paths of AI in theoretical teaching, practical teaching, assessment and evaluation of TCM diagnostics, and proposes targeted safeguard measures, so as to provide theoretical support and practical reference for promoting the digital and intelligent transformation of TCM diagnostics teaching and cultivating high-quality TCM talents who meet the needs of the new era.