Abstract: In traditional medical genetics education, the genetic trait analysis component encounters several challenges. These include inefficient data collection and processing, limitations in pattern recognition and correlation analysis, and technical hurdles in integrating multi-source data. These issues severely impede teaching effectiveness and diminish students' learning experiences. With the rapid advancement of artificial intelligence(AI)technologies, deep learning, natural language processing, and computer vision have emerged as revolutionary tools for genetic trait analysis. This study developed a three-in-one AI-assisted teaching system encompassing “data acquisition,intelligent analysis,classroom application”. Empirical research involving clinical students and their relatives validated the substantial advantages of AI technology in genetic trait analysis instruction. Teaching practice demonstrated that AI-assisted teaching significantly boosted students' learning motivation, knowledge acquisition, and research proficiency. Students provided overwhelmingly positive feedback, noting that AI technology streamlined genetic trait analysis, enabling them to better comprehend the intricate mechanisms underlying genetic correlations. Moreover, AI-assisted teaching has spurred educational innovation, including technological breakthroughs, novel teaching models, and enhanced teaching value. This approach offers fresh perspectives and methodologies for the reform of medical genetics education.

Key words: medical genetics, genetic trait analysis, artificial intelligence, teaching reform, multi-source data integration