"Microteaching" serves as a core practical course for Master of Education students. Traditional teaching models face challenges such as vague and delayed feedback

rigid teaching resources

poor situational simulation

and insufficient innovation cultivation

making them inadequate for the professional training needs of modern educators. To address these issues

this study focuses on Master of Education (Chinese) candidates in higher education institutions

employing a controlled experimental approach to implement AI-enhanced curriculum reform. Through modular project design

generative AI is deeply integrated into the entire teaching process

including instructional design

case resources

situational simulation

and personalized tutoring

thus establishing a step-by-step skill development system from foundational knowledge to innovative practice. Evaluation results demonstrate that learners under the AI-enhanced model show significant improvements in both microteaching-specific skills and comprehensive applications

with overall teaching effectiveness markedly optimized compared to traditional methods. The research indicates that generative AI provides robust support for precise and personalized cultivation of teaching skills in Master of Education programs. This study not only enriches theoretical research on the deep integration of technology and teacher education but also offers replicable practical paradigms for digital transformation in similar courses.