Utilizing an Aigc-Supported Lesson Planning Approach to Enhance Teacher Education Students’ Performance in Educational Practice Course

This study investigates the effectiveness of an Artificial Intelligence Generated Content (AIGC)-supported lesson planning approach in enhancing teacher education students' performance in educational practice courses. A quasiexperimental pretest-posttest control group design was employed in this study, including 100 education majors divided into experimental and control groups. The experimental group utilized an AIGC-supported approach for lesson planning, while the control group followed traditional methods. Results revealed that the intervention significantly reduced test anxiety and improved test grades in the experimental group, while the control group showed no notable changes. These findings highlight the potential of AIGC technology to address the limitations of traditional instruction by providing personalized feedback, iterative refinement, and dynamic support, fostering creativity, critical thinking, and self-efficacy. This study underscores the transformative role of AIGC in bridging the gap between theory and practice, offering valuable insights for integrating AI-driven tools into teacher education.