Optimizing Curriculum Design for Professorship Education Using CAD and Neural Network Technologies

Abstract

The traditional curriculum design methods suffer from issues like outdated content and limited instructional approaches. To address these, this article proposes an optimized curriculum design system that integrates CAD and neural network models. This system enables intelligent curriculum content generation, introduces CAD-assisted instructional practices, and plans personalized learning paths. To validate the effectiveness of this framework, two majors were chosen as case studies. Courses were designed using both the optimized framework and traditional methods, and evaluations were conducted on student learning outcomes, course satisfaction, and teacher feedback.Results indicate that the experimental group using the optimized framework showed significant improvements in learning effectiveness, course satisfaction, and teaching efficiency. Students' academic performance and practical skills improved markedly, with higher course satisfaction. This study offers a new approach for professorship education curriculum design, supporting innovative educational technology applications.