Research on the Practical Teaching Reform of “Deep Learning and Applications” Course Supported by Generative AI Technology

Abstract

With the rapid advancement of artificial intelligence, deep learning has become a core competency for students in computer science and related fields. However, the traditional practical teaching of "Deep Learning and Applications" faces significant challenges, including a steep learning curve, a notable gap between theoretical knowledge and practical application, insufficient computational resources, and a lack of personalized guidance, which collectively stifle student innovation and engagement. This paper explores a novel pedagogical reform for this course, centered on the integration of Generative AI (GenAI) technologies. We designed and implemented a new practical teaching framework that leverages GenAI as a multifaceted tool for code generation and debugging, synthetic data creation, personalized tutoring, and creative project development. Through a semester-long empirical study involving undergraduate students, we evaluated the effectiveness of this reformed curriculum. The study employed a mixed-methods approach, including pre- and post-course surveys, analysis of final project quality, and qualitative feedback. The results demonstrate that the GenAI-supported approach significantly enhances students' practical skills, deepens their conceptual understanding, and boosts their problem-solving capabilities. Specifically, students showed marked improvements in model implementation efficiency, debugging proficiency, and the ability to undertake more complex and innovative projects. The integration of GenAI not only lowered the technical barrier to entry but also fostered a more dynamic and interactive learning environment, effectively bridging the theory-practice divide. This research provides valuable insights and a replicable model for reforming advanced technology courses, highlighting the transformative potential of Generative AI in modern higher education.