Path of practical teaching of artificial intelligence technology incorporated into animation majors

As Artificial Intelligence (AI) technology rapidly develops, the animation industry is undergoing a transformation from traditional production to intelligence. However, animation education faces challenges such as lagging technological iteration and insufficient interdisciplinary integration. To systematically solve the problem of the disconnect between AI technology and artistic creation, this study constructs an animation practice teaching path that integrates AI technology, proposes an animation design framework that combines generative AI with human pose estimation, and develops an animation-oriented optimization strategy for Transformer-based Human Pose Estimation (HPE). This strategy enhances temporal smoothness for character animation generation. The experiment shows that the model incorporating this optimization strategy reaches a stable training plateau after 15 epochs, with an error reduced to 0.15, and the animation generation quality score improves to 92 points, with an efficiency increase of 38%. Teaching practice verification shows that the comprehensive improvement of technical application, artistic creation and other indicators in the AI integrated teaching group is significantly better than that in the traditional teaching group. Research results denote that this path effectively bridges the gap between technological application and artistic thinking, providing a systematic solution for animation education and related creative technology fields.