The analysis of teaching behavior and optimization of digital literacy cultivation path for college teachers driven by AI

To address the problem of identifying teaching behavior characteristics and optimizing the path for improving digital literacy abilities of university teachers, a multidimensional AI analysis framework integrating computer vision, semantic analysis, graph neural networks, and reinforcement learning is constructed. By collecting multimodal data and modeling graph structures, accurate qualitative and structural expression of teaching behavior characteristics is achieved. Combined with the five-dimensional competency system, a behavior driven evaluation model and path recommendation mechanism are established. Adopting the path optimization strategy of DQN, constructing an intelligent intervention tool and conducting experimental verification to verify the superiority of the model in terms of positioning accuracy, improvement of quality ability, and cross school transfer ability. The research provides intelligent solutions for the development of teachers' abilities and technical support for the digital transformation of the education system.