Research on Innovative Teaching Reform of Python Courses in Universities Based on Artificial Intelligence Tools

The rapid evolution of Artificial Intelligence (AI) presents both challenges and opportunities for programming education. Traditional teaching modes in Python courses often struggle to keep pace with industrial demands and technological advancements, leading to a gap between acquired skills and practical application. This paper analyzes the prevalent issues in current Python teaching, including disconnection from AI frontiers, monotonous pedagogy, insufficient resources, a single evaluation system, and lack of deep industry-academia collaboration. In response, we propose a systematic reform plan centered on a "Three Integrations, Three Innovations, and Three Links" framework. This model emphasizes the deep integration of artificial intelligence tools, such as DeepSeek and intelligent code analysis tools, a blended dual-mentor teaching approach, and the construction of a multidimensional evaluation system. By restructuring course content, innovating practice models, and forging a seamless cultivation loop, the reform aims to enhance students' practical and innovative abilities. The paper also discusses potential challenges in implementation and corresponding strategies. This model provides a replicable and scalable reference for the reform of programming courses in the context of "New Engineering Education" in universities.