AI-Driven Reform of General Elective Courses: The Case of Hands-On Data Analysis with Python

This study addresses four critical challenges in the general elective course Hands-On Data Analysis with Python at vocational colleges: significant student competency stratification (only 14% possess programming foundations), imbalanced class-hour allocation (36 hours covering content from basic to advanced), a disconnect between learning and application, and unregulated AI usage. To tackle these issues, we developed a multi-dimensional reform framework that: (1) establishes a dual-track curriculum combining core modules in Python programming and basic data analysis with advanced electives (e.g., web scraping, machine learning); (2) implements a three-phase learning strategy—pre-class exploration, in-class intensive lectures, and post-class AI-assisted review—supported by the ChaoXing platform; and (3) designs a tri-dimensional assessment system evaluating project implementation completeness, result presentation standardization, and compliance with innovative AI-integrated practices. Moreover, integrating AI for intelligent tutoring alongside graded AI usage protocols (prohibited, restricted, encouraged and permitted) addresses individualized instruction gaps while mitigating ethical risks. Collectively, this framework provides a replicable paradigm for AI-driven reform of elective courses.