Volume 8,Issue 9
Building a Hierarchical and Progressive Teaching Model for PythonT Programming Empowered by Generative AI
Against the background of the digital era, the in‑depth integration of artificial‑intelligence technology and higher education has become a core trend of educational reform. Intelligent teaching, personalized teaching and targeted talent cultivation have turned into the core development directions of university curriculum teaching in the new era. Featuring concise syntax, wide application scope and strong expandability, Python programming covers multiple technical fields including big data, artificial intelligence, data analysis, back‑end development and automated operation‑and‑maintenance. It constitutes a fundamental programming skill essential for contemporary college students, and also a core university course for cultivating computational thinking and science‑and‑innovation‑oriented talents. The hierarchical and progressive teaching model for Python programming empowered by generative AI realizes precise adaptation and continuous advancement in the whole teaching process through constructing a dynamic student‑stratification system, a stepped teaching‑content system, an intelligent hierarchical‑tutoring system and a process‑oriented hierarchical‑evaluation system. Accordingly, this paper conducts research on constructing the hierarchical and progressive teaching model for Python programming empowered by generative AI, hoping to provide certain references for relevant researchers.
[1] Shi G, Hu Y, Lu C, 2026, Exploration on Stratified-Teaching Reform of the Course Practical Logistics Data Analysis Driven by AI. Logistics Engineering and Management, 48(2): 152–155.
[2] Wang Y, Zhu Y, Yin T, et al., 2025, Teaching Practice of Web-Development Course Oriented Toward Vocational Competence: "AI-Empowered, Project-Led, Hierarchical-Progressive". Computer Knowledge and Technology, 21(36): 166–170.
[3] Zhao Z, 2025, Practice of AI Dynamic Stratified-Teaching Model in University Optional Football Courses. Sports Style, (19): 41–43.
[4] Sun J, 2025, Construction of Multi-Dimensional Stratified-Case-Teaching Model for Higher-Vocational Economic-Law Course Empowered by AI. Investment and Cooperation, (9): 208–210.
[5] Chang X, 2025, Empirical Research on AI-Supported Hierarchical Interactive Teaching Strategies and Effects in Java Classroom. Information & Computer, 37(17): 212–214.
[6] Guo Y, Chu X, Zhao T, 2025, Experience in Applying Hierarchical-Progressive Teaching Mode to Clinical Immunoassay Internship Teaching. Science & Education Article Collects, (15): 154–158.
[7] He Y, Huang B, 2025, Application of Hierarchical-Progressive Teaching Mode Based on Intelligent Technology in Internship Teaching for Medical-Laboratory-Technology Major. China Continuing Medical Education, 17(4): 80–84.
[8] Dai W, Chu D, Wang S, et al., 2023, Research on the Reform of Integrating Online-Offline Mixed-Teaching Mode Under Hierarchical-Progressive Education Into Chemistry of Chinese Materia Medica Experimental Teaching. Guangdong Chemical Industry, 50(23): 172–174.
[9] Zhou A, Fu X, Du S, et al., 2022, Research on Hierarchical-Progressive Practical-Teaching Mode for Computer-Aided Mapping Based on OBE Concept. University Education, (7): 66–68.
[10] Zhang X, 2020, Exploration on the Reform of Hierarchical-Progressive Extracurricular-Practical-Teaching Mode for Internet-of-Things Major. Office Automation, 25(6): 33–34 + 61.
