Innovative Practice and Exploration of Large-Model-Empowered Project-Based Teaching in Higher Vocational Education
DOI:
https://doi.org/10.54097/gjdg4991Keywords:
Large Model, Project based Teaching, HTML5 Application Development, Smart Shipping, Three tier HierarchyAbstract
With the rapid development of generative artificial intelligence represented by large language models, vocational education is encountering critical opportunities for teaching model transformation. At present, project‑based teaching for software technology majors in higher vocational colleges faces prominent challenges, including insufficient differentiated instruction, lack of personalized guidance, and disconnection between teaching content and industrial demands. Taking the course Fundamentals of HTML5 Application Development as a practical carrier and drawing on the reform practice of “Large Model + Smart Shipping” in our college, this paper systematically elaborates the implementation path of large‑model‑empowered project‑based teaching. This study proposes an innovative teaching mode integrating a three‑tier hierarchical project system, human‑AI collaborative learning guidance, and multi‑dimensional process‑oriented assessment. By constructing a course‑specific prompt library, a hierarchical project case library and an intelligent auxiliary evaluation system, full‑chain teaching empowerment of large models is realized throughout pre‑class, in‑class, after‑class and assessment phases. Practical results show that full‑process empowerment by large models can effectively address pain points such as inadequate hierarchical adaptation and shortage of personalized guidance in project‑based teaching. It provides a replicable practical paradigm for the digital and intelligent transformation of curriculum teaching for software technology majors in higher vocational colleges.
Downloads
References
[1] Song, X. L., & Liu, L. (2025). Construction and practice of large model based AI intelligent assistant system for software engineering courses. In Proceedings of 2025 National Conference on Artificial Intelligence in Education (AIEC2025) (pp. 160–165). Research Association of Higher Education Computer Education of China. https://doi.org/10.26914/c.cnkihy.2025.117698 (in Chinese)
[2] Chen, J. Z. (2026). Construction and application of human AI collaborative programming teaching mode for junior high school Python courses [Doctoral dissertation]. Qufu Normal University. https://doi.org/10.27267/d.cnki.gqfsu.2026.000440 (in Chinese)
[3] Wang, H. Y., Liu, J., & Hu, J. (2026). AI empowered teaching reform for the C language programming course. Computer Education, (4), 105–109. https://doi.org/10.16512/j.cnki.jsjjy.2026.04.009 (in Chinese)
[4] Li, L., Guo, X. Y., Lü, J. N., et al. (2026). Exploration of teaching reform for Python programming courses in the context of large language models. Software Guide, 1–5. (in Chinese)
[5] Qi, Z. X., & Li, X. L. (2026). Research on wisdom teaching system based on “teacher student machine” tripartite interaction for the course organizational behavior. Western China Quality Education, 12(5), 13–16. https://doi.org/10.16681/j.cnki.wcqe.202605004 (in Chinese)
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Academic Journal of Education

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.










