Abstract:

To address the challenges of outdated teaching resources, insufficient practical skills, and a lack of value-oriented guidance in education, this study constructs an innovative pedagogical model driven by the dual-engine of large language model (LLM) and open source ecosystem. The model is designed to bridge the gap between theoretical knowledge and real-world engineering practice by integrating open-source tools, dynamic code repositories, and authentic project scenarios into the curriculum. Meanwhile, LLMs are employed as intelligent teaching assistants to enable personalized learning paths, generate automated feedback, and support immersive ideological and ethical modules. This research was implemented in the course “Artificial intelligence and its applications”, where a mixed-method evaluation was conducted. Quantitative metrics such as attendance, interaction frequency, repository contributions, and assignment performance were used to measure student engagement and learning effectiveness. Additionally, a set of custom-designed assessment formulas was used to evaluate cross-platform transferability and community participation. Experimental results from 90 undergraduate students showed that learners engaged in open-source collaboration and LLM-assisted learning achieved significantly higher scores in both technical proficiency and value cognition than those in the control group. The study demonstrates that the integration of LLMs and open-source collaboration can effectively enhance student autonomy, promote engineering skills, and reinforce ethical awareness. This dual-driven model not only offers a feasible approach for modernizing AI education but also contributes to the broader goal of cultivating socially responsible and technically competent AI talents.

Key words: artificial intelligence, large language model, open source ecosystem, instructional reform