Dynamic Updating Method of Course Content Based on Multiple Intelligent Agents Collaboration

In the rapidly evolving field of education, the timely updating of course content is an essential task, especially in disciplines such as computer science and artificial intelligence, where knowledge evolves swiftly. However, traditional methods of course revision are labor-intensive, time-consuming, and struggle to keep up with the latest academic developments and industry practices. Moreover, directly utilizing content generated by agent tools often results in a disorganized structure and questionable quality. To address these challenges, this paper proposes a dynamic updating method of course content based on the collaboration of multiple intelligent agents. By integrating multiple intelligent agents and knowledge graph technology, this method enables automated retrieval, filtering, and integration of the latest knowledge points, thereby generating a structured, accurate, and comprehensive course content structure. For engineering education, this method can help educators quickly adapt to the needs of technological development, which not only effectively alleviates the burden on educators but also significantly enhances the efficiency and quality of course updating, offering a novel solution for educational content management and promoting the development of engineering education towards intelligence and dynamism. Experimental results indicate that compared to traditional manual revision and direct utilization of single agent, this method maintains high accuracy (over 95%) and high coverage (over 96%), while reducing the knowledge point update cycle to less than 0.5 h and controlling the task execution cycle within 1.5 h.