Collaborative optimization for interdisciplinary intelligent education systems: algorithm design and application

With the rapid development of intelligent education technology, how to build an efficient and intelligent interdisciplinary education system has become a key problem to be solved. This study proposes a design scheme of intelligent education system based on collaborative optimization algorithm, which effectively improves the intelligent level and application effect of the system by integrating multimodal data, optimizing algorithm parameters and realizing interdisciplinary collaboration mechanism. Intelligent tutoring system (ITS) is an adaptive learning support system that lets computers play the role of expert tutors to impart knowledge and provide learning guidance to learners with different characteristics with the help of artificial intelligence technology. It has a different basic structure and operation mechanism from the traditional tutoring system, and student model construction and adaptive guidance are its two core features. The research firstly constructs a theoretical model of collaborative optimization algorithm, develops three modules of data fusion, algorithm optimization and system integration by combining the characteristics of interdisciplinary education system, and conducts application tests in real education scenarios. The experimental results show that the system exhibits significant advantages in classroom management, learning path recommendation and personalized teaching feedback. This study provides new ideas for the further optimization of intelligent education systems and lays a theoretical and practical foundation for educational innovation in interdisciplinary fields.