Innovative Research on Interdisciplinary Collaborative Education Mechanisms Driven by Artificial Intelligence

This study systematically explores the innovative applications and mechanisms of artificial intelligence (AI) in interdisciplinary collaborative education to reconstruct interdisciplinary learning systems. The research focuses on three core dimensions: Intelligent Personalized Learning: AI dynamically optimizes interdisciplinary learning paths based on learning analytic and adaptive algorithms, improving knowledge integration efficiency. Collaborative Learning Platforms: Utilizing multi-agent matching and knowledge graph technologies, AI facilitates efficient knowledge construction in interdisciplinary teams. Multidimensional Dynamic Assessment: Combining cognitive analytics, behavioral analytics, and machine learning algorithms, AI enables precise learning evaluation and adaptive feedback. The findings demonstrate that AI technologies significantly enhance students' interdisciplinary problem-solving abilities and team collaboration efficacy, while effectively bridging traditional disciplinary divides. This study provides a theoretical framework and practical pathway for transforming interdisciplinary education in the AI era, offering critical insights for cultivating interdisciplinary innovators.