Emotion-Driven AI Collaboration and Multimodal Learning in Vocational Education

With the rapid development of artificial intelligence technology, the application of personalized learning systems in vocational education has gained increasing attention. However, existing systems often handle only single-modal data and lack real-time monitoring and adaptive adjustments based on emotional states. To address these issues, this study proposes a personalized learning system based on human-machine collaboration, integrating multimodal data fusion and emotion-driven mechanisms, specifically designed for vocational education. Experimental validation shows that the proposed system significantly improves student learning outcomes and teacher satisfaction, particularly in the areas of complex interdisciplinary knowledge integration and personalized learning path generation. This study provides new insights into the design of personalized learning systems in vocational education and lays the foundation for the application of human-machine collaboration and multimodal data fusion technologies in the educational field.