Exploring the Application of Multimodal Large Models in Environmental Art Design Education

With the rapid advancement of multimodal large models, their integration into education has opened new possibilities for enhancing creative and design-oriented disciplines. This study explores the application of multimodal AI modelscapable of processing and reasoning over images, audio, and textin the context of environmental art design education. A unified teaching framework is proposed that leverages cross-modal attention and contrastive learning to generate accurate and personalized feedback for students. Experimental results on a simulated dataset demonstrate significant improvements in feedback quality, multimodal alignment, and student satisfaction compared to traditional and text-only AI models. The findings suggest that multimodal models offer a promising direction for transforming art and design pedagogy by enabling more interactive, context-aware, and efficient learning experiences.