Multimodal deep learning for art behavior analysis and personalized teaching path generation
Abstract In the current era of continuous innovation in educational philosophy, the importance of art education for students ‘creativity, aesthetic ability, and all-round development has become increasingly prominent, with personalized education becoming a key trend. This study focuses on art educat...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-08-01
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| Series: | Discover Artificial Intelligence |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44163-025-00480-w |
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| Summary: | Abstract In the current era of continuous innovation in educational philosophy, the importance of art education for students ‘creativity, aesthetic ability, and all-round development has become increasingly prominent, with personalized education becoming a key trend. This study focuses on art education, aiming to address the lack of personalization in traditional teaching methods by proposing innovative solutions using multimodal deep learning technology. The research constructs a Multimodal Feature Fusion Network (MFFN) and an Evolutionary Path Generation Algorithm (EPG), utilizing Kinect sensors, digital tablets, and other devices to collect multi-modal data including visual, tactile, behavioral, and environmental information, establishing a large-scale art learning dataset. Experimental results show that the MFFN model performs excellently in technique recognition, emotion judgment, and attention detection tasks, achieving an accuracy rate of 93.7% in technique recognition. Personalized teaching paths generated based on EPG can dynamically adjust according to learners’ real-time capability profiles and learning progress, improving learning efficiency by 42.6%. Additionally, the study constructs a system architecture covering data layers, algorithm layers, and application layers, enabling real-time dynamic adjustment and visual traceability of teaching paths. This research establishes a multimodal analysis paradigm for art behavior, promoting the digitalization and personalization of art education, with significant application value in areas such as art training institutions, special education, and the digital transformation of art education. |
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| ISSN: | 2731-0809 |