A Study on the Teaching Practice of AI-Empowered Traditional Textile Pattern Art Design: Synergistic Development of Inheritance and Modern Innovation
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Abstract
Traditional textile patterns, as an important component of national cultural heritage, contain rich historical information and aesthetic value. However, their design inheritance faces difficulties including strong dependence on craft experience and insufficient teaching innovation. The development of artificial intelligence provides new possibilities for revitalizing traditional patterns and reforming design teaching. This study constructs an image dataset containing multiple categories of traditional textile patterns, proposes a pattern innovation generation method based on conditional deep convolutional generative adversarial networks, and builds an AI-assisted design teaching platform. The platform is applied in course practice, and teaching effects are evaluated through quantitative assessment and questionnaire survey. Results show that AI-assisted teaching effectively improves students’ understanding of traditional patterns and enhances their innovation ability. The generated patterns retain traditional elements while presenting diversified modern aesthetic characteristics. The proposed teaching model provides a feasible path for coordinating inheritance and innovation in traditional textile pattern design, and supports the digital transformation of textile art education.
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