
info@juzhikan.asia
Qingdao Hengxing University of Science and Technology, Qingdao City, Shandong Province 266000;
Abstract:With the advent of the digital age, traditional fashion design education faces several challenges, particularly the lack of innovative digital tools to support teaching. Traditional courses primarily rely on hand-drawing and static images as main teaching resources, which limits students' design capabilities and creative expression. In this context, Artificial Intelligence Generated Content (AIGC) technology offers a new optimized path for fashion design education. AIGC technology generates multimodal content, such as character assets and fabric simulation images, greatly enhancing the diversity of teaching resources and the efficiency of design creation. This paper compares the teaching effects of traditional design methods and AIGC-assisted design methods, exploring the advantages and disadvantages of both approaches. In the experiment, two groups, a "Traditional Group" and an "AIGC Group," were formed to engage in design creation. The differences between the two groups in terms of inspiration gathering, presentation of renderings, and application of technology were compared. The results show that AIGC technology significantly improves students' creative efficiency, enabling rapid design iteration through AI-generated renderings, thereby stimulating creative thinking. However, designs generated by AI may lack the emotional depth and personalization found in traditional designs. The traditional model, on the other hand, offers unique advantages in terms of artistry and personalized design, but the process is cumbersome and feedback is less timely, which limits students' design speed and innovation. Moving forward, teachers and students should strive to find a balance between AI and creative expression in the teaching process, promoting the organic integration of traditional design and AIGC technology to achieve the modernization and digital transformation of teaching methods.
Keywords: AIGC;Fashion Design ;Teaching path
References
[1]Liu, Z. Y. (2025). AIGC-enabled optimization of teaching paths for visual communication design: A case study of IP image design course [In Chinese]. Footwear Technology and Design, 5(16), 60–62.
[2]Wang, X. Y., Li, Y. Y., & Niu, S. (2025). Research on the application of AIGC technology in IP image clothing design [In Chinese]. Leather Science and Engineering (West China), 47(14), 122–124.
[3]Yuan, X. Y., Wu, Z., & Yan, X. (2025). The transformation of fashion design education paradigm in the AIGC era [In Chinese]. Shanghai Fashion, (08), 159–161.
[4]Kong, F. D., Ge, W. X., & Li, K. X. (2025). Application status and development prospects of AIGC in the field of fashion visualization [In Chinese]. Journal of Xi’an Polytechnic University, 39(3), 70–79.