While the adoption of AI-based design tools is accelerating in design education, limited research has examined learners’ psychological acceptance of these tools. This study therefore investigates perceptions of CLO 3D, Stable diffusion, and ChatGPT through the Technology Acceptance Model (TAM). Survey data were collected from 70 design majors at a university in Seoul and analyzed using regression methods, focusing on four key variables: perceived learning difficulty, efficiency, visual satisfaction, and commercialization potential. The results revealed paradoxical patterns in learning experience, where higher learning intention and perceived intuitiveness sometimes increased learning burden, while efficiency and output similarity reduced it. Efficiency perceptions were strengthened by learning intention, CLO 3D output similarity, and ChatGPT’s visualization support, but weakened when learners relied heavily on traditional creativity or when Stable diffusion’s creativity reflection was emphasized. Visual satisfaction was positively influenced by portfolio development and practical application intentions yet decreased when judged strictly by conventional creativity standards. Commercialization potential increased with efficiency, time savings, ChatGPT utilization, and application planning, but declined with greater familiarity with hand sketching. These findings validate TAM’s dimensions of usefulness and ease of use while highlighting the moderating role of comparison with traditional workflows. The study contributes theoretically by extending TAM to creative education contexts and provides practical guidance for developing instructional strategies that balance efficiency, creativity, and professional applicability.

Abstract
I. Introduction
Ⅱ. Review of Literature
    1. Generative AI and digital innovation in fashiondesign
    2. The technology acceptance model (TAM) andits application in this study
    3. Research variables and framework
Ⅲ. Research Method
Ⅳ. Results and Discussion
    1. Factors influencing perceived difficulty of AIlearning
    2. Factors influencing the perception of reducedwork time
    3. Factors influencing visual satisfaction
    4. Factors influencing perceived commercializationpotential
Ⅴ. Conclusion
References
 Demographic information of participants and their experience with AI and design tools

• Sujin Lim(Instructor, Dept. of Fashion and Textiles, Seoul National University, Korea) Corresponding author