Exploring AI Application Capability among Undergraduate Fashion Design Students: Current Status and Influencing Factors

Background and Aims: As artificial intelligence becomes increasingly important in fashion design education, students’ ability to apply AI tools has become a key issue in talent development. This study aimed to examine the current status of AI application capability among undergraduate fashion design students and to identify its main influencing factors. Methodology: A quantitative research design was adopted. Data were collected from 180 undergraduate students majoring in Fashion and Apparel Design at Xianyang Normal University, China. Descriptive statistics, independent samples t-tests, one-way ANOVA, correlation analysis, and hierarchical regression analysis were used to analyze the data. Results: The results showed that students’ self-rated AI application capability was at a moderate level (M = 3.17, SD = 1.03), while their developmental demand was relatively high. Significant differences were found across gender and grade level. Personal, school, and social factors were all positively associated with AI application capability. Hierarchical regression analysis further showed that these three factors had significant positive effects, with grade remaining the strongest predictor (β = .383, p < .001). Conclusion: AI application capability among fashion design students is shaped by multiple factors and develops progressively across academic stages. Improving this capability requires stronger student engagement, better curriculum support, more practice-oriented teaching, and closer university–industry collaboration. However, the findings should be interpreted with caution because the study relied on self-reported data from a single university.