This study investigates the factors influencing undergraduate design students’ technological acceptance of Artificial Intelligence-Generated Content (AIGC) in design education. Utilizing Structural Equation Modeling (SEM), the research aims to develop a comprehensive model that elucidates the direct and indirect effects of personal attitudes, cognition and motivation, external environment, and usage behavior on the acceptance of AIGC technology. The study was conducted among 236 undergraduate design students from five universities in Zhuhai, Guangdong Province, using a stratified random sampling method. Data were collected via a 5-point Likert scale questionnaire, achieving a 100% response rate. Confirmatory Factor Analysis (CFA) and SEM were employed for data analysis and hypothesis testing. The findings indicate that personal attitudes (PA), cognition and motivation (CM), external environment (EE), and usage behavior (UB) have significant direct positive effects on the acceptance of AIGC technology (AA) (Std. = 0.353, 0.302, 0.309, and 0.210, respectively; all p < 0.01). Additionally, PA positively influences CM, EE, and UB (Std. = 0.425, 0.435, and 0.389, respectively; all p < 0.01), and indirectly affects AA through these mediators (Std. = 0.11, 0.115, and 0.07, respectively; all p < 0.01). These results highlight the pivotal role of personal attitudes in shaping students’ acceptance of AIGC technology. The study concludes that fostering positive personal attitudes, enhancing cognition and motivation, ensuring a supportive external environment, and promoting regular usage behavior are essential for integrating AIGC technology into design education. Future research should explore the acceptance of AIGC technology in diverse regions and educational contexts, incorporate multiple data sources, and examine the impact of demographic factors on technology acceptance.