<p>This study examines the factors influencing students’ acceptance of Artificial Intelligence Generated Content (AIGC) technology in product design instruction by extending the Technology Acceptance Model (TAM) with external factors: Learning Motivation and Cognitive Load. A cross-sectional survey was conducted with 400 undergraduate students from product design courses in Jiangsu Province, China, capturing demographic information, AIGC technology experience, and attitudes toward technology acceptance. Structural Equation Modeling (SEM) was used to analyze the relationships between the core variables of the Technology Acceptance Model (TAM), namely Perceived Ease of Use (PE), Perceived Usefulness (PU), and Behavioral Intention to Use (BIU). Additionally, Learning Motivation and three types of cognitive load—Intrinsic Load, Extraneous Load, and Germane Load—were considered as influencing factors in the analysis. Findings indicate that PE and PU are significant predictors of BIU, with motivated students perceiving AIGC as useful and beneficial. While Intrinsic and Extraneous Load negatively impacted PE, Germane Load positively influenced it, underscoring the importance of well-aligned content for usability. Mediation analysis further showed that PE and PU serve as intermediaries between cognitive, motivational factors, and BIU, suggesting that reducing cognitive barriers and aligning content with learning goals can improve AIGC adoption. This study contributes theoretically by extending TAM and provides practical insights for designing user-friendly and engaging AIGC tools. Future research could explore AIGC acceptance across disciplines and assess long-term impacts on learning outcomes.</p>