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Munich University of Applied Sciences, Germany

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Home > Archive > 2025 > Volume 15, Number 5, 2025
IJSSH 2025 Vol.15(5): 156-165
doi: 10.18178/ijssh.2025.15.5.1258

Determinants on the Learning Satisfaction of Art and Design Major Undergraduates in Traditional Craft Courses Based on AIGC

Chaochu Xiang
Visual Communication Design Department, Academy of Fine Arts and Design, Chengdu University, Chengdu, Sichuan, China
Email: xiangchaochu@cdu.edu.cn

Manuscript received July 7, 2025; accepted August 3, 2025; published September 10, 2025.

Abstract—Artificial Intelligence Generated Content (AIGC) technology has produced an essential impact in the field of higher education, and is a vital breakthrough point to open a new educational track and develop educational advantages in China. Currently, art design majors in multiple universities of Sichuan have begun to apply AIGC technology in the professional course instruction and curriculum achievement evaluation, while the academic circles have conducted relatively few quantitative research theoretical results on it. In order to analyze target students’ learning satisfaction with AIGC, this study established six determinants, including Information Quality, System Quality, Interaction Learning Quality, Perceived Ease of Use, Perceived Usefulness, and AI-Assisted Design, constructed corresponding scale items, and collected empirical data. Finally, descriptive data analysis, Confirmatory Factor Analysis (CFA), and Structural Equation Model (SEM) were conducted on 546 valid samples by JAMOVI, SPSS, and AMOS statistical analysis software. It is statistically verified that all independent and mediator variables have positive and significant effects on the dependent variables, among which AI-Assisted Design has the greatest effect on Learning Satisfaction. It is suggested that the corresponding instruction units and relevant frontline teachers could evaluate and adjust the corresponding teaching of the current AIGC, to obtain the ideal teaching effect.

Keywords—Artificial Intelligence Generated Content (AIGC), satisfaction, technology acceptance model, information system success model, structural equation model

Cite: Chaochu Xiang, "Determinants on the Learning Satisfaction of Art and Design Major Undergraduates in Traditional Craft Courses Based on AIGC," International Journal of Social Science and Humanity, vol. 15, no. 5, pp. 156-165, 2025.

Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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