Evaluating technological and instructional factors influencing the acceptance of AIGC-assisted design courses

Abstract

Purpose: This study aims to explore the key factors influencing design students' acceptance of AIGC-assisted design courses, providing specific strategies for course design to help students better learn this new technology and enhance their competitiveness in the design industry. The research focuses on evaluating technological and course-level factors, providing actionable insights for course developers. Design/methodology/approach: The research establishes and validates evaluation dimensions and indicators affecting acceptance using structured questionnaires to collect data and employs factor analysis and weight analysis to determine the importance of each factor. Findings: The results of the study reveal that the main dimensions influencing student acceptance include technology application and innovation, teaching content and methods, and extracurricular learning support and resources. Regarding indicators, data privacy, timeliness of extracurricular learning support, and availability of extracurricular learning resources are identified as the most critical factors. Originality: The uniqueness of this study lies in providing specific course design strategies for AIGC-assisted design courses based on the weight analysis results for different dimensions and indicators. These strategies aim to help students better adapt to these courses and enhance their acceptance. Furthermore, the conclusions and recommendations of this study offer valuable insights for educational institutions and instructors, promoting further optimization and development of AIGC-assisted design courses.

Keywords

• Artificial intelligence assisted design
• Course acceptance
• Factor analysis
• Entropy method