Analysis of the application of generative artificial intelligence in interior design education
Yao Liu, Bing Xu, Jiarong Feng, Pengjun Wu
Abstract
The rapid advancement of artificial intelligence (AI) has introduced generative AI tools as transformative resources in interior design education, enhancing creativity, aesthetic quality, and practical design outcomes. Traditional interior design education often limits students’ theoretical knowledge, aesthetic skills, and technical abilities development. Generative AI tools such as Stable Diffusion and Midjourney, which utilize big data and deep learning, offer innovative design concepts to address these limitations. This study applies established models—UTAUT and AHP—in a novel educational context to evaluate generative AI tools in terms of creativity, aesthetics, practicality, and feasibility, offering empirically grounded insights for interior design pedagogy. Results showed that Stable Diffusion excelled in creativity, while Midjourney outperformed in aesthetics and functionality, with both tools proved more feasible than traditional methods. Despite challenges such as limited technical support and high hardware requirements, generative AI tools can significantly enhance interior design education by fostering innovation and improving design efficiency.