Research on the Application of StableDiffusion-Based Generative Al Tools inEnvironmental Art Design Education
Year: 2025
Type: article
Abstract: The breakthrough development of Artificial Intelligence Generated Content (AIGC) technology is reshaping the creative paradigm in the field of environmental art design.In 2023, an article on a deep learning-based model for automatic urban planning was first published in Nature Computational Science [1].Traditional design tools are increasingly showing limitations in addressing the complexity, efficiency demands, and innovation dimensions of contemporary design needs.In contrast, generative AI tools (such as MidJourney, Stable Diffusion, etc.), through algorithm-driven creative models, demonstrate revolutionary potential in fields like architectural design, visual communication, and environmental art design [2].These technologies can not only efficiently solve complex problems such as spatial form generation and multi-element collaborative design but also promote a paradigm shift in design thinking from "manual trial-and-error" to "intelligent evolution."In higher education, creative learning ability, as a core competency for individual lifelong development and social progress, has become an important goal in cultivating design talent [3].This research focuses on the pedagogical innovation of generative AI in environmental art design education, using Stable Diffusion platform's Text-to-Image and Image-to-Image functionalities as the technical carriers.Through literature review, case studies, and process-based design methods, it deeply explores the application and potential for improvement of generative AI design tools in environmental art design education.Research findings indicate that generative AI provides instant visual feedback, expands the boundaries of design possibilities, and promotes human-AI collaborative innovation, bringing a technological tool revolution to environmental art design education.
Source: Aussie-Sino Studies
DOI: 10.64210/olvg8040
Author: Wentao Leng
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: bronze
OpenAlex ID: W4412465846