Generative AI in Art and Design Education: A Systematic Review of Tools, Pedagogy, and Collaboration

Abstract

Generative AI (GenAI) is rapidly emerging as a transformative force in art and design education. It not only expands the boundaries of creative expression but is also reshaping traditional teaching models. Tools such as Midjourney, DALL·E, Stable Diffusion, Leonardo, and Firefly are now widely adopted in classrooms and studios, demonstrating formidable capabilities in image generation, visual storytelling, and creative exploration. These technologies empower students to generate high-quality visual works and design sketches at unprecedented speeds, fundamentally altering learning approaches and creative workflows. However, despite GenAI's growing adoption, related research remains in its infancy. Existing literature predominantly focuses on technical performance or creative potential, with limited systematic comparisons and analyses across different regions, disciplines, and teaching contexts. Concurrently, practical challenges and deeper educational implications have yet to be fully explored. Therefore, comprehensive research is needed to map current developments, unlock AI's untapped potential, and address emerging challenges. Accordingly, this study categorizes AI's role in education into three core dimensions to establish a systematic analytical framework: First, Generative Tools—emphasizing their function in creating artworks, optimizing techniques, and transforming styles; Second, Pedagogical Methods—focusing on AI's application in interactive learning and instant feedback, integrating it as a pedagogical scaffold; Third, Collaborative Partners—exploring AI's participation in artistic evaluation and its potential for real-time collaborative creation with humans. This three-dimensional framework enables a more comprehensive understanding of generative AI's multifaceted roles and value within higher education in art and design.