Artificial Intelligence in Fine Arts Education: A Systematic Literature Review

Artificial Intelligence (AI) has emerged as a transformative force across various domains, including education. In fine arts education, AI offers unprecedented opportunities to innovate teaching methodologies, foster creativity, and enhance student engagement. While significant advancements have been made in integrating AI into fine arts education through techniques such as Generative Adversarial Networks (GANs), Convolutional Neural Networks (CNNs), and Large Language Models (LLMs), there is a notable lack of a comprehensive review summarizing these developments. To bridge this gap, this study systematically examines the most adopted applications of AI in fine arts education from 2019 to 2024. Utilizing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, an exhaustive review of 78 studies was conducted, focusing on the types of technologies commonly employed by AI in fine arts education and the specializations within fine arts education that leverage AI. This review further provides a structured analysis of existing literature, focusing on their methodologies, objectives, and trends. The study highlights the transformative potential of AI in fine arts education while identifying critical gaps and challenges that require further investigation. By offering a comprehensive analysis, it serves as a roadmap for educators, researchers, and policymakers to harness AI’s capabilities in the fine arts domain effectively.