Visual Recognition in Art Education: Enhancing Creativity, Assessment, and Personalized Learning through AI-Driven Technologies

Abstract

Art education plays a pivotal role in fostering creativity, critical thinking, and cultural understanding among students. However, traditional teaching methods often fall short in addressing individual learning needs and integrating modern technological advancements. The advent of artificial intelligence (AI), particularly visual recognition technology, offers transformative potential for art education by enabling innovative approaches to teaching, learning, and assessment. This study explores the application of visual recognition in art education, focusing on its ability to enhance creativity, improve assessment methods, and enable personalized learning. By integrating key learning theories—such as generative learning theory, personalized learning theory, constructivism, and interdisciplinary integration theory—with the capabilities of visual recognition, a theoretical framework is developed to guide its implementation. Case studies demonstrate practical applications, including AI-driven art creation tools, automated assessment systems, and personalized learning platforms, which address the limitations of traditional methods. Despite its potential, challenges such as ethical concerns, algorithmic bias, and the need for teacher training must be addressed. This research highlights the opportunities and challenges of integrating visual recognition into art education, providing a foundation for future research and practice. The findings underscore the importance of balancing technological innovation with the preservation of the humanistic and emotional aspects of art education, ultimately empowering students to become more creative, critically thinking, and culturally aware individuals.