AI-ENHANCED VISUAL EDITING TOOLS FOR ART SCHOOLS

Abstract

Current visual editing tools are changing the way art is taught through the incorporation of generative, analytical, and assistive computational analysis into studio practice through the use of AI. The paper discusses the effect of the diffusion networks, style-transfer systems, semantic segmentation engines, and restorative AI workflows on the learning of art, the development of skills, visual analysis, and exploration of concepts in art schools. The study proves the utilization of AI tools in quick ideation, better color and composition analysis, more available to learners with physical and cognitive disabilities, and more intensive interaction with stylistic experimentation. Simultaneously, the study also reveals such ethical, cultural, and legal issues as dataset transparency, authorship, cultural appropriation, and algorithmic bias. Four-layer integration framework is suggested to be used to be responsible in its adoption, including creative empowerment, skill deepening, ethical literacy, and institutional policy. The results highlight that AI must not be used as a replacement to the learning and practicing of basic artistic abilities but rather as a co-creative collaborator, capable of enhancing the ability to reflect and experiment as well as inclusive and critically informed visual education.