ABSTRACT
Artificial Intelligence Generated Content (AIGC) has redefined the interaction between individuals and artistic works, injecting new sources of creative inspiration into artistic expression and providing exploratory directions and interpretative perspectives in response to the transformations of the digital era. This study employs portraiture as the research sample, integrating formalism and emotion theory, supplemented by literature on art education and cognition, to construct an art appreciation framework facilitated by the operation of AIGC tools. The proposed framework consists of six sequential stages: Recognize, Describe, Modify, Dissect, Reframe, and Integrate. This framework leverages AI-driven capabilities in image recognition, analysis, and generation to facilitate viewers’ engagement with aesthetic appreciation across dimensions such as composition, color, and emotional expression in artistic forms. Additionally, it examines variations in appreciation outcomes through the perceptual perspectives of diverse AIGC systems, thereby transcending reliance on specialized expertise and subjective interpretation. The findings not only expand the scope of art appreciation and AIGC applications, enriching aesthetic experiences, but also establish multidimensional and personalized pathways for aesthetic education, thus advancing the modernization and democratization of art education.