Volume 2 · Issue 6 (2025)
A Systematic Review of Aesthetic Judgment and Creativity Cognition in AI Visual Art
Haibo Gao
City University of Macau, Zhuhai, Guangdong, China
Abstract: Objective: With generative AI technologies such as diffusion models increasingly shaping the production and circulation of visual art, the question of how AI-generated works acquire identity and value within artistic contexts has become urgent and calls for a systematic response. This study conducts a systematic literature review focusing on (1) how the aesthetic status of AI visual art is conceptualized, (2) evaluative differences between human- and AI-produced works, and (3) the mechanisms underlying aesthetic bias.
Methods: Following the PRISMA 2020 guidelines, we searched Scopus and Web of Science for English-language publications from 2016 to 2026. After merging records, removing duplicates, and screening according to predefined inclusion and exclusion criteria, 55 journal articles were included. We coded and synthesized research orientation, artwork samples and the information provided in their presentation, evaluation dimensions, experimental paradigms, and primary findings using a thematic approach.
Results: First, at the level of value justification, the eligibility of AI visual art as an aesthetic object is largely framed as context-dependent, hinging on the attribution and traceability of responsibility in the creative process, the explainability of agency, and recognition by artistic institutions and cultural frameworks. Second, at the level of empirical comparison, “source information” exerts a relatively stable influence on evaluations and affects perceptions of artistry, creativity, and authenticity more strongly than it affects ratings of beauty. Third, at the level of mechanisms, insufficient attribution of creative agency and intention, essentialist beliefs about creator uniqueness, culturally embedded evaluative frameworks, and the joint effects of data and algorithms on the visible form of artworks lead to systematic downward adjustments in perceived artistry and creativity of AI works, with effect sizes varying by task context and detection methods.
Contributions: Using an “acceptance value–creative agency” analytical framework, this review stratifies normative arguments and empirical evidence, clarifies cross-study differences in evaluation metrics, artwork sampling and presentation, and task design, and proposes testable recommendations to improve cross-study and cross-cultural comparability.
Keywords: AI art; Generative AI; Visual art; Aesthetic judgment; Creativity cognition; Systematic literature review
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