# Exploring the aesthetic cognition and artistic acceptance of AIGC-generated urban sculptures: A structural equation modeling and visual content analysis approach

## TL;DR

This study explores how people perceive and accept AI-generated urban sculptures, finding that visual features and trust in AI influence artistic acceptance.

## Contribution

The study introduces a novel structural equation model linking aesthetic cognition, emotional engagement, and trust in AI-generated art.

## Key findings

- Cognitive mastery and emotional arousal mediate the relationship between aesthetic features and perceived artistic value.
- Trust in AIGC and perceived artistic value jointly predict artistic acceptance intentions.
- Audiences can emotionally resonate with AI-generated sculptures when they are visually coherent and symbolically rich.

## Abstract

As artificial intelligence–generated content (AIGC) becomes increasingly integrated into creative practices, its application in public art—particularly in urban sculpture—raises fundamental questions regarding aesthetic cognition, emotional engagement, and artistic acceptance. This study proposes and empirically tests a conceptual model to explain how general audiences perceive and evaluate AIGC-generated urban sculptures. Drawing upon Leder et al.’s aesthetic appreciation framework and theories of human–AI trust, we develop a structural equation model (SEM) comprising seven latent constructs: visual aesthetic features, cognitive mastery, emotional arousal, perceived artistic value, trust in AIGC, artistic acceptance intention, and familiarity control. A total of 24 AI-generated sculpture stimuli were produced using Midjourney v6 and evaluated along five aesthetic dimensions through expert visual content analysis. Questionnaire data were collected from 326 respondents across sculpture parks, art plazas, and university campuses in China. SEM results reveal that both cognitive mastery and emotional arousal significantly mediate the relationship between aesthetic features and perceived artistic value. Moreover, trust in AIGC and perceived artistic value jointly predict acceptance intentions, highlighting the intertwined roles of perceptual, affective, and attitudinal factors in the legitimation of AI-generated art. This research extends classical aesthetic theory to non-human creative contexts and provides practical implications for the design, deployment, and public communication of algorithmically generated urban artworks. By demonstrating that audiences can cognitively and emotionally resonate with AI-generated sculptures—contingent on visual coherence, symbolic richness, and technological trust—this study offers a novel empirical foundation for future investigations into the cultural and spatial integration of artificial creativity. However, the ecological validity of the study is inherently limited, as the stimuli consisted of digital renderings rather than physical public sculptures. Therefore, the findings represent preliminary insights into audience responses to conceptual AIGC artworks.