Exploring the aesthetic cognition and artistic acceptance of AIGC-generated urban sculptures: A structural equation modeling and visual content analysis approach
Hao Fang, Bowen Li, Ziwen Zhou, Mu Li, Huasheng Lai, Yihao Zheng, Bin Hu, Weichang Chen
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.
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…
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Taxonomy
TopicsAesthetic Perception and Analysis · Creativity in Education and Neuroscience · Visual Attention and Saliency Detection