Evidence map›Paper›PMID 41818281›Full record

ArticlePloS one2026

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

Abstract read
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Hao FangInstitute of Fine Arts and Design, Quanzhou Normal University, Quanzhou, China.
Bowen LiInstitute of Fine Arts and Design, Quanzhou Normal University, Quanzhou, China.
Ziwen ZhouSchool of Fine Arts, Jiangxi Science & Technology Normal University, Nanchang, China.
Mu LiInstitute of Fine Arts and Design, Quanzhou Normal University, Quanzhou, China.
Huasheng LaiProduct Design Department, School of the Arts, Universiti Sains Malaysia, Penang, Malaysia.
Yihao ZhengFuzhou University Xiamen College of Art and Design, Xiamen, China.
Bin HuSchool of Design and Art, Southwest Forestry University, Kunming, China.
Weichang ChenInstitute of Fine Arts and Design, Quanzhou Normal University, Quanzhou, China.ORCID https://orcid.org/0009-0001-4654-9504

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

ArtCognitionEstheticsAdultChinaEmotionsFemaleGenerative Artificial IntelligenceHumansMaleSurveys and QuestionnairesYoung Adult

Identifiers

PMID41818281
PMCPMC12981516

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.