Evidence map›Paper›PMID 42534477›Full record

ArticleFrontiers in psychology2026

How generative AI shapes designers' cultural heritage revitalization intention: process and content cues in AI-assisted cultural heritage design.

Chang Liu, Ping Cang

Abstract read
In one paragraph

Article in Frontiers in psychology, 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

2 authors.

Chang LiuCollege of Fashion and Design, Donghua University, Shanghai, China.
Ping CangCollege of Fashion and Design, Donghua University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As generative AI becomes increasingly involved in cultural heritage design, designers' evaluations of AI collaboration are shaping cultural revitalization practices. Yet existing research offers limited explanation of how AI characteristics affect designers' willingness to engage in cultural heritage revitalization or how professional experience conditions this process. Based on the stimulus-organism-response (S-O-R) framework, this study develops a dual-path moderated mediation model. It examines how AI process cues and AI-generated content cues influence revitalization intention through psychological ownership and perceived output quality. Using data from 312 Chinese designers with experience in generative AI-assisted cultural heritage design, the study combines PLS-SEM, multi-group analysis, fsQCA, and LDA text analysis. The results show that algorithmic autonomy reduces psychological ownership, whereas interaction transparency and interaction depth enhance it. Cultural authenticity improves perceived output quality, while the effect of visual complexity varies by experience level. Experienced designers rely more on psychological ownership, whereas early-career designers rely more on perceived output quality. fsQCA and LDA further reveal multiple configurations of high revitalization intention and two evaluative orientations in design discourse: ownership-oriented and quality-oriented. This study clarifies the dual-evaluation mechanism of AI-assisted cultural heritage design and offers behavioral evidence for generative AI's role in cultural translation and sustainable dissemination.

Indexed as

cultural heritage revitalizationcultural sustainabilitydesigner experiencefsQCAgenerative artificial intelligencehuman—AI collaborationperceived output qualitypsychological ownership

Identifiers

PMID42534477
PMCPMC13420728

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.