ArticleScientific reports2025
Research on user satisfaction with AIGC assisted museum scenario design.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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.
Who cites it
1 citing paper in PubMed.
- How generative AI shapes designers' cultural heritage revitalization intention: process and content cues in AI-assisted cultural heritage design.Frontiers in psychology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
In the context of digital transformation, artificial intelligence generated content (AIGC) technology provides an innovative path for museum smart scene design, but existing research lacks a user-centered systematic framework. This study uses questionnaire surveys and structural equation models (SEM) to explore the mechanism of AIGC technology adaptability, user demand fit, scene design innovation, technology acceptance and user satisfaction. The results show that user demand fit has the strongest direct impact on satisfaction, highlighting the "user-centered" design core; AIGC technology adaptability improves satisfaction through direct and indirect paths, verifying the mediating effect of the technology acceptance model (TAM); scene design innovation needs to transform value through technology acceptance to affect user satisfaction. The study constructs a closed-loop model of "demand drive-technology adaptation-scene innovation-acceptance conversion", and proposes a design strategy based on cognitive load balance, which provides a theoretical basis and practical path for museums to use AIGC technology to improve user experience, and promotes the paradigm shift of museums from "object-centered" to "people-centered".
Identifiers
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Registered trials
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.