Evidence map›Paper›PMID 40328802›Full record

ArticleScientific reports2025

The use of deep learning and artificial intelligence-based digital technologies in art education.

Yali Liu, Can Zhu

Abstract read
In one paragraph

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 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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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.

Yali LiuSchool of Art, Jiujiang University, Jiujiang, 332005, China.
Can ZhuCollege of Media and International Culture, Zhejiang University, Hangzhou, 310058, China. canzhu@zju.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In order to explore the application of deep learning (DL) and artificial intelligence (AI) technologies in art education, this work proposes and optimizes an innovative art creation system-Creative Intelligence Cloud (CIC). The system combines a deep generative adversarial network and convolutional neural network, aiming to enhance the automation level, consistency of artistic styles, and creation efficiency in art creation. This work first analyzes existing art creation methods. It points out the shortcomings of traditional systems in terms of image quality, style transfer, and computational performance, especially the application limitations in real teaching scenarios. Therefore, this work designs an art creation model optimized by DL and validates and evaluates it through extensive experiments. The experimental results show that CIC outperforms existing mainstream models in multiple dimensions, including image quality, computational performance, user experience, and style creation. For example, in image quality evaluation, CIC achieves high scores in clarity (0.89), detail performance (0.85), style consistency (0.87), and color accuracy (0.91). In terms of computational performance and resource consumption, CIC shows its superiority, with a training time of only 1500 s, memory consumption of 4.9GB, and a Graphics Processing Unit resource usage rate of 70%. Compared to models such as the Visual Perception Generative Adversarial Network and Artistic Recognition and Transfer Style Convolutional Neural Network, CIC is more efficient and consumes fewer resources. Furthermore, CIC's scores in user experience and style transfer capability are significantly higher than those of other models, providing smoother and more creatively rich art creation tools for art education. Therefore, this work offers new ideas and methods for the application of DL and AI technologies in art creation and art education, and promotes the practical use of AI in art education. The work has certain academic contributions and practical value.

Indexed as

Art creationArt educationArtificial intelligenceDeep learningStyle transfer

Identifiers

PMID40328802
PMCPMC12056105

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.