The use of deep learning and artificial intelligence-based digital technologies in art education
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 convo...
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-00892-9 |
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| author | Yali Liu Can Zhu |
| author_facet | Yali Liu Can Zhu |
| author_sort | Yali Liu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d0bbc799e8fb45adbe4b8a1bf9c3b890 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-d0bbc799e8fb45adbe4b8a1bf9c3b8902025-08-20T02:15:00ZengNature PortfolioScientific Reports2045-23222025-05-0115111410.1038/s41598-025-00892-9The use of deep learning and artificial intelligence-based digital technologies in art educationYali Liu0Can Zhu1School of Art, Jiujiang UniversityCollege of Media and International Culture, Zhejiang UniversityAbstract 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.https://doi.org/10.1038/s41598-025-00892-9Deep learningArtificial intelligenceArt creationArt educationStyle transfer |
| spellingShingle | Yali Liu Can Zhu The use of deep learning and artificial intelligence-based digital technologies in art education Scientific Reports Deep learning Artificial intelligence Art creation Art education Style transfer |
| title | The use of deep learning and artificial intelligence-based digital technologies in art education |
| title_full | The use of deep learning and artificial intelligence-based digital technologies in art education |
| title_fullStr | The use of deep learning and artificial intelligence-based digital technologies in art education |
| title_full_unstemmed | The use of deep learning and artificial intelligence-based digital technologies in art education |
| title_short | The use of deep learning and artificial intelligence-based digital technologies in art education |
| title_sort | use of deep learning and artificial intelligence based digital technologies in art education |
| topic | Deep learning Artificial intelligence Art creation Art education Style transfer |
| url | https://doi.org/10.1038/s41598-025-00892-9 |
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