NEURAL STYLE TRANSFER IN ART EDUCATION A CASE STUDY OF DIGITAL CREATIVITY
DOI:
https://doi.org/10.29121/shodhkosh.v6.i2s.2025.6700Keywords:
Neural Style Transfer, Art Education, Digital Creativity, Artificial Intelligence In Art, Computational Creativity, Digital PedagogyAbstract [English]
The paper is a pedagogical investigation of the Neural Style Transfer (NST) as a means of promoting digital creativity in art education. With the growing influence of artificial intelligence in creative practice, it is up to educators to identify ways of using emerging technologies to facilitate creative expression and not to automate it. The paper presents a case study on how NST may be applied in a formal education setting to enhance students’ knowledge of visual styles, increase their range of creative decisions, and promote experiments in digital media. The study was done using a sample group of undergraduate art students that had gone through a sequence of workshops that were aimed at creating stylized images using the NST algorithms. The qualitative information such as student feedback, notes, and art analysis were gathered to analyze the transformation of creative processes and attitudes toward AI-assisted artmaking. Results have shown that NST has offered a point of access to the world of computational creativity, allowing students to rebuild their own work based on the prism of various artistic styles. The students were found to have been more engaged and more willing to take aesthetic risks, and to have been digital literate. Nonetheless, other issues (i.e. excessive dependence on automated results and lack of knowledge about algorithmic decision-making) are also noted in the study. Comprehensively, the study implies that a well-considered application of NST can become an effective pedagogical means that can enhance the creative exploration and help to cultivate hybrid digital-artistic qualities during art education.
References
Ashiq, F., Asif, M., Ahmad, M. B., Zafar, S., Masood, K., Mahmood, T., Mahmood, M. T., and Lee, I. H. (2022). CNN-Based Object Recognition and Tracking System to Assist Visually Impaired People. IEEE Access, 10, 14819–14834. https://doi.org/10.1109/ACCESS.2022.3159150 DOI: https://doi.org/10.1109/ACCESS.2022.3148036
Benitez-Garcia, G., Takahashi, H., and Yanai, K. (2022). Material Translation Based on Neural Style Transfer with Ideal Style Image Retrieval. Sensors, 22, 7317. https://doi.org/10.3390/s22197317 DOI: https://doi.org/10.3390/s22197317
Chauhan, S., Manmohan, S., and Kumar, A. (2021). Data Science and Data Analytics: Artificial Intelligence and Machine Learning Integrated Based Approach. In Data Science and Data Analytics: Opportunities and Challenges. Chapman and Hall/CRC. DOI: https://doi.org/10.1201/9781003111290-1-2
Dehghani, M., Gritsenko, A., Arnab, A., Minderer, M., and Tay, Y. (2022). SCENIC: A JAX Library for Computer Vision Research and Beyond. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 21393–21398. DOI: https://doi.org/10.1109/CVPR52688.2022.02070
Leong, W. Y. (2025). AI-Generated Artwork as a Modern Interpretation of Historical Paintings. International Journal of Social Sciences and Artistic Innovation, 5, 15–19.
Leong, W. Y. (2025). Machine Learning in Evolving Art Styles: A Study of Algorithmic Creativity. Engineering Proceedings, 92, 45. https://doi.org/10.3390/engproc2025092045 DOI: https://doi.org/10.3390/engproc2025092045
Leong, W. Y. (2025). AI-Powered Color Restoration of Faded Historical Painting. In Proceedings of the 10th International Conference on Digital Arts, Media and Technology (DAMT) and 8th ECTI NCON, Nan, Thailand, 623–627. DOI: https://doi.org/10.1109/ECTIDAMTNCON64748.2025.10961986
Li, X., Cao, H., Zhang, Z., Hu, J., Jin, Y., and Zhao, Z. (2025). Artistic Neural Style Transfer Algorithms with Activation Smoothing. In Proceedings of the 2nd International Conference on Informatics Education and Computer Technology Applications (IECA ’25) ACM, 1–6. https://doi.org/10.1145/3732801.3732802 DOI: https://doi.org/10.1145/3732801.3732802
Longo, U. G., Salvatore, S. D., Sassi, M., Carnevale, A., Luca, G. D., and Denaro, V. (2022). Motion Tracking Algorithms Based on Wearable Inertial Sensor: A focus on Shoulder. Electronics, 11(11), 1741. https://doi.org/10.3390/electronics11111741 DOI: https://doi.org/10.3390/electronics11111741
Lyu, Y., Lin, C.-L., Lin, P.-H., and Lin, R. (2021). The Cognition of Audience to Artistic Style Transfer. Applied Sciences, 11, 3290. https://doi.org/10.3390/app11073290 DOI: https://doi.org/10.3390/app11073290
Psychogyios, K., Ilias, L., and Askounis, D. (2022). Comparison of Missing Data Imputation Methods using the Framingham Heart Study Dataset. In Proceedings of the IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), 1–5. DOI: https://doi.org/10.1109/BHI56158.2022.9926882
Psychogyios, K., Leligou, H. C., Melissari, F., Bourou, S., Anastasakis, Z., and Zahariadis, T. (2023). SAMStyler: Enhancing Visual Creativity with Neural Style Transfer and Segment Anything Model (SAM). IEEE Access, 11, 100256–100267. https://doi.org/10.1109/ACCESS.2023.3315235 DOI: https://doi.org/10.1109/ACCESS.2023.3315235
Salcudean, S. E., Moradi, H., Black, D. G., and Navab, N. (2022). Robot-Assisted Medical Imaging: A Review. Proceedings of the IEEE, 110(7), 951–967. https://doi.org/10.1109/JPROC.2022.3169739 DOI: https://doi.org/10.1109/JPROC.2022.3162840
Yi, X., Zhou, Y., Habermann, M., Shimada, S., Golyanik, V., Theobalt, C., and Xu, F. (2022). Physical Inertial Poser (PIP): Physics-aware Real-Time Human Motion Tracking from Sparse Inertial Sensors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 13167–13178. DOI: https://doi.org/10.1109/CVPR52688.2022.01282
Zhang, S., Qi, Y., and Wu, J. (2025). Applying Deep Learning for Style Transfer in Digital Art: Enhancing Creative Expression Through Neural Networks. Scientific Reports, 15, 11744. https://doi.org/10.1038/s41598-025-95819-9 DOI: https://doi.org/10.1038/s41598-025-95819-9
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Dr. Jyoti Saini, Mr. Bhaskar Mitra, Vibhor Mahajan, Vibhor Mahajan, Sourav Rampal
This work is licensed under a Creative Commons Attribution 4.0 International License.
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
