Keywords
artificial intelligence
multimedia design
algorithm
project activity
digital workspace
How to Cite
Abstract
The digital workspace is one of the main tools of design activity in the creative industries. In particular, in multimedia design, the traditional means of performing design tasks, among other possible ones, is determined by specialized digital software for editing multimedia content. Examples include software for editing vector and raster graphics, video editing, and working with digital three-dimensional graphics. It is advisable to form a functionally oriented typology of artificial intelligence models and more applied schemes for integrating generative systems into the design process. The purpose of the study is to develop a comprehensive approach to integrating artificial intelligence into design activity for three-dimensional animation design through the formation of a generalized scheme for applying functionally defined types of artificial intelligence models at the main stages of the production process. The study identifies the main stages of project activities in creating three-dimensional animation, develops a typology of user-friendly artificial intelligence systems in the context of application in design project activities, analyzes the possibilities of applying certain types of artificial intelligence systems at each stage of project activities, and summarizes the results of the study in a structural diagram of the complex integration of artificial intelligence into the design of three-dimensional animation. The study substantiates that the development of algorithmic data processing systems – “artificial intelligence” systems – is a source of replenishment of the tools of the digital work environment, in particular, both in the form of separate user interfaces for controlling the work of artificial intelligence, and in the form of integration of algorithmic systems into traditional software.
References
1. Bobryshev, Ye. (2025). Zastosuvannia shtuchnoho intelektu v 3D-modeliuvanni na bazi Blender [The use of artificial intelligence in 3D modeling based on Blender]. Kiberbezpeka: Osvita, Nauka, Tekhnika, 3(31), 741–751. https://doi.org/10.28925/2663-4023.2025.31.1071 (in Ukrainian)
2. Chen, Y., Wang, Y., Yu, T., & Pan, Y. (2024). The effect of AI on animation production efficiency: An empirical investigation through the network data envelopment analysis. Electronics, 13(24), 5001. https://doi.org/10.3390/electronics13245001
3. Dellaert, F., & Yen-Chen, L. (2021). Neural volume rendering: NeRF and beyond (Version 2). arXiv. https://doi.org/10.48550/ARXIV.2101.05204
4. Elgammal, A., Liu, B., Elhoseiny, M., & Mazzone, M. (2017). CAN: Creative adversarial networks, generating “art” by learning about styles and deviating from style norms (Version 1). arXiv. https://doi.org/10.48550/ARXIV.1706.07068
5. Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial networks (Version 1). arXiv. https://doi.org/10.48550/ARXIV.1406.2661
6. Holden, D., Komura, T., & Saito, J. (2017). Phase-functioned neural networks for character control. ACM Transactions on Graphics, 36(4), 1–13. https://doi.org/10.1145/3072959.3073663
7. Huang, Y.-S., & Yang, Y.-H. (2020). Pop music transformer: Beat-based modeling and generation of expressive pop piano compositions (Version 3). arXiv. https://doi.org/10.48550/ARXIV.2002.00212
8. Jin, J., Yang, M., Hu, H., Guo, X., Luo, J., & Liu, Y. (2025). Empowering design innovation using AI-generated content. Journal of Engineering Design, 36(1), 1–18. https://doi.org/10.1080/09544828.2024.2401751
9. Kardianawati, A., Laksana, D. A. W., Yulianto, L., Banjaransari, T., Widjajanto, B., & Syarif, A. M. (2025). AI-driven animation systems for efficient and scalable production: Prototype study and user evaluation. https://ritecs.org/journal/index.php/IRCS/article/view/5
10. Kerlow, I. V. (2004). The art of 3D computer animation and effects (3rd ed.). Wiley.
11. McCormack, J., Hutchings, P., Gifford, T., Yee-King, M., Llano, M. T., & D’Inverno, M. (2020). Design considerations for real-time collaboration with creative artificial intelligence. Organised Sound, 25(1), 41–52. https://doi.org/10.1017/s1355771819000451
12. Park, J. J., Florence, P., Straub, J., Newcombe, R., & Lovegrove, S. (2019). DeepSDF: Learning continuous signed distance functions for shape representation (Version 1). arXiv. https://doi.org/10.48550/ARXIV.1901.05103
13. Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M. (2022). Hierarchical text-conditional image generation with CLIP latents (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2204.06125
14. Shaura, A. (2025). Intehratsiia shtuchnoho intelektu v protsesy stvorennia multymediinoho kontentu: Avtomatyzatsiia dyzainu ta heneratsiia kontentu [Integration of artificial intelligence into multimedia content creation processes: Design automation and content generation]. In Aktualni problemy suchasnoho dyzainu: Materialy VII Mizhnarodnoi naukovo-praktychnoi konferentsii (Vol. 2, pp. 121–124). KNUTD. https://er.chdtu.edu.ua/bitstream/ChSTU/6949/3/%D0%A2%D0%BE%D0%BC%202_2025%20%281%29.pdf (in Ukrainian)
15. Wahyudin, W., Nugroho, N., & Munawir, A. (2025). Designing AI-based 3D animation to inspire young entrepreneurs at Bina Bangsa. Brilliance: Research of Artificial Intelligence, 5(2), 636–645. https://doi.org/10.47709/brilliance.v5i2.5227
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright (c) 2026 Oleksii Proskuryakov, Daniil Ditkovskyi