ARTIFICIAL INTELLIGENCE AS A CREATIVE COLLABORATOR IN CONTEMPORARY VISUAL AND PERFORMING ARTS
DOI:
https://doi.org/10.29121/shodhkosh.v7.i3s.2026.7310Keywords:
Artificial Intelligence, Computational Creativity, Generative Art, Human–AI Collaboration, Diffusion Models, Digital Visual Arts, Creative AI Systems, Interactive Art, Generative DesignAbstract [English]
The use of artificial intelligence in modern art is gaining more and more popularity; machines are able to produce visual data and be involved in creative processes. This paper examines how artificial intelligence is used as a creative partner in contemporary visual arts, specifically how generative models can support artists in the design of the concept and exploration of the artistic process. To encourage human-AI co-creation, a collaborative model that combines data preparation, generative AI models, interactive user interfaces and rendering modules is suggested. A dataset of 10,000 digital artworks that represent a variety of artistic styles was used to evaluate the experimentation. A generative model that runs on diffusion has been used to generate visual compositions in response to artist prompts. Findings show that AI-guided creative processes are highly time-saving and enhance the production time of artwork, as well as, stylistic and conceptual range. The comparative analysis shows that the rating of visual quality and artist satisfaction improves in case AI tools are introduced to the creative process. The results also demonstrate the possibility of artificial intelligence as a creative companion that can make humans more creative instead of stealing artistic status. The suggested framework helps in the evolving body of computational creativity because it will show how intelligent systems can supplement artistic processes and contribute to interactive creation of visual arts. The research offers information on how generative AI can be integrated into artistic space and outlines the future research prospects of immersive and interactive systems based on AI using creativity.
References
Ali, S., DiPaola, D., Lee, I., Hong, J., and Breazeal, C. (2021). Exploring Generative Models with Middle School Students. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems ( 1–13). https://doi.org/10.1145/3411764.3445226 DOI: https://doi.org/10.1145/3411764.3445226
Ali, S., DiPaola, D., Lee, I., Sindato, V., Kim, G., Blumofe, R., and Breazeal, C. (2021). Children as Creators, Thinkers and Citizens in an AI-Driven Future. Computers and Education: Artificial Intelligence, 2, Article 100040. https://doi.org/10.1016/j.caeai.2021.100040 DOI: https://doi.org/10.1016/j.caeai.2021.100040
Ali, S., Park, H. W., and Breazeal, C. (2020). Can cHildren Emulate a Robotic Non-Player Character’s Figural Creativity? In Proceedings of the Annual Symposium on Computer-Human Interaction in Play (CHI PLAY ’20) ( 499–509). https://doi.org/10.1145/3410404.3414251 DOI: https://doi.org/10.1145/3410404.3414251
Ambrosio, C. (2019). Unsettling Robots and the Future of Art. Science, 365(6448), 38–39. https://doi.org/10.1126/science.aay1956 DOI: https://doi.org/10.1126/science.aay1956
Anadol, R. (2022). Space in the Mind of a Machine: Immersive Narratives. Architectural Design, 92(3), 28–37. https://doi.org/10.1002/ad.2810 DOI: https://doi.org/10.1002/ad.2810
Anadol, R., and Kivrak, P. (2023). Machines That Dream: How AI-Human Collaborations in Art Deepen Audience Engagement. Management and Business Review, 3, 101–107. https://doi.org/10.1177/2694105820230301018 DOI: https://doi.org/10.1177/2694105820230301018
Avlonitou, Z. (2018). Collector and Collection, an Indivisible Unity: The Case of the Collector and Collection of Giorgos Kostakis (Doctoral Dissertation, University of Ioannina).
Bellaiche, L., et al. (2023). Humans Versus AI: Whether and Why we Prefer Human-Created Compared to AI-Created Artwork. Cognitive Research: Principles and Implications, 8, Article 42. https://doi.org/10.1186/s41235-023-00499-6 DOI: https://doi.org/10.1186/s41235-023-00499-6
Benjamin, W. (2008). The Work of Art in the Age of Its Technological Reproducibility, and Other Writings on Media (M. W. Jennings, B. Doherty, and T. Y. Levin, Eds.). Belknap Press. https://doi.org/10.2307/j.ctv1nzfgns DOI: https://doi.org/10.2307/j.ctv1nzfgns
Bhullar, R. (2024). Creative Artificial Intelligence: Exploring the Qualities of Popular AI Art Tools to Determine Effectiveness. NHSJS.
Black, G. (2018). Meeting the Audience Challenge in the “Age of Participation.” Museum Management and Curatorship, 33(4), 302–319. https://doi.org/10.1080/09647775.2018.1469097 DOI: https://doi.org/10.1080/09647775.2018.1469097
Blanco, A. D., et al. (2024, November 11). Exploring the Neural Impact of AI-Generated Art at MoMA: An EEG Study on Refik Anadol’s Unsupervised. OSF Preprints.
Boden, M. A. (2004). The Creative Mind: Myths and Mechanisms (2nd ed.). Routledge. https://doi.org/10.4324/9780203508527 DOI: https://doi.org/10.4324/9780203508527
Bowen, J. P., and Giannini, T. (2019). The Digital Future for Museums. In T. Giannini and J. P. Bowen (Eds.), Museums and Digital Culture. Springer. https://doi.org/10.1007/978-3-319-97457-6_28 DOI: https://doi.org/10.1007/978-3-319-97457-6
Cao, Y., et al. (2023). A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT. arXiv.
Colton, S., and Wiggins, G. A. (2012). Computational Creativity: The Final Frontier? In Proceedings of the 20th European Conference on Artificial Intelligence (ECAI 2012) ( 21–26).
Jadhav, K. D. (2027). Integrating IoT, Sensors, and Machine Learning for Enhancing Crop Yield and Irrigation Efficiency Systems. In R. R. Janghel, R. Doriya, J. Lachure, and Y. K. Rathore (Eds.), Sustainable Agriculture Production Using Blockchain Technology. https://doi.org/10.1002/9781394248711.ch11 DOI: https://doi.org/10.1002/9781394248711.ch11
Karwande, V. S., Pawar, U. B., and Pattnaik, O. (2024). Leveraging Speech-Driven Patterns Multimodal Machine Learning Framework for Accurate Early-Stage Parkinson’s Disease Prediction: A Survey. In Proceedings of the 2nd International Conference on Advanced Computing and Communication Technologies (ICACCTech 2024) ( 525–532). https://doi.org/10.1109/ICACCTech65084.2024.00091 DOI: https://doi.org/10.1109/ICACCTech65084.2024.00091
Patil, R. V., et al. (2025). Decentralized Autonomous Organizations as Emerging Economic Entities in Accounting and Governance Frameworks. International Journal of Accounting and Economics Studies, 12(4), 166–177. https://doi.org/10.14419/1sy2j677 DOI: https://doi.org/10.14419/1sy2j677
Phurisikul, P., and Phuakkong, C. (2025). The Elderly Welfare Management of Local Administrative Government Organizations in Thailand. International Journal of Research and Development in Management Research, 14(1), 21–28. https://doi.org/10.65521/ijrdmr.v14i1.102 DOI: https://doi.org/10.65521/ijrdmr.v14i1.102
Rawandale, U. S., and Kolte, M. T. (2021). Design, Development and Analysis of Variable Bandwidth Filter Bank for Enhancing the Performance of Hearing Aid System. International Journal of Intelligent Engineering Systems, 14(6), 391–401. https://doi.org/10.22266/ijies2021.1231.35 DOI: https://doi.org/10.22266/ijies2021.1231.35
Suri, S., Lakshman, K., Goyal, E., Goyal, G., Sood, G., Mirajkar, G. S., and Anerao, P. (2025). Emotion Modeling in Sculpture Design Using Neural Networks. ShodhKosh: Journal of Visual and Performing Arts, 3(3s), 31–40. https://doi.org/10.29121/shodhkosh.v6.i3s.2025.6756 DOI: https://doi.org/10.29121/shodhkosh.v6.i3s.2025.6756
Vasanthan, R., Balaji, P., and Dzuvichu, K. (2019). A Study on Kinaesthetic Style Preferences and Second Language Learning Requirements at the Primary Level. Journal of Advanced Research in Dynamical and Control Systems, 11(11 Special Issue), 861–869. https://doi.org/10.5373/JARDCS/V11SP11/20193108 DOI: https://doi.org/10.5373/JARDCS/V11SP11/20193108
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Copyright (c) 2026 Pushpalatha P, Pushpa Nagini Sripada, Vinitha M, Muninathan N, Subbulakshmi Packirisamy, Yu Yang
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