ART AND ARTIFICIAL INTELLIGENCE IN SHAPING CONTEMPORARY VISUAL CULTURE
DOI:
https://doi.org/10.29121/shodhkosh.v7.i3s.2026.7326Keywords:
Artificial Intelligence, Digital Art, Visual Culture, Generative Art, Machine Learning, Computational Creativity, Media ArtAbstract [English]
In the digital age, more than ever before, Artificial Intelligence (AI) has quickly reshaped the practices of art, visual communication, and cultural production. The combination of machine learning, computer vision, generative models and neural networks with artistic workflows has established new paradigms of artistic creation and curation and interaction among audiences. AI-generated artworks, algorithmic aesthetic and interactive media installations are transforming the modern visual culture in a way that defines the concept of creativity, authorship as well as cultural representation. Artists continue to work with smart systems in order to create new visual representations, immersive environments, and algorithmic art works. Simultaneously, AI poses complicated challenges concerning intellectual property, the authenticity of artworks, dataset bias, and the socio-economic consequences of the creative experts. The recent breakthroughs in generative adversarial networks (GANs) and diffusion models can enable machines to create images, paintings, and visual compositions that are aesthetically of a high quality and go against the conventional definition of artistic production. The AI-powered systems also can provide dynamic visual experiences, in which artworks react to the audience behavior and environmental inputs, which increases the participation and engagement. Nevertheless, the increasing role of algorithmic creativity also demands new ethical principles and regulatory models of responsible and transparent artistic activity. In this paper, the author discusses the changing connection between art and artificial intelligence and its role in the formation of modern visual culture. It examines the previous literature on AI-driven artistic practices, considers the technological processes that drive AI-authored art, and compares the current digital art models. Moreover, the paper suggests a theoretical framework of AI-based visual culture which combines the creative aspect of artistry and the smart implementation of computational mechanisms. The paper indicates the revolutionary nature of AI in broadening the creative opportunities of art and the necessity of ethical regulation and collaborative creativity between humans and machines in further cultural output.
References
Ali, S., DiPaola, D., Lee, I., Hong, J., and Breazeal, C. (2021). Exploring Generative Models With Middle School Students. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI ’21) (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, 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, 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, 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, Greece).
Babu, M. R. N., Tungoe, C., Vasanthan, R., Pimple, J., Khandare, K. S., and Kalyani, L. K. (2025). AI for Accessibility in Digital Media Education. ShodhKosh, 6(2S), Article 67. https://doi.org/10.29121/shodhkosh.v6.i2s.2025.6704 DOI: https://doi.org/10.29121/shodhkosh.v6.i2s.2025.6704
Bellaiche, L., Shahi, R., Turpin, M. H., Ragnhildstveit, A., Sprockett, S., Barr, N., Christensen, A., and Seli, P. (2023). Humans Versus AI: Whether and Why We Prefer Human-Created Compared to AI-Created Artwork. Cognitive Research: Principles and Implications, 8, 42. https://doi.org/10.1186/s41235-023-00499-6 DOI: https://doi.org/10.1186/s41235-023-00499-6
Bhullar, R. (2024). Creative Artificial Intelligence: Exploring the Qualities of Popular AI Art Tools to Determine Effectiveness.
Black, G. (2018). Meeting the Audience Challenge in the ‘Age of Participation’. Museum Management and Curatorship, 33, 302–319. https://doi.org/10.1080/09647775.2018.1469097 DOI: https://doi.org/10.1080/09647775.2018.1469097
Blanco, A. D., Kroupi, E., Soria-Frisch, A., Gazzaley, A., Anadol, R., Maiques, A., and Ruffini, G. (2024). 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. 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
Chatterjee, A. (2022). Art in an Age of Artificial Intelligence. Annals of the New York Academy of Sciences.
Desai, V. P., Shinde, P. P., Mirajkar, G. S., Pillai, P. K., and Oza, K. S. (2026). Assessment of Adulteration in Edible Oil Using Machine Learning. IAENG International Journal of Computer Science, 53(1), 456–464.
Ekatpure, M. J. N., Asabe, T., Gaikwad, R., and Nagare, N. (2025). Exploration of EduQuest – An AI-Powered System for Question Generation and Test Automation. International Journal of Research in Applied Engineering and Technology, 14(2), 42–45.
Garg, N., Jadhav, K. D., Solanki, S., Dabral, K., Padghan, N. P., and Gode, S. A. (2025). Public Private Partnerships for Sustainable Urban WASH Infrastructure Development. Waterlines, 43(2), 113–130. https://doi.org/10.3362/waterlines.v43i2.528 DOI: https://doi.org/10.3362/waterlines.v43i2.528
Hazarika, I., Cai, Z., Ghunnar, P., and Cheng, Y. (2025). Cross-Cultural Advertising for Promoting Gender Equality and Diversity (SDG 5 and 10). Lex Localis: Journal of Local Self-Government, 23(S6), 7520. https://doi.org/10.52152/tmw9y075 DOI: https://doi.org/10.52152/tmw9y075
Ibrahim, A. (2023). Impact of Artificial Intelligence in Visual Art Performance. Research Journal in Advanced Humanities. DOI: https://doi.org/10.58256/rjah.v4i1.1214
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
Núñez-Cacho, P., Pastor, J. M., Gil-Ruiz, M. A., et al. (2024). Exploring the Transformative Power of AI in Art. Heliyon.
Rawandale, U. S., and Kolte, M. T. (2019). Study of Audiogram for Speech Processing in Hearing Aid System. In 2019 IEEE Pune Section International Conference (PuneCon) (1–4). https://doi.org/10.1109/PuneCon46936.2019.9105706 DOI: https://doi.org/10.1109/PuneCon46936.2019.9105706
Verma, D. A., Kale, A., Agarwal, K., Chandratreya, A., Rani, A., and Ajani, S. N. (2026). Digital Preservation and Intelligent Innovation in Traditional and Modern Arts. ShodhKosh Journal of Visual and Performing Arts, 7(1s), 1–3. https://doi.org/10.29121/shodhkosh.v7.i1s.2026.7169 DOI: https://doi.org/10.29121/shodhkosh.v7.i1s.2026.7169
Vijayakumar, M., Muniyandy, E., Mirajkar, G., et al. (2026). Intrusion Detection and Localization Using Deep Learning Approaches in VANET Environments. SN Computer Science, 7, 234. https://doi.org/10.1007/s42979-026-04769-0 DOI: https://doi.org/10.1007/s42979-026-04769-0
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Copyright (c) 2026 Gayathri M, Harshini R, Gayathri B, Ashika Fathima B, Prathiba S, Dr. Priyadharshini S
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