AI AND DIGITAL PAINTING: REIMAGINING HUMAN–MACHINE COLLABORATION

Authors

  • P. Thara Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology, Vinayaka Mission’s Research Foundation (DU), Tamil Nadu, India
  • Dr. Roopa Traisa Associate Professor, Department of Management Studies, JAIN (Deemed-to-be University), Bengaluru, Karnataka, India
  • Swati Chaudhary Assistant Professor,School of Business Management, Noida international University 203201
  • Priya Modi Assistant Professor, Department of Development Studies, Vivekananda Global University, Jaipur, India
  • Vivek Saraswat Centre of Research Impact and Outcome, Chitkara University, Rajpura- 140417, Punjab, India
  • Hitesh Kalra Chitkara Centre for Research and Development, Chitkara University, Himachal Pradesh, Solan, 174103, India
  • Chandrashekhar Ramesh Ramtirthkar Department of Mechanical Engineering, Vishwakarma Institute of Technology, Pune, Maharashtra, 411037 India

DOI:

https://doi.org/10.29121/shodhkosh.v6.i3s.2025.6753

Keywords:

Digital Painting, Human–Machine Collaboration, Generative Models, Diffusion Models, Algorithmic Creativity, Hybrid Intelligence, Posthuman Aesthetics, Ethical Authorship

Abstract [English]

In this paper, I examine the digital transformation the artificial intelligence (AI) has on digital painting by examining how the relationship between humans and machines is changing as a new artistic creation paradigm. It follows the historical heritage of machine-assisted art, studies the underlying ground of technology of GANs, diffusion models, and reinforcement learning, and evaluates how these systems grapple with artists, in real-time feedback and adaptive learning. The paper presents case studies of such large platforms like DALL3, Midjourney, Runway ML, and Adobe Firefly to show that AI is not an independent agent, but a cognitive partner, who expands the imagination of humans and redefines authorship and aesthetic agency. The move of aesthetics toward posthuman, collective authorship, and the need to be transparent in the use and attribution of data is revealed in philosophical and ethical analyses. The conclusion of the paper will be a futuristic projection of a hybrid creative future in which human emotion and artificial cognition will come together to create a generative, ethical and interactive art ecosystem.

References

Al-Khazraji, L. R., Abbas, A. R., Jamil, A. S., and Hussain, A. J. (2023). A Hybrid Artistic Model Using DeepDream Model and Multiple Convolutional Neural-Network Architectures. IEEE Access, 11, 101443–101459. https://doi.org/10.1109/ACCESS.2023.3309419 DOI: https://doi.org/10.1109/ACCESS.2023.3309419

Cheng, M. (2022). The Creativity of Artificial Intelligence in Art. Proceedings, 81, 110. https://doi.org/10.3390/proceedings2022081110 DOI: https://doi.org/10.3390/proceedings2022081110

Déguernel, K., and Sturm, B. L. T. (2024). Bias in Favour or Against Computational Creativity: A Survey and Reflection on the Importance of Socio-Cultural Context in its Evaluation. KTH DivA Portal.

Elgammal, A., Liu, B., Elhoseiny, M., and Mazzone, M. (2017). CAN: Creative Adversarial Networks, Generating “art” by Learning About Styles and Deviating from Style Norms. arXiv.

Gatys, L. A., Ecker, A. S., and Bethge, M. A. (2015). A Neural Algorithm of Artistic Style. arXiv. https://doi.org/10.1167/16.12.326 DOI: https://doi.org/10.1167/16.12.326

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2025). Generative Adversarial Networks. Communications of the ACM, 63, 139–144. https://doi.org/10.1145/3422622 DOI: https://doi.org/10.1145/3422622

Guo, D. H., Chen, H. X., Wu, R. L., and Wang, Y. G. (2023). AIGC Challenges and Opportunities Related to Public Safety: A Case Study of ChatGPT. Journal of Safety Science and Resilience, 4, 329–339. https://doi.org/10.1016/j.jnlssr.2023.08.001 DOI: https://doi.org/10.1016/j.jnlssr.2023.08.001

Guo, Y., Lin, S., Acar, S., Jin, S., Xu, X., Feng, Y., and Zeng, Y. (2022). Divergent Thinking and Evaluative Skill: A Meta-Analysis. Journal of Creative Behavior, 56, 432–448. https://doi.org/10.1002/jocb.539 DOI: https://doi.org/10.1002/jocb.539

Jiang, Q. L., Zhang, Y. Z., Wei, W., and Gu, C. (2024). Evaluating Technological and Instructional Factors Influencing the Acceptance of Aigc-Assisted Design Courses. Computers and Education: Artificial Intelligence, 7, 100287. https://doi.org/10.1016/j.caeai.2024.100287 DOI: https://doi.org/10.1016/j.caeai.2024.100287

Lee, U., et al. (2024). LLaVA-docent: Instruction Tuning with Multimodal Large-Language Model to Support art Appreciation Education. Computers and Education: Artificial Intelligence, 7, 100297. https://doi.org/10.1016/j.caeai.2024.100297 DOI: https://doi.org/10.1016/j.caeai.2024.100297

Li, G., Chu, R., and Tang, T. (2024). Creativity Self-Assessments in Design Education: A Systematic Review. Thinking Skills and Creativity, 52, 101494. https://doi.org/10.1016/j.tsc.2024.101494 DOI: https://doi.org/10.1016/j.tsc.2024.101494

Lou, Y. Q. (2023). Human Creativity in the AIGC era. Journal of Design Economics and Innovation, 9, 541–552. https://doi.org/10.1016/j.sheji.2024.02.002 DOI: https://doi.org/10.1016/j.sheji.2024.02.002

McCormack, J., Gifford, T., and Hutchings, P. (2019). Autonomy, Authenticity, Authorship and Intention in Computer-Generated art. In Proceedings of the EvoMUSART: International Conference on Computational Intelligence in Music, Sound, Art and Design (EvoStar) (pp. 35–50). Springer. https://doi.org/10.1007/978-3-030-16667-0_3 DOI: https://doi.org/10.1007/978-3-030-16667-0_3

Oksanen, A., et al. (2023). Artificial Intelligence in Fine Arts: A Systematic Review of Empirical Research. Computers in Human Behavior: Artificial Humans, 1, 100004. https://doi.org/10.1016/j.chbah.2023.100004 DOI: https://doi.org/10.1016/j.chbah.2023.100004

Tamm, T., Hallikainen, P., and Tim, Y. (2022). Creative Analytics: Towards Data-Inspired Creative Decisions. Information Systems Journal, 32, 729–753. https://doi.org/10.1111/isj.12369 DOI: https://doi.org/10.1111/isj.12369

Tang, Z. C., Wang, D. L., Xia, D., and Li, X. T. (2020). “Artificial Intelligence + Design”: A New Exploration of Teaching Practice of Product Design Courses for Design Majors. Art and Design, 1, 120–123.

Utz, V., and DiPaola, S. (2020). Using an AI creativity System to Explore How Aesthetic Experiences are Processed Along the Brain’s Perceptual Neural Pathways. Cognitive Systems Research, 59, 63–72. https://doi.org/10.1016/j.cogsys.2019.09.012 DOI: https://doi.org/10.1016/j.cogsys.2019.09.012

Wammes, J. D., Roberts, B. R. T., and Fernandes, M. A. (2018). Task Preparation as a Mnemonic: The Benefits of Drawing (and Not Drawing). Psychonomic Bulletin and Review, 25, 2365–2372. https://doi.org/10.3758/s13423-018-1477-y DOI: https://doi.org/10.3758/s13423-018-1477-y

Xie, H., and Zhou, Z. (2024). Finger Versus Pencil: An Eye-Tracking Study of Learning by Drawing on Touchscreens. Journal of Computer Assisted Learning, 40, 49–64. https://doi.org/10.1111/jcal.12863 DOI: https://doi.org/10.1111/jcal.12863

Zhang, Y. C., Li, Z. F., Feng, X. Y., Suo, X. C., and Hu, P. (2023). Visualization Research on the Status Quo of Virtual-Reality Education in China: Knowledge-Graph Analysis Based on Citespace. Modern Information Technology, 7, 135–141.

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Published

2025-12-20

How to Cite

P. Thara, Traisa, R., Chaudhary, S., Modi, P., Saraswat, V., Kalra, H., & Ramtirthkar, C. R. (2025). AI AND DIGITAL PAINTING: REIMAGINING HUMAN–MACHINE COLLABORATION. ShodhKosh: Journal of Visual and Performing Arts, 6(3s), 1–11. https://doi.org/10.29121/shodhkosh.v6.i3s.2025.6753