HUMAN–AI COLLABORATION IN ABSTRACT ART CREATION

Authors

  • Dr. Premalatha. P Department of Management and Science, Mysore University, India
  • Suvarna Milind Patil Department of Engineering, Science and Humanities, Vishwakarma Institute of Technology, Pune, Maharashtra, 411037, India
  • Nitesh Kumar Kushwaha Assistant Professor, School of Fine Arts and Design, Noida International University, Noida, Uttar Pradesh, India
  • Rohit Jaiswal Assistant Professor, School of Management and School of Advertising, PR and Events, AAFT University, Raipur, Chhattisgarh-492001, India
  • Rajashri C K Assistant Professor, Meenakshi College of Arts and Science, Meenakshi Academy of Higher Education and Research, Chennai, Tamil Nadu, 600106, India
  • Kalyani P. Karule Assistant Professor, Department of Computer Technology, Yeshwantrao Chavan College of Engineering, Nagpur, India

DOI:

https://doi.org/10.29121/shodhkosh.v7.i1s.2026.7125

Keywords:

Human–AI Collaboration, Abstract Art, Generative Art, Human-in-the-loop Creativity, Computational Aesthetics, Creative AI

Abstract [English]

Human-AI collaboration is becoming a new paradigm in the modern form of abstract art production with the redefinition of the concept of creativity, authorship, and aesthetic choice. This paper explores a computer-assisted human and AI-based collaborative system where human artists create abstract art with artificial intelligence systems. The proposed solution is based on co-creation, a concept where human intuition and emotion and intent to generate are combined with the computational exploration, finding patterns, and generative abilities of AI as compared to fully automated generative art or purely human-driven abstraction. The study compiles the experiences of the AI-generated art systems, human-in-the-loop creative systems and machine-learning-generated abstract art to create a conceptual and implementation framework of collaborative creativity. The methodology will be based on an experimental design with three creative conditions and will include human-only creation, AI-only generation, and human-AI collaboration. Several generative models such as diffusion-based and transformer-inspired are used with interactive interfaces that enable the use of human guidance in an iterative manner, constraint setting, and feedback-based refinement. The quantitative evaluation metrics, including the compositional complexity, color variety, new score of novelty, and aesthetic integrity scores are supported by qualitative ones of artistic intent and expressive depth. Findings show that collaborative artworks are always more successful than the human-only and AI-only works in their novelty-coherence balance, conceptual diversity, and fashion variety.

References

Alalaq, A. S. (2025). AI-Powered Search Engines. ShodhAI: Journal of Artificial Intelligence, 2(1), 49–62. https://doi.org/10.29121/shodhai.v2.i1.2025.31 DOI: https://doi.org/10.29121/shodhai.v2.i1.2025.31

Alzoubi, A. M. A., Qudah, M. F. A., Albursan, I. S., Bakhiet, S. F. A., and Alfnan, A. A. (2021). The Predictive Ability of Emotional Creativity in Creative Performance Among University Students. SAGE Open, 11, 215824402110088. DOI: https://doi.org/10.1177/21582440211008876

Buschek, D., Mecke, L., Lehmann, F., and Dang, H. (2021). Nine Potential Pitfalls When Designing Human-AI Co-Creative Systems (arXiv:2104.00358). arXiv. https://arxiv.org/abs/2104.00358

Čábelková, I., Dvořák, M., Smutka, L., Strielkowski, W., and Volchik, V. (2022). The Predictive Ability of Emotional Creativity in Motivation for Adaptive Innovation Among University Professors Under COVID-19 Epidemic: An International Study. Frontiers in Psychology, 13, 997213. DOI: https://doi.org/10.3389/fpsyg.2022.997213

Chatterjee, S. (2024). Diffmorph: Text-Less Image Morphing with Diffusion Models (arXiv:2401.00739). arXiv. https://arxiv.org/abs/2401.00739

Chiou, E. K., and Lee, J. D. (2023). Trusting Automation: Designing for Responsivity and Resilience. Human Factors, 65, 137–165. DOI: https://doi.org/10.1177/00187208211009995

De Vries, K. (2020). You Never Fake Alone: Creative AI in Action. Information, Communication & Society, 23, 2110–2127. DOI: https://doi.org/10.1080/1369118X.2020.1754877

Deonna, J., and Teroni, F. (2025). The Creativity of Emotions. Philosophical Explorations, 28, 165–179. DOI: https://doi.org/10.1080/13869795.2025.2471824

E G, J. J., and A, N. J. (2024). Art of gamification: Exploring the Transformative Influence of Games on English Language Teaching and Learning. ShodhGyan-NU: Journal of Literature and Culture Studies, 2(1), 36–45. https://doi.org/10.29121/shodhgyan.v2.i1.2024.27 DOI: https://doi.org/10.29121/shodhgyan.v2.i1.2024.27

Haase, J., and Hanel, P. H. P. (2023). Artificial Muses: Generative Artificial Intelligence Chatbots have Risen to Human-Level Creativity. Journal of Creativity, 33, 100066. DOI: https://doi.org/10.1016/j.yjoc.2023.100066

Haase, J., and Pokutta, S. (2024). Human-Ai Co-Creativity: Exploring Synergies Across Levels of Creative Collaboration (arXiv:2411.12527). arXiv. https://arxiv.org/abs/2411.12527

Haj-Bolouri, A., Conboy, K., and Gregor, S. (2024). Research Perspectives: An Encompassing Framework for Conceptualizing Space in Information Systems: Philosophical Perspectives, Themes, and Concepts. Journal of the Association for Information Systems, 25, 407–441. DOI: https://doi.org/10.17705/1jais.00830

Jennings, K. E. (2010). Developing Creativity: Artificial Barriers in Artificial Intelligence. Minds and Machines, 20, 489–501. DOI: https://doi.org/10.1007/s11023-010-9206-y

Mateja, D., and Heinzl, A. (2021). Towards Machine Learning as an Enabler of Computational Creativity. IEEE Transactions on Artificial Intelligence, 2, 460–475. DOI: https://doi.org/10.1109/TAI.2021.3100456

Melville, N. P., Robert, L., and Xiao, X. (2023). Putting Humans Back in the Loop: An Affordance Conceptualization of the 4th Industrial Revolution. Information Systems Journal, 33, 733–757. DOI: https://doi.org/10.1111/isj.12422

Prudviraj, J., and Jamwal, V. (2025). Sketch and Paint: Stroke-By-Stroke Evolution of Visual Artworks (arXiv:2502.20119). arXiv. https://arxiv.org/abs/2502.20119 DOI: https://doi.org/10.1007/978-3-031-92808-6_13

Sundquist, D., and Lubart, T. (2022). Being Intelligent with Emotions to Benefit Creativity: Emotion Across the Seven Cs of Creativity. Journal of Intelligence, 10, 106. DOI: https://doi.org/10.3390/jintelligence10040106

Wu, Z., Gong, Z., Ai, L., Shi, P., Donbekci, K., and Hirschberg, J. (2024). Beyond Silent Letters: Amplifying LLMs in Emotion Recognition with Vocal Nuances (arXiv:2407.21315). arXiv. https://arxiv.org/abs/2407.21315 DOI: https://doi.org/10.18653/v1/2025.findings-naacl.117

Zhou, M., Wang, Z., Zheng, H., and Huang, H. (2024). Long and Short Guidance in Score Identity Distillation for One-Step Text-To-Image Generation (arXiv:2406.01561). arXiv. https://arxiv.org/abs/2406.01561

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Published

2026-02-17

How to Cite

Premalatha. P, Patil, S. M., Kushwaha, N. K., Jaiswal, R., Rajashri C K, & Karule, K. P. (2026). HUMAN–AI COLLABORATION IN ABSTRACT ART CREATION. ShodhKosh: Journal of Visual and Performing Arts, 7(1s), 646–656. https://doi.org/10.29121/shodhkosh.v7.i1s.2026.7125