Artificial Intelligence in Visual Arts: A Systematic Literature Review of Human–AI Co-Creation, Creative Processes, and Artistic Authorship
DOI:
https://doi.org/10.61978/harmonia.v3i4.1706Keywords:
artificial intelligence, visual arts, human–AI co-creation, creativity, artistic authorship, generative artificial intelligence, systematic literature reviewAbstract
Artificial intelligence has fundamentally transformed visual arts through generative systems that support ideation, image generation, and collaborative creative production. The widespread adoption of diffusion models, multimodal systems, and text-to-image technologies has raised critical questions surrounding creativity, authorship, human agency, and ethical responsibility. This study conducted a systematic literature review using the PRISMA framework, drawing on peer-reviewed publications from 2020 to 2025 across major academic databases, analyzed through narrative and thematic synthesis. A clear technological evolution is evident, from rule-based computational art to diffusion-based and multimodal AI systems. While these technologies accelerate ideation and visual exploration, increased productivity does not automatically yield greater originality, conceptual depth, or artistic quality. Professional artists consistently outperform novice users in concept development, prompt construction, visual evaluation, and contextual interpretation. Public and expert assessments of AI-assisted artworks are shaped more by perceived human intention, authenticity, and authorship attribution than by technical image quality alone. Persistent challenges remain around intellectual property, creative responsibility, explainability, and the measurement of distributed creative agency. AI is best understood as an augmentative collaborator rather than an autonomous creator. Meaningful artistic production emerges from sustained interaction between computational generation and human intention, expertise, and ethical stewardship. The study's principal contribution is an integrated conceptual framework linking technological capability, human creative processes, distributed agency, artistic evaluation, and governance, serving as a foundation for future research, professional practice, and policy development in AI-assisted visual arts.
References
Atkinson, P., & Barker, R. (2023). AI and the Social Construction of Creativity. Convergence the International Journal of Research Into New Media Technologies, 29(4), 1054–1069. https://doi.org/10.1177/13548565231187730 DOI: https://doi.org/10.1177/13548565231187730
Batlle-Roca, R., Gómez, E., Liao, W., Serra, X., & Mitsufuji, Y. (2023). Transparency in Music-Generative AI: A Systematic Literature Review. https://doi.org/10.21203/rs.3.rs-3708077/v1 DOI: https://doi.org/10.21203/rs.3.rs-3708077/v1
Begemann, A., & Hutson, J. (2024). Empirical Insights Into AI-assisted Game Development: A Case Study on the Integration of Generative AI Tools in Creative Pipelines. Metaverse, 5(2), 2568. https://doi.org/10.54517/m.v5i2.2568 DOI: https://doi.org/10.54517/m.v5i2.2568
Bryan–Kinns, N., Ford, C., Chamberlain, A., Benford, S., Kennedy, H., Li, Z., Wu, Q., Xia, G., & Rezwana, J. (2023). Explainable AI for the Arts: XAIxArts. 1–7. https://doi.org/10.1145/3591196.3593517 DOI: https://doi.org/10.1145/3591196.3593517
Bryan–Kinns, N., Ford, C., Zheng, S., Kennedy, H., Chamberlain, A., Lewis, M., Hemment, D., Li, Z., Wu, Q., Xiao, L., Xia, G., Rezwana, J., Clemens, M., & Vigliensoni, G. (2024). Explainable AI for the Arts 2 (XAIxArts2). 86–92. https://doi.org/10.1145/3635636.3660763 DOI: https://doi.org/10.1145/3635636.3660763
Burkhardt, S., & Rieder, B. (2024). Foundation Models Are Platform Models: Prompting and the Political Economy of AI. Big Data & Society, 11(2). https://doi.org/10.1177/20539517241247839 DOI: https://doi.org/10.1177/20539517241247839
Cao, Y., Aziz, M. A. A., & Wan Nur Rukiah Mohd Arshard. (2024). Stable Diffusion in Architectural Design: Closing Doors or Opening New Horizons? International Journal of Architectural Computing, 23(2), 339–357. https://doi.org/10.1177/14780771241270257 DOI: https://doi.org/10.1177/14780771241270257
Çelik, T. (2024). AI-Driven Production in Modular Architecture: An Examination of Design Processes and Methods. Comdem, 1, 320–339. https://doi.org/10.59543/comdem.v1i.10825 DOI: https://doi.org/10.59543/comdem.v1i.10825
Copper, C., Harrison, P. H., & Yang, Z. (2024). Artificial Intelligence Literacy: Collaborating to Support Image Research in Architecture Education. 150–156. https://doi.org/10.35483/acsa.am.112.21 DOI: https://doi.org/10.35483/ACSA.AM.112.21
Fan, P., & Jiang, Q. (2024). Exploring the Factors Influencing Continuance Intention to Use AI Drawing Tools: Insights From Designers. Systems, 12(3), 68. https://doi.org/10.3390/systems12030068 DOI: https://doi.org/10.3390/systems12030068
Göring, S., Rao, R. R. R., Merten, R., & Raake, A. (2023). Analysis of Appeal for Realistic AI-Generated Photos. Ieee Access, 11, 38999–39012. https://doi.org/10.1109/access.2023.3267968 DOI: https://doi.org/10.1109/ACCESS.2023.3267968
Guo, M., Nie, K., Gao, Z., Wang, X., Han, J., & Wu, X. (2025). I Prompt, It Generates, We Negotiate. Exploring Text-Image Intertextuality in Human-Ai Co-Creation of Visual Narratives With VLMs. https://doi.org/10.48550/arxiv.2511.03375
Han, R., Brennecke, J., Borah, D., & Lam, H. K. (2024). The Use of Social Media in Different Phases of the New Product Development Process: A Systematic Literature Review. R and D Management, 55(1), 108–126. https://doi.org/10.1111/radm.12687 DOI: https://doi.org/10.1111/radm.12687
Hunt, K. M. R. (2022). Could Artificial Intelligence Win the Next Weather Photographer of the Year Competition? Weather, 78(4), 108–112. https://doi.org/10.1002/wea.4348 DOI: https://doi.org/10.1002/wea.4348
Jansen, C., & Sklar, E. (2021). Exploring Co-Creative Drawing Workflows. Frontiers in Robotics and Ai, 8. https://doi.org/10.3389/frobt.2021.577770 DOI: https://doi.org/10.3389/frobt.2021.577770
Jo, H.-Y., Sakashita, M., Mishra, A., Suzuki, R., Niinuma, K., & Gupta, A. (2025). Map2Video: Street View Imagery Driven AI Video Generation. https://doi.org/10.48550/arxiv.2512.17883
Lee, J.-J., & Lee, K.-P. (2007). Cultural Differences and Design Methods for User Experience Research. 21–34. https://doi.org/10.1145/1314161.1314164 DOI: https://doi.org/10.1145/1314161.1314164
Leibowicz, C., Saltz, E., & Coleman, L. (2021). Creating AI Art Responsibly: A Field Guide for Artists. Diseña, (19). https://doi.org/10.7764/disena.19.article.5 DOI: https://doi.org/10.7764/disena.19.Article.5
Ling, L., Chen, X., Wen, R., Li, T. J., & Lc, R. (2024). Sketchar: Supporting Character Design and Illustration Prototyping Using Generative AI. Proceedings of the Acm on Human-Computer Interaction, 8(CHI PLAY), 1–28. https://doi.org/10.1145/3677102 DOI: https://doi.org/10.1145/3677102
Lyu, Y., Wang, X., Lin, R., & Wu, J. (2022). Communication in Human–AI Co-Creation: Perceptual Analysis of Paintings Generated by Text-to-Image System. Applied Sciences, 12(22), 11312. https://doi.org/10.3390/app122211312 DOI: https://doi.org/10.3390/app122211312
Meng, Y., & Chen, R. (2025). Tracing Generative AI in Digital Art: A Longitudinal Study of Chinese Painters’ Attitudes, Practices, and Identity Negotiation. https://doi.org/10.48550/arxiv.2511.03117
Oppenlaender, J. (2022). The Creativity of Text-to-Image Generation. 192–202. https://doi.org/10.1145/3569219.3569352 DOI: https://doi.org/10.1145/3569219.3569352
Ploennigs, J., & Berger, M. (2024). Automating Computational Design With Generative AI. Civil Engineering Design, 6(2), 41–52. https://doi.org/10.1002/cend.202400006 DOI: https://doi.org/10.1002/cend.202400006
Pouliou, P., Horvath, A.-S., & Palamas, G. (2023). Speculative Hybrids: Investigating the Generation of Conceptual Architectural Forms Through the Use of 3D Generative Adversarial Networks. International Journal of Architectural Computing, 21(2), 315–336. https://doi.org/10.1177/14780771231168229 DOI: https://doi.org/10.1177/14780771231168229
Rotter, J., & Bailkoski, W. (2025). AI Adoption in NGOs: A Systematic Literature Review. https://doi.org/10.48550/arxiv.2510.15509
Sahu, J. P. (2024). From Clay to Code: The Evolution of 3D Sculpting and Its Impact on Virtual Realms. International Journal of Research Publication and Reviews, 5(4), 886–900. https://doi.org/10.55248/gengpi.5.0424.0921 DOI: https://doi.org/10.55248/gengpi.5.0424.0921
Schetinger, V., Bartolomeo, S. D., El‐Assady, M., McNutt, A., Miller, M., & Adams, J. L. (2023). Doom or Deliciousness: Challenges and Opportunities for Visualization in the Age of Generative Models. https://doi.org/10.31219/osf.io/3jrcm DOI: https://doi.org/10.31219/osf.io/3jrcm
Seta, G. d., Pohjonen, M., & Knuutila, A. (2024). Synthetic Ethnography: Field Devices for the Qualitative Study of Generative Models. Big Data & Society, 11(4). https://doi.org/10.1177/20539517241303126 DOI: https://doi.org/10.1177/20539517241303126
Soikun, T. M. (2023). Understanding of Generative Art With ‘Python’ Programming. Jurnal Gendang Alam (Ga), 13(2). https://doi.org/10.51200/ga.v13i2.4735 DOI: https://doi.org/10.51200/ga.v13i2.4735
SUN, B. (2025). Community Art as an Egalitarian Participatory Practice. https://doi.org/10.22501/rc.3759070 DOI: https://doi.org/10.22501/rc.3759070
Veloso, P. (2024). (In)forming the New Building Envelope: A Pedagogical Study in Generative Design With Precedents and Multimodal Large Language Models. International Journal of Architectural Computing, 23(1), 96–121. https://doi.org/10.1177/14780771241254634 DOI: https://doi.org/10.1177/14780771241254634
Wang, H., Smith, D., & Demir, U. (2025). Scaling AI Filmmaking With Collaborative Networking. Iet Communications, 19(1). https://doi.org/10.1049/cmu2.12877 DOI: https://doi.org/10.1049/cmu2.12877
Zawacki‐Richter, O., Cefa, B., & Bai, J. Y. H. (2025). Towards Reproducible Systematic Reviews in Open, Distance, and Digital Education—An Umbrella Mapping Review. Review of Education, 13(1). https://doi.org/10.1002/rev3.70031 DOI: https://doi.org/10.1002/rev3.70031
Zhou, E., & Lee, D. (2024). Generative Artificial Intelligence, Human Creativity, and Art. Pnas Nexus, 3(3). https://doi.org/10.1093/pnasnexus/pgae052 DOI: https://doi.org/10.1093/pnasnexus/pgae052
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Harmonia : Journal of Music and Arts
This work is licensed under a Creative Commons Attribution 4.0 International License.
