Examining the Application Value of Graphic Software as a Painting Medium in the Artificial Intelligence Era

Authors

  • Songsheng Huang College of Creative Arts, Universiti Teknologi MARA, Perak Branch, Seri Iskandar Campus, 32610 Seri Iskandar, Perak, Malaysia
  • Azian Tahir College of Creative Arts, Universiti Teknologi MARA, Perak Branch, Seri Iskandar Campus, 32610 Seri Iskandar, Perak, Malaysia
  • Issarezal Ismail College of Creative Arts, Universiti Teknologi MARA, Perak Branch, Seri Iskandar Campus, 32610 Seri Iskandar, Perak, Malaysia

DOI:

https://doi.org/10.24191/idealogy.v11i2.909

Keywords:

Digital painting, Graphics software, Artificial intelligence, Application value, Pure art

Abstract

In the realm of digital painting, the rapid development of artificial intelligence (AI) has weakened several practical advantages previously associated with graphics software, particularly efficiency, flexible revision, and the production of visually complex images. This raises a focused question: what application value can graphics software still retain as a painting medium in the AI era? Responding to this question, this study proposes a medium-oriented strategy for digital painting grounded in the idea of pure art and supported by observation and semi-structured interviews with three selected artists. The cases of Mario Viele, Southeast-Northwest, and Sébastien Dessauvage show that graphics software remains valuable when it is not treated merely as a tool for accelerating production or reproducing established physical or digital styles. Its distinctive value lies in artists' critical exploration of software-specific imaging conditions, including cross-software manipulation, parameter experimentation, software malfunction, version difference, layering, and obsolete digital environments. The study argues that such practices allow graphics software to function as a medium for extending the expressive boundaries of painting. In the AI era, this value is twofold: it sustains non-standard digital painting languages that are difficult for data-driven models to reproduce smoothly, and it provides new visual material through which AI image-generation systems may later be trained and diversified. 

 

Keywords: Digital painting, Graphics software, Artificial intelligence, Application value, Pure art. 

 

References

Agyeman, C. A. (2015). Artists' perception of the use of digital media in painting. Ohio University.

Alcaraz, A. Ł. (2016). Neomodernist digital painting. Art Inquiry, (18), 177-197.

Annum, G. Y. (2014). Digital painting evolution: A multimedia technological platform for expressivity in fine art painting. Journal of Fine and Studio Art, 4(1), 1-8.

Barskaya, G. B., Chernysheva, T. Y., Krupkin, I. A., & Lesiv, A. A. (2024, February). Creation of a painting dataset for use in artificial intelligence tasks. In Proc. of SPIE Vol (Vol. 13065, pp. 1306502-1).

Betancourt, M. (2016). Glitch art in theory and practice: Critical failures and post-digital aesthetics. Routledge.

Cançat, A. K. (2016). Jean Fautrier ve İmpasto Resim Tekniği(Jean Fautrier and Impasto Painting Technique). İdil Sanat ve Dil Dergisi, 5(22), 677-690.

Chaudhary, A., Rastogi, R., Mattoo, A., Kumar, P., Kumari, T., & Dubey, D. (2025). Generative Adversarial Networks (GANs). Generative AI: Disruptive Technologies for Innovative Applications, 29-55.

Chopra, M., & Thakur, A. (2025). Generative AI for the Creation of Images. Intelligent Solutions for Smart Adaptation in Digital Era: Select Proceedings of InCITe 2024, Volume 2, 1278, 193.

Ciesielska, M., Boström, K. W., & Öhlander, M. (2017). Observation methods. In Qualitative methodologies in organization studies: Volume II: Methods and possibilities (pp. 33-52). Cham: Springer International Publishing.

De Souza Junior, L. C. (2023). Y2K typography: origins, examples, and characteristics of experimental techno fonts from the turn of the millennium.

Di Dio, C., Ardizzi, M., Schieppati, S. V., Massaro, D., Gilli, G., Gallese, V., & Marchetti, A. (2025). Art made by artificial intelligence: The effect of authorship on aesthetic judgments. Psychology of Aesthetics, Creativity, and the Arts, 19(5), 1164.

Duan, F., Ismail, I., & Ramli, I. (2024). The Influence of Rice Paper on the Texture Characteristics of Modern Chinese Boneless Paintings. Idealogy Journal, 9(2).

Fried, M. (1998). Art and objecthood: essays and reviews. University of Chicago Press.

Greenberg, C. (1940). Towards a newer Laocoon. Partisan Review, 7(4), 296-310.

Greenberg, C. (1971/1989). Art and culture: Critical essays. Beacon Press.

Greenberg, C. (2018). Modernist painting. In Modern art and Modernism (pp. 5-10). Routledge.

Greenway, T., Clee, L., Perrins, C., & Tilbury, R. (Eds.). (2009). Digital art masters. Taylor & Francis. https://dl.acm.org/doi/10.5555/1795846

Hess, B., & De Kooning, W. (2004). Willem de Kooning, 1904-1997: Content as a Glimpse. Taschen.

Laurie, A. P. (2020). The Painter's Methods and Materials: The Handling of Pigments in Oil, Tempera, Water-Colour and in Mural Painting, the Preparation of Grounds and Canvas, and the Prevention of Discolouration-With Many Illustrations. Read Books Ltd.

Leong, W. Y. (2025). AI-Generated Artwork as Modern Interpretation of Historical Paintings. International Journal of Social Sciences, 5(1).

LeRue, D. (2024). Epistemology of Digital Painting: Examining Plein-Aire Landscape Painting Between the Physical and the Digital.

Liu, X., Liu, Y., & Wei, Z. (2020, February). A rational survey of art and technology: From traditional painting to intelligent painting. In 6th International Conference on Education, Language, Art and Inter-cultural Communication (ICELAIC 2019) (pp. 722-728). Atlantis Press.

Oksanen, A., Cvetkovic, A., Akin, N., Latikka, R., Bergdahl, J., Chen, Y., & Savela, N. (2023). Artificial intelligence in fine arts: A systematic review of empirical research. Computers in Human Behavior: Artificial Humans, 1(2), 100004.

Ringle, M. (2019). Artificial intelligence and semantic theory. In Language, Mind, and Brain (pp. 45-63). Psychology Press.

Rogala, D. V. (2016). Hans Hofmann: the artist's materials. Getty Publications.

Roose, K. (2022). An AI-Generated Picture Won an Art Prize. Artists Arenʼt Happy. New York Times, 16(01), 2025.

Singh, G. (2012). Jerry Palmer: Digital Painter. IEEE Computer Graphics and Applications, 32(06), 4-5.

Speidel, K. P. (2018). Could it be painting? Definitions, Symptoms (and Digital Retouching). In PaintingDigitalPhotography: Synthesis and Difference in the Age of Media Equivalence (pp. 77-100). Cambridge Scholars Publishing.

Suda, M. (2018). Brushstrokes as Art’s Unique Signature East and West. Idealogy Journal, 3(3), 90-95.

Sugiarto, E., Kurniawati, D. W., Febriani, M., Fiyanto, A., & Imawati, R. A. (2021, March). Computer-based art in folklore illustration: development of mixed media digital painting in education context. In IOP Conference Series: materials science and engineering (Vol. 1098, No. 3, p. 032017). IOP Publishing.

Sun, Y., Yang, C. H., Lyu, Y., & Lin, R. (2022). From pigments to pixels: a comparison of human and AI painting. Applied Sciences, 12(8), 3724.

Taherdoost, H. (2022). How to conduct an effective interview; a guide to interview design in research study authors. International Journal of Academic Research in Management (IJARM), 11(1), 39-51.

Wenyan, G., Daud, W. S. A. W. M., & Tahir, A. (2023). The spirit and elements of Malaysian multiculturalism in Chuah Thean Teng’s Batik paintings. Idealogy Journal, 8(2).

Xiao, Y. (2025). Research on the Application of Generative Adversarial Networks in Artificial Intelligence Painting. Advances in Engineering Technology Research, 15(1), 1460-1460.

Yin, S. (2022). Analysis on the inner relationship between computer digital painting and traditional painting. Mobile Information Systems, 2022(1), 9462510.

Yuan, H. (2025). An Ongoing Visual Turn About Contemporary “Green Screen” Painting.

林韬 (Lin, T). (2002). (Digital Art). 重庆出版社 (Chongqing Publishing House).

Downloads

Published

2026-09-01

Most read articles by the same author(s)

1 2 3 > >>