Vol 22 | Issue 3 | 63-78 | September 2026
André D. Gopal
Department of Digital Arts,
University of the Witwatersrand
andre.gopal@wits.ac.za
https://orcid.org/0009-0003-9585-7786
*Corresponding author
Abstract
This study investigates how the origin of visual art (human vs. AI) influences perception, aesthetic appreciation, and purchase intent, and whether individuals can distinguish between them. We explored shifts in perception after origin disclosure and underlying factors.
In a double-blind, between-subjects experiment, 60 young adults rated human-made or AI-generated artworks on aesthetic, emotional, and purchase dimensions. After initial ratings, true origin was revealed, and participants reflected on changes.
Results showed that when origin was concealed, participants rated AI and human art similarly across all measures and could not reliably distinguish them. However, disclosing AI authorship significantly reduced appreciation, emotional engagement, and purchase willingness. Conversely, human authorship disclosure increased these values. Qualitative data emphasized the importance of perceived authenticity and human agency. These findings suggest biases against AI-generated art stem not from intrinsic aesthetic qualities, but from knowledge of authorship, reflecting psychological and cultural narratives. This highlights the need for nuanced dialogue on technology, creativity, and value as AI reshapes the art world.
Keywords: AI-generated art, Human-computer interaction, Art consumption, Creative artificial intelligence, Art psychology, Art perception
References
Acemoglu, D., & Restrepo, P. (2019). Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives, 33(2), 3–30. https://doi.org/10.1257/jep.33.2.3
Afroogh, S., Akbari, A., Malone, E., Kargar, M., & Alambeigi, H. (2024). Trust in AI: Progress, challenges, and future directions. Humanities and Social Sciences Communications, 11, Article 40. https://doi.org/10.1057/s41599-024-04044-8
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Chamberlain, R., Mullin, C., Scheerlinck, B., & Wagemans, J. (2017). Putting the art in artificial: Aesthetic responses to computer-generated art. Psychology of Aesthetics, Creativity, and the Arts, 12, Article 13. https://doi.org/10.1037/aca0000136
Chiang, T. (2024, August 31). Why A.I. isn’t going to make art. The New Yorker. https://www.newyorker.com/culture/the-weekend-essay/why-ai-isnt-going-to-make-art
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
Elgammal, A., Liu, B., Elhoseiny, M., & Mazzone, M. (2017). CAN: Creative adversarial networks, generating “art” by learning about styles and deviating from style norms. arXiv. https://doi.org/10.48550/arXiv.1706.07068
Escalona-Márquez, L., Cedeño Tapia, S., Camputaro, L., & Aparicio-Escalante, C. (2024). Teachers in the age of artificial intelligence: Preparation and response to challenges. https://doi.org/10.5772/intechopen.1005172
Field, A. (2013). Discovering statistics using IBM SPSS statistics: And sex and drugs and rock “n” roll (4th ed.). Sage.
Florida, R. (2003). The rise of the creative class: And how it’s transforming work, leisure, community and everyday life. Basic Books. [Reviewed in Canadian Public Policy / Analyse de Politiques, 29, https://doi.org/10.2307/3552294]
Henrich, J., Heine, S. J., & Norenzayan, A. (2010). The weirdest people in the world? Behavioral and Brain Sciences, 33(2-3), 61–135. https://doi.org/10.1017/S0140525X0999152X
Kahneman, D., Knetsch, J. L., & Thaler, R. H. (1991). Anomalies: The endowment effect, loss aversion, and status quo bias. Journal of Economic Perspectives, 5(1), 193–206. https://doi.org/10.1257/jep.5.1.193
Kaufman, S. B., & Kaufman, J. C. (Eds.). (2009). The psychology of creative writing. Cambridge University Press.
Leder, H., Belke, B., Oeberst, A., & Augustin, D. (2004). A model of aesthetic appreciation and aesthetic judgments. British Journal of Psychology, 95(Pt 4), 489–508. https://doi.org/10.1348/0007126042369811
Magni, F., Park, J., & Chao, M. (2023). Humans as creativity gatekeepers: Are we biased against AI creativity? Journal of Business and Psychology, 39. https://doi.org/10.1007/s10869-023-09910-x
McCormack, J., Gifford, T., & Hutchings, P. (2019). Autonomy, authenticity, authorship and intention in computer generated art. In A. Ekárt, A. Liapis, & M. L. Castro Pena (Eds.), Lecture notes in computer science (pp. 35–50). Springer. https://doi.org/10.1007/978-3-030-16667-0_3
Mikalonytė, E. S., & Kneer, M. (2021). Can artificial intelligence make art? Folk intuitions as to whether AI-driven robots can be viewed as artists and produce art. ACM Transactions on Human-Robot Interaction. https://ssrn.com/abstract=3827314 or http://dx.doi.org/10.2139/ssrn.3827314
Newman, G., & Bloom, P. (2011). Art and authenticity: The importance of originals in judgments of value. Journal of Experimental Psychology: General, 141, 558–569. https://doi.org/10.1037/a0026035
Othman, A. (2024). Democratizing creativity: The rise of generative AI in digital art and storytelling. https://doi.org/10.13140/RG.2.2.11126.10563
Prissé, B., Deng, R., Ho, J. Q., Jayasekara, D., & Ouangraoua, C. (2026). The creativity of artificial intelligence. Journal of Behavioral and Experimental Economics, 121, Article 102528. https://doi.org/10.1016/j.socec.2026.102528
Shin, D. (2020). User perceptions of algorithmic decisions in the personalized AI system: Perceptual evaluation of fairness, accountability, transparency, and explainability. Journal of Broadcasting & Electronic Media, 64, 1–25. https://doi.org/10.1080/08838151.2020.1843357
Singh, A., Saxena, R., & Saxena, S. (2024). The human touch in the age of artificial intelligence: A literature review on the interplay of emotional intelligence and AI. Asian Journal of Current Research, 9, 36–50. https://doi.org/10.56557/ajocr/2024/v9i48860
The Economist. (2023, May 24). Art made by artificial intelligence is developing a style of its own. https://www.economist.com/culture/2023/05/24/art-made-by-artificial-intelligence-is-developing-a-style-of-its-own
Thompson, D. (2022). The orange balloon dog: Bubbles, turmoil and avarice in the contemporary art market. Aurum Press.
Tigre Moura, F., Castrucci, C., & Hindley, C. (2023). Artificial intelligence creates art? An experimental investigation of value and creativity perceptions. The Journal of Creative Behavior, 57, 534–549. https://doi.org/10.1002/jocb.600
Włodarczyk, M. (2024). Digital disruption in art: A comprehensive analysis of AI and NFT market dynamics. Annales Universitatis Mariae Curie-Skłodowska, sectio H – Oeconomia, 58(2), 171–193. http://dx.doi.org/10.17951/h.2024.58.2.171-193