Human Art Wins Hearts, but AI Ideas Win Minds, Creativity Study Finds

When people judge creativity, the label attached to a work can matter as much as the work itself. A new study published in BMC Psychology by Yanming Hou, Yingying Zhang, Bin Wang, Yuxi Zhu, and Xiaofei Wu of Hangzhou Normal University and Shenzhen Polytechnic University shows that the mere attribution of a creative product to artificial intelligence or to a human being systematically shifts how creative that product is perceived to be. The twist, and the reason the findings are already generating discussion among psychologists and technologists alike, is that the direction of the bias depends entirely on the domain being judged. In art, humans win. In divergent thinking tasks, the machine comes out ahead.

The research team set out to answer a deceptively simple question: how do humans perceive and evaluate creativity when it is attributed to artificial intelligence? As generative AI systems produce paintings, poems, and ideas that are increasingly difficult to distinguish from human output, the psychological question of evaluation becomes urgent. If identical works are judged differently depending on whether people believe a human or an algorithm produced them, then much of what passes for creativity assessment may be contaminated by attitudes toward the creator rather than qualities of the creation.

The investigators addressed this question across three experiments, each probing a different corner of creative production. The first experiment focused on artistic creation, using paintings and poetry as stimuli. Participants evaluated creative outputs, and the researchers manipulated only the source label: some participants were told a work was created by a human, while others were told the identical work came from an AI system. The results were unambiguous. Outputs labeled as human-created were rated as more creative than the same outputs bearing an AI label. In the realm of art, at least, a persistent pro-human bias remains firmly intact.

This finding aligns with a long tradition in creativity research suggesting that artistic creativity is judged not only on formal properties such as novelty and usefulness but also on beliefs about intentionality, emotion, and lived experience. A painting is not merely an arrangement of pigments; it is presumed to encode a mind that chose, suffered, and expressed. When that presumption is removed by an AI attribution, evaluators appear to discount the work, even though the physical stimulus before their eyes has not changed by a single brushstroke or syllable.

The second experiment added a layer of nuance by examining co-created works, in which the attribution of responsibility between human and machine was manipulated. Here the picture became more textured. When poetry was labeled as involving human ideation with AI assistance, perceived originality was enhanced relative to other attributions. In other words, for poetry, a collaborative framing actually boosted judgments of originality, suggesting that participants saw the combination of human conceptual direction and machine execution as a virtue rather than a contaminant. Painting, however, told a different story: works attributed primarily to humans remained dominant in evaluators’ judgments. The medium itself, it seems, shapes how comfortable people are with machine involvement in the creative process.

Why would collaboration elevate poetry but not painting? The authors’ findings point toward a domain-specific pattern in attitudes toward AI creativity. Poetry, as a verbal art, may be perceived as closer to the kind of combinatorial, idea-driven processing at which large language models excel, so human-AI partnership reads as a natural extension of the writer’s toolkit. Painting, by contrast, may be more tightly bound to embodied skill, personal vision, and the aura of the individual artist, making any dilution of human authorship costly in evaluators’ eyes. The study’s summary of these results captures the tension precisely: viewing collaboration positively yet maintaining a persistent bias in valuing AI’s creative contributions.

The third experiment delivered the study’s most striking reversal. Instead of asking participants to judge finished artworks, the researchers used the Alternative Uses Test, a classic measure of divergent thinking in which respondents generate unusual uses for everyday objects. Divergent thinking is a cornerstone construct in creativity research, operationalizing the capacity to produce varied and original responses in open-ended tasks. When the same output was presented under different source labels, participants gave higher creativity ratings to responses bearing an AI label. The pro-human bias that dominated artistic judgment flipped entirely when the task was framed as idea generation rather than artistic creation.

This reversal is more than a curiosity; it carries real implications for how organizations, educators, and researchers measure creative potential. If evaluators unconsciously reward ideas attributed to AI in brainstorming and ideation contexts, then human contributors may be systematically undervalued in precisely the settings, such as innovation workshops, design sprints, and research labs, where divergent thinking is most prized. Conversely, in galleries, literary prizes, and art markets, AI-labeled work may face a penalty that no improvement in algorithmic technique can overcome. The bias is not a single attitude but a pair of domain-specific expectations about where minds, biological or artificial, belong.

Methodologically, the study relied on repeated-measures multivariate analysis of variance, known by the abbreviation RM MANOVA, a statistical framework well suited to comparing ratings across multiple dependent measures when the same evaluators encounter multiple conditions. By holding the creative products constant and varying only the attribution, the design isolates attribution itself as the causal factor, ruling out the alternative explanation that AI outputs simply differ in quality. The research was approved by the Ethics Committee of the Jing Hengyi School of Education at Hangzhou Normal University under approval number 2023019, and all procedures followed the ethical standards of the Declaration of Helsinki, with informed consent obtained electronically before data collection. The work was supported by the National Natural Science Foundation of China, the Natural Science Foundation of Zhejiang Province, and the Organized Research Program of Hangzhou Normal University.

There is also an irony worth savoring: the authors acknowledge that during the preparation of the work they used ChatGPT, DALL-E, and other AI tools to make experimental materials and correct the manuscript. A study revealing that humans discount AI-labeled art while favoring AI-labeled ideas was itself produced with substantial machine assistance, a fact that would likely have boosted its perceived originality had it been framed as a poetry project, and diminished it had it been framed as a painting. As generative models weave themselves ever deeper into creative work, this research suggests that the hardest problem may not be teaching machines to create, but teaching ourselves to judge what they create on its merits. Until then, the label on the canvas, or on the idea, will keep quietly rewriting the verdict before anyone has truly looked.

Subject of Research: Domain-specific human bias in evaluating creativity attributed to AI versus humans

Article Title: The domain-specific bias in creativity evaluation: human evaluators favor human-created art, but prefer AI outputs for divergent thinking

Article References: Hou, Y., Zhang, Y., Wang, B., Zhu, Y., & Wu, X. (2026). The domain-specific bias in creativity evaluation: human evaluators favor human-created art, but prefer AI outputs for divergent thinking. BMC Psychology. https://doi.org/10.1186/s40359-026-05704-x

Image Credits: AI Generated

DOI: 10.1186/s40359-026-05704-x

Keywords: artificial intelligence, creativity, creativity evaluation, divergent thinking, Alternative Uses Test, human-AI collaboration, artistic creation, poetry, painting, attribution bias, BMC Psychology, psychology