Does the Source Matter? An Experimental Study on User Perception of AI-Generated Versus Human-Authored Curatorial Narratives

Abstract

This study investigates how curatorial attribution (AI versus human) influences audience perception across Narrative Coherence, legitimacy, and Aesthetic-Emotional Engagement. Utilizing a 2 × 2 factorial design (N = 281), participants evaluated identical artworks under varying provenance labels. Results from PLS-SEM indicate that while Narrative Coherence was not statistically significant, a marginal trend suggests a possible influence of attribution. The “Human” label significantly enhances perceived legitimacy and Aesthetic-Emotional Engagement. Notably, Signal Detection Theory (SDT) analysis indicates limited discrimination ability, suggesting that participants were generally unable to reliably distinguish between AI and human authored texts. However, Aesthetic-Emotional Engagement emerged as the primary significant mediator of Overall Evaluation. These findings demonstrate an attribution-based asymmetry, where evaluations appear to be shaped more by attribution cues than by accurate source identification. This research extends the Model of Aesthetic Experience, offering strategic implications for galleries regarding the integration of AI efficiency with human expertise.

Keywords

curatorial attribution AI-generated curation aesthetic-emotional engagement attribution bias signal detection theory

Get full access to this article

View all access options for this article.

References

Arantes

(2025). Museums in dispute: Artificial intelligence, digital culture, and critical curation. Arts, 14(3), 65. https://doi.org/10.3390/arts14030065

Balzer

(2014). Curationism : how curating took over the art world and everything else. Coach House Books.

Bareither

Wirth

(2025). Social media feeds as curatorial assemblages: A conceptual framework. Media, Culture & Society. https://doi.org/10.1177/01634437251360372

Bourdieu

(1984). Distinction: A social critique of the judgement of taste. Harvard University Press.

Brieber

Nadal

Leder

Rosenberg

(2014). Art in time and space: Context modulates the relation between art experience and viewing time. PLoS ONE, 9(6), https://doi.org/10.1371/journal.pone.0099019

Buder

Becker

Bareiß

Huff

(2024). Beyond mere algorithm aversion: Are judgments about computer agents more variable? Communication Research. https://doi.org/10.1177/00936502241303588

Carah

(2014). Curators of databases: Circulating images, managing attention and making value on social Media. Media International Australia, 150(1), 137–142. https://doi.org/10.1177/1329878X1415000125

Cohen

(1988). Statistical power analysis for the behavioral sciences (second). Lawrence Erlbaum Associates.

Dickey

(2023). Act as if you are a Curator: An AI-Generated exhibition. https://doi.org/10.24926/24716839

10.

Dietvorst

B. J.

Simmons

J. P.

Massey

(2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General, 144(1), 114–126. https://doi.org/10.1037/xge0000033

11.

Gerger

Leder

Kremer

(2014). Context effects on emotional and aesthetic evaluations of artworks and IAPS pictures. Acta Psychologica, 151, 174–183. https://doi.org/10.1016/j.actpsy.2014.06.008

12.

Graesser

A. C.

Singer

Trabasso

(1994). Constructing Inferences During Narrative Text Comprehension. Psychological Review, 101(3). https://doi.org/10.1037/0033-295X.101.3.371

13.

Hair

J. F.

Hult

G. T. M.

Ringle

C. M.

Sarstedt

Danks

N. P.

Ray

(2021). Partial least squares structural equation modeling (PLS-SEM) using R. Springer International Publishing. https://doi.org/10.1007/978-3-030-80519-7

14.

Horton

C. B.

White

M. W.

Iyengar

S. S.

(2023). Bias against AI art can enhance perceptions of human creativity. Scientific Reports, 13(1). https://doi.org/10.1038/s41598-023-45202-3

15.

Jiang

H. H.

Brown

Cheng

Khan

Gupta

Workman

Hanna

Flowers

Gebru

Artist

(2023). AI Art and its impact on artists. AIES 2023 - Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society (pp. 363–374). https://doi.org/10.1145/3600211.3604681

16.

Kirk

Skov

Hulme

Christensen

M. S.

Zeki

(2009). Modulation of aesthetic value by semantic context: An fMRI study. NeuroImage, 44(3), 1125–1132. https://doi.org/10.1016/j.neuroimage.2008.10.009

17.

Leder

Belke

Oeberst

Augustin

(2004). A model of aesthetic appreciation and aesthetic judgments. British Journal of Psychology, 95. www.bps.org.uk https://doi.org/10.1348/0007126042369811

18.

She

(2025). AI-Assisted Curation: A framework for curatorial materials. 2025 IEEE International Conference on Multimedia and Expo Workshops (ICMEW) (pp. 1–7). https://doi.org/10.1109/ICMEW68306.2025.11152075

19.

(2020). The trend and characteristic of AI in art design. Journal of Physics: Conference Series, 1624(5), https://doi.org/10.1088/1742-6596/1624/5/052028

20.

Logg

J. M.

Minson

J. A.

Moore

D. A.

(2019). Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes, 151, 90–103. https://doi.org/10.1016/j.obhdp.2018.12.005

21.

Macmillan

N. A.

Creelman

C. D.

(2004). Detection theory. Psychology Press.

22.

Maksimova

(2025). Negotiation of dominant AI narratives in museum exhibitions. AI and Society, 40(5), 3813–3826. https://doi.org/10.1007/s00146-024-02140-6

23.

Mikalonyte

E. S.

Kneer

(2022). 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, 11(4), https://doi.org/10.1145/3530875

24.

Newman

G. E.

Bloom

(2012). Art and authenticity: The importance of originals in judgments of value. Journal of Experimental Psychology: General, 141(3), 558–569. https://doi.org/10.1037/a0026035

25.

Newton

Dhole

(2023). Is AI art another industrial revolution in the making? http://arxiv.org/abs/2301.05133

26.

O’Neill

(2016). The culture of curating and the curating of culture(s) .

27.

Pelowski

Gerger

Chetouani

Markey

P. S.

Leder

(2017a). But Is It really Art? The classification of images as “Art”/"Not Art” and correlation with appraisal and viewer interpersonal differences. Frontiers in Psychology, 8(OCT). https://doi.org/10.3389/fpsyg.2017.01729

28.

Pelowski

Markey

P. S.

Forster

Gerger

Leder

(2017b). Move me, astonish me… delight my eyes and brain: The Vienna integrated model of top-down and bottom-up processes in art perception (VIMAP) and corresponding affective, evaluative, and neurophysiological correlates. Physics of Life Reviews, 21, 80–125. Elsevier B.V. https://doi.org/10.1016/j.plrev.2017.02.003

29.

Podsakoff

P. M.

MacKenzie

S. B.

Lee

J. Y.

Podsakoff

N. P.

(2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. American Psychological Association Inc. https://doi.org/10.1037/0021-9010.88.5.879

30.

R Core Team. (2025). R: A language and environment for statistical computing. https://doi.org/10.1890/0012-9658(2002)083[3097:CFHIWS]2.0.CO;2

31.

Reymond

Pelowski

Opwis

Takala

Mekler

E. D.

(2020). Aesthetic Evaluation of Digitally Reproduced Art Images. Frontiers in Psychology, 11, https://doi.org/10.3389/fpsyg.2020.615575

32.

Ringle

C. M.

Sarstedt

Sinkovics

R. R.

(2023). A perspective on using partial least squares structural equation modelling in data articles. Data in Brief, 48, https://doi.org/10.1016/j.dib.2023.109074

33.

Rix

Berger

Hess

Rzepka

(2025). The algorithm discount: Explaining Consumers’ valuation of human- versus algorithm-created digital products. Journal of Management Information Systems, 42(2), 633–668. https://doi.org/10.1080/07421222.2025.2487308

34.

Russell

P. A.

Milne

(1997). Meaningfulness and hedonic value of paintings: Effects of titles. Empirical Studies of the Arts, 15(1), 61–73. https://doi.org/10.2190/eht3-hwvm-52cb-8qhj

35.

Schaerf

Zapata

P. B.

Bernasconi

Neri

Negueruela

Castillo

(2024). AI Art Curation: Re-imagining the city of Helsinki in occasion of its Biennial (Vol. 1). https://doi.org/10.48550/arXiv.2306.03753

36.

Schindler

Hosoya

Menninghaus

Beermann

Wagner

Eid

Scherer

K. R.

(2017). Measuring aesthetic emotions: A review of the literature and a new assessment tool. PLoS ONE, 12(6). Public Library of Science. https://doi.org/10.1371/journal.pone.0178899

37.

Sussman

S. W.

Siegal

W. S.

(2003). Informational influence in organizations: An integrated approach to knowledge adoption. Information Systems Research, 14(1), 47–65. https://doi.org/10.1287/isre.14.1.47.14767

38.

Swami

(2013). Context matters: Investigating the impact of contextual information on aesthetic appreciation of paintings by Max Ernst and Pablo Picasso. Psychology of Aesthetics, Creativity, and the Arts, 7(3), 285–295. https://doi.org/10.1037/a0030965

39.

Thiel

Bernhardt

J. C.

(2024). AI in Museums - reflections, perspectives and applications. https://www.ebsco.com/terms-of-use

40.

Esteva

Jain

S. D.

Jain

(2014). Interactive visualization for curatorial analysis of large digital collection. Information Visualization, 13(2), 159–183. https://doi.org/10.1177/1473871612473590

41.

Zhao

Lynch

J. G.

Chen

(2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37(2), 197–206. https://doi.org/10.1086/651257

42.

Zhou

Kawabata

(2023). Eyes can tell: Assessment of implicit attitudes toward AI art. I-Perception, 14(5). https://doi.org/10.1177/20416695231209846