AI Usage and Disclosure: Effects on Peer Perceptions of Creativity in Multidisciplinary Teams
DOI:
https://doi.org/10.23726/cij.2026.1853Keywords:
LLM, generative AI, AI Disclosure, Perceived Creativity, Algorithm Aversion, Peer Perception, Collaborative TeamsAbstract
As generative Artificial Intelligence (AI) becomes increasingly integrated into collaborative creative work, questions remain about how AI usage and disclosure shape interpersonal evaluations of creativity. This study examines how a team member's reputation for AI usage and the disclosure of AI assistance affect their peers' perception of their creativity in a collaborative environment. Using a vignette-based 2×2 within-subjects factorial design, a multidisciplinary sample (N=37) evaluated teammates across four conditions varying two independent variables: the teammate's established AI usage habit (frequent vs. rare) and their disclosure of the idea's origin (AI-generated vs. Human-generated). Perceived creativity served as the primary dependent variable. Results indicate a significant creativity discount associated with frequent AI use and explicit AI disclosure. A significant interaction revealed that disclosure mattered more for rare AI users, who gained substantially from claiming human generation, than for frequent users, whose creative reputation was discounted regardless of what they disclosed. The highest creativity ratings were assigned to a teammate described as a rare AI user who attributed the idea to human origin. These findings highlight a tension between AI transparency and the maintenance of creative credibility within team environments.
References
Bauer, K., Jussupow, E., Heigl, R., Vogt, B., & Hinz, O. (2025). How disclosing generative AI use impacts human creative involvement in Human-GenAI collaboration. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4782554
Castelo, N., Bos, M. W., & Lehmann, D. R. (2019). Task-dependent algorithm aversion. Journal of Marketing Research, 56(5), 809-825. https://doi.org/10.1177/0022243719851788
Cunningham, C. V., Radvansky, G. A., & Brockmole, J. R. (2025). Human creativity versus artificial intelligence: Source attribution, observer attitudes, and eye movements while viewing visual art. Frontiers in Psychology, 16. https://doi.org/10.3389/fpsyg.2025.1509974
Dhanuka, D. (2025, October 7). Impact of LLMS on team collaboration in software development. https://arxiv.org/html/2510.08612v1
Doshi, A. R., & Hauser, O. P. (2024). Generative AI enhances individual creativity but reduces the collective diversity of novel content. Science Advances, 10(28). https://doi.org/10.1126/sciadv.adn5290
Folorunso, J., Vayyala, R., Oladepo, O., Kolapo, M. O., & Ogunsanya, V. A. (2025). Product Design: The evolving role of generative AI in creative workflows. International Journal of Scientific and Management Research, 08(04), 01–27. https://doi.org/10.37502/ijsmr.2025.8401
Gough, H. G. (1979). A creative personality scale for the Adjective Check List. Journal of Personality and Social Psychology. 37. 1398-1405.https://doi.org/10.1037/0022-3514.37.8.1398
Gough, H. G., & Heilbrun, A. B. (1965). The Adjective Check List Manual. Palo Alto, CA: Consulting Psychologists Press. https://doi.org/10.1037/t02310-000
He, J., Houde, S., & Weisz, J. D. (2025). Which contributions deserve credit? Perceptions of attribution in human-AI co-creation. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (Article 540, pp. 1–18). Association for Computing Machinery. https://doi.org/10.1145/3706598.3713522
Kelley, H. H. (1973). The processes of causal attribution. American Psychologist. 28. 107-128. https://doi.org/10.1037/h0034225
Raj, M., Berg, J., & Seamans, R. (2023). Art-ificial intelligence: The Effect of AI disclosure on evaluations of creative content. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2303.06217
Runco, M. A., & Jaeger, G. J. (2012). The standard definition of creativity. Creativity Research Journal, 24(1), 92–96. https://doi.org/10.1080/10400419.2012.650092
Runco, M. A., & Johnson, D. J. (2002). Parents’ and Teachers’ Implicit Theories of Children’s Creativity: A Cross-Cultural Perspective. Creativity Research Journal, 14(3–4), 427–438. https://doi.org/10.1207/S15326934CRJ1434_12
Schilke, O., & Reimann, M. (2025). The transparency dilemma: How AI disclosure erodes trust. Organizational Behavior and Human Decision Processes, 188, Article 104405. https://doi.org/10.1016/j.obhdp.2025.104405
Steele, C. M., & Aronson, J. (1995). Stereotype Threat and the Intellectual Test Performance of African Americans. Journal of Personality and Social Psychology. 69. 797-811. https://doi.org/10.1037/0022-3514.69.5.797
Vygotsky, L. S. (2004). Imagination and creativity in childhood. Journal of Russian and East European Psychology, 42(1), 7–97. https://doi.org/10.1080/10610405.2004.11059210
Weiner, B. (1985). Attribution Theory. In Human Motivation (pp. 275–326). https://doi.org/10.1007/978-1-4612-5092-0_7
Williams, J. E., & Best, D. L. (1983). The Gough-Heilbrun Adjective Check List as a cross-cultural research tool. In J. B. Deregowski, S. Dziurawiec, & R. C. Annis (Eds.), Explications in cross-cultural psychology (pp. 164). Swets & Zeitlinger B.V.
Yang, Y., & Xu, H. (2025). Perception of AI Creativity: dimensional exploration and scale development. The Journal of Creative Behavior, 59(2). https://doi.org/10.1002/jocb.70028
Zampetakis, L. A. (2010). Unfolding the measurement of the creative personality. The Journal of Creative Behavior, 44(2), 105–123. https://doi.org/10.1002/j.2162-6057.2010.tb01328.x
Downloads
Published
How to Cite
Issue
Section
Categories
License
Copyright (c) 2026 Behdad Etezadi, Ira Vaidya
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
