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Inhuman Rhetoric: Generative AI and Crisis Communication
Abstract
This article considers the rhetorical risks of using generative AI to compose organizational communication during crises or in the aftermath of tragedies. It focuses on a case study in which representatives of Vanderbilt University’s Peabody College of Education and Human Development disclosed their use of ChatGPT to write a response to a school shooting at another university. The author argues that although generative AI can often be useful in technical and professional communication, it can also undermine perceptions of “rhetorical humanity” if its use is disclosed or discovered, making it rhetorically risky in certain contexts. Thus, knowing when not to utilize AI is an important aspect of AI literacy for practitioners.
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“However, even when guided by frameworks such as SCCT, the effects of AI disclosure on how messages are received and understood by stakeholders remain uncertain.”
From Artificial Intelligence in Crisis Communication: Investigating Source Effects and Matched Crisis Responses on Stakeholder Perceptions (2026) · cites this paper as Piller 2025
In the citing paper
“Scholars have raised concerns that revealing AI involvement may alter perceptions of the humanity and authenticity of organizational responses (Piller 2025), thereby shaping judgments about the credibility and reputation of the organization.”
From Artificial Intelligence in Crisis Communication: Investigating Source Effects and Matched Crisis Responses on Stakeholder Perceptions (2026) · cites this paper as Piller 2025
In the citing paper
“While these studies show that individuals' perceptions of the source of a message shape how they evaluate the communication content from the source, it is not clear to what extent these findings apply to AI-developed crisis communication, such that disclosing the use of AI in writing can fundamentally alter how audiences perceive the authenticity of the message (Piller 2025).”
From Artificial Intelligence in Crisis Communication: Investigating Source Effects and Matched Crisis Responses on Stakeholder Perceptions (2026) · cites this paper as Piller 2025
In the citing paper
“However, while these guidelines address broad ethical considerations, public perceptions of AI usage, especially in sensitive or high-risk contexts such as crisis communication, can differ significantly depending on whether and how AI's role is disclosed (Piller 2025).”
From Artificial Intelligence in Crisis Communication: Investigating Source Effects and Matched Crisis Responses on Stakeholder Perceptions (2026) · cites this paper as Piller 2025
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Research works printed but not linked: 19
- Adiwardana D., 2020, ArXiv
- Appiah K. A., New York Times
- Beccue M., Manifesto: For humanity, the case against generative AI writing
- 10.1177/1050651910389147 · doi:10.1177/1050651910389147
- Card D., Intercom
- 10.1177/23294906221074311 · DOI
- Ghosal S. S., 2023, ArXiv
- 10.1080/03637751.2010.502536 · doi:10.1080/03637751.2010.502536
- 10.1111/j.1460-2466.2008.01410.x · DOI · Paper page
- 10.1111/j.1083-6101.2006.00019.x · DOI · Paper page
- 10.1108/JPBM-02-2022-3864 · DOI
- Lele A., Vanderbilt Hustler
- Markel M., 2021, Technical communication
- Moschella D., 2023, Defending Digital, 19, 1
- 10.1177/1077699014538827 · DOI
- Perrotta R., Vanderbilt Hustler
- Ulmer R. R., Effective crisis communication: Moving from crisis to opportunity
- Wu D., The Washington Post
- Yeats W. B., 1996, The collected poems of W. B. Yeats, 80
Cited by
Schneider, E., Kyriakopoulos, V., Seo, Y., Hong, N., Lee, M., & Lim, J. S. (2026) · Corporate Reputation Review · doi:10.1057/s41299-026-00254-9 · Cited by 4
Metadata
- Authors
- Piller, E.
- Venue
- Journal of Business and Technical Communication
- Publisher
- SAGE Publications
- Published
- 2024-09-28
- Volume
- 39
- Issue
- 1
- Pages
- 42-50
- ISSN
- 1050-6519
- 1552-4574
- Cited by
- 16
- References
- 0
- Language
- en
- Licence
- journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
