Date of Defense

4-17-2025

Date of Graduation

4-2025

Department

Communication

First Advisor

Brian Gogan

Second Advisor

Keith Hearit

Third Advisor

Anna Popkova

Abstract

Background: Emerging in 2022 with ChatGPT, generative AI has seen an unprecedented adoption for US professionals, with a 2023 survey showing that half of US professionals already used ChatGPT at work (Bedington et al., 2024). However, much of the public perceive generative AI as a nebulous or risky technology, with expressed concerns including disruptions to workflows and overwhelming industry adopters (Cummings et al., 2024). Nevertheless, there is also clearly defined potential surrounding AI, such as benefits to student populations and scholarly writing (Knowles, 2024). Moreover, much of this potential has begun being realized in the public relations industry by adding supplementary dimensions to previously-developed workflows (Jeong & Park, 2023). In the context of this study, literature has also been divided by the main findings thereof, including authenticity, privacy, and expertise. Authentic content should accurately depict an author’s intent or belief (Rice, 2021), which generative AI has clashed with by generating content for the author (Deptula et al., 2024). Privacy concerns are also highlighted as AI use causes users to lose informational control (Gupta et al., 2023). Also, expertise is a long-valued facet of professional expectations, but some scholars wonder if generative AI has shifted the kinds of expertise that are valued (Ferrario et al., 2024).

Objective: This work describes and explores the experiences of public relations professionals as the industry has begun widely adopting generative AI. This is a niche of the literature in regards to AI that is currently lacking, and expanding upon this niche will promote greater understanding of public relations use cases for generative AI. That understanding in turn can allow generative AI and public relations to mutually adapt to one another, benefiting both industries simultaneously as they expand their capabilities to compliment one another. Further exploration of professional experiences, being organized by the main findings of authenticity, privacy, and expertise, allows in-depth, industry-specific takeaways to be developed from this study.

Methods: First, 30 professionals had contact information compiled from various sources to create a comprehensive list of potential respondents. Second, 10 of those professionals were recruited via pre-scripted emails explaining the content and intent of the study. Third, interviews were mutually scheduled between November 25, 2024, and January 16, 2025. Fourth, interviews were conducted and recorded via auto-generated transcriptions. Finally, transcriptions were polished and analyzed manually using semantic fields to determine anchor and minor themes.

Abstract References

Bedington, A., Halcomb, E. F., McKee, H. A., Sargent, T., & Smith, A. (2024). Writing with generative AI and human-machine teaming: Insights and recommendations from faculty and students. Computers & Composition/Computers and Composition, 71, 102833. https://doi.org/10.1016/j.compcom.2024.102833

Cummings, R. E., Monroe, S. M., & Watkins, M. (2024). Generative AI in first-year writing: An early analysis of affordances, limitations, and a framework for the future. Computers & Composition/Computers and Composition, 71, 102827. https://doi.org/10.1016/j.compcom.2024.102827

Deptula, A., Hunter, P. T., & Johnson-Sheehan, R. (2024). Rhetorics of Authenticity: Ethics, ethos, and Artificial intelligence. Journal of Business and Technical Communication. https://doi.org/10.1177/10506519241280639

Ferrario, A., Facchini, A., & Termine, A. (2024). Experts or authorities? The strange case of the presumed epistemic superiority of artificial intelligence systems. Minds and Machines, 34(3). https://doi.org/10.1007/s11023-024-09681-1

Gupta, M., Akiri, C., Aryal, K., Parker, E., & Praharaj, L. (2023). From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy. IEEE Access, 11, 80218–80245. https://doi.org/10.1109/access.2023.3300381

Jeong, J. Y., & Park, N. (2023). Examining the Influence of Artificial Intelligence on Public Relations: Insights from the Organization-Situation-Public-Communication (OSPC) Model. Asia-pacific Journal of Convergent Research Interchange, 9(7), 485–495. https://doi.org/10.47116/apjcri.2023.07.38

Knowles, A. M. (2024). Machine-in-the-loop writing: Optimizing the rhetorical load. Computers & Composition/Computers and Composition, 71, 102826. https://doi.org/10.1016/j.compcom.2024.102826

Rice, J. (2021). Authentic Writing. University of Pittsburgh Press.

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Honors Thesis-Restricted

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