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Article

Examining Generative AI Disruption: A Repeated Cross-Sectional Study of Faculty and Staff Sensemaking in Higher Education

1
Department of Multidisciplinary Engineering, Texas A&M University, College Station, TX 77843-3125, USA
2
Department of Mechanical and Industrial Engineering, Louisiana State University, Baton Rouge, LA 70803, USA
3
Department of Aerospace Engineering, Texas A&M University, College Station, TX 77843-3141, USA
*
Author to whom correspondence should be addressed.
Algorithms 2026, 19(8), 703; https://doi.org/10.3390/a19080703
Submission received: 17 July 2026 / Revised: 17 August 2026 / Accepted: 19 August 2026 / Published: 21 August 2026
(This article belongs to the Special Issue Artificial Intelligence in Education: Innovations and Implications)

Abstract

The rapid diffusion of generative artificial intelligence (GenAI) tools such as ChatGPT has unsettled established academic practices related to assessment, authorship, integrity, and disciplinary knowledge production. This qualitative repeated cross-sectional study examines how faculty and staff at a large public research university understood these changes in 2023 and 2024. The analysis draws on responses to the same open-ended survey question collected from independent respondent groups in 2023 (n = 104) and 2024 (n = 313). Responses were analyzed inductively through thematic analysis and subsequently interpreted using Disruptive Innovation Theory and Complex Adaptive Systems Theory. The analysis identified both continuity and change across the two datasets. Responses in 2023 emphasized uncertainty, threats to academic integrity, and defensive assessment redesign. Responses in 2024 more frequently described pedagogical experimentation, process-oriented assessment, and AI literacy as emerging academic and professional competencies. Concerns about authorship, equity, reliability, and inconsistent institutional guidance persisted across both years. The findings suggest that faculty and staff discourse shifted from primarily containing GenAI-related risks toward selectively integrating the technology into teaching and professional practice. However, because the study used independent cross-sectional samples, it does not establish individual change over time. The study contributes a theoretically informed account of institutional sensemaking during the first two years following ChatGPT’s public release and identifies strategies for balancing innovation, integrity, equity, and the human purposes of higher education.
Keywords: GenAI; disruption; faculty perceptions; higher education; qualitative research GenAI; disruption; faculty perceptions; higher education; qualitative research

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MDPI and ACS Style

Balart, T.; Raju, G.; Shryock, K.J. Examining Generative AI Disruption: A Repeated Cross-Sectional Study of Faculty and Staff Sensemaking in Higher Education. Algorithms 2026, 19, 703. https://doi.org/10.3390/a19080703

AMA Style

Balart T, Raju G, Shryock KJ. Examining Generative AI Disruption: A Repeated Cross-Sectional Study of Faculty and Staff Sensemaking in Higher Education. Algorithms. 2026; 19(8):703. https://doi.org/10.3390/a19080703

Chicago/Turabian Style

Balart, Trini, Gibin Raju, and Kristi J. Shryock. 2026. "Examining Generative AI Disruption: A Repeated Cross-Sectional Study of Faculty and Staff Sensemaking in Higher Education" Algorithms 19, no. 8: 703. https://doi.org/10.3390/a19080703

APA Style

Balart, T., Raju, G., & Shryock, K. J. (2026). Examining Generative AI Disruption: A Repeated Cross-Sectional Study of Faculty and Staff Sensemaking in Higher Education. Algorithms, 19(8), 703. https://doi.org/10.3390/a19080703

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