Faculty Leadership and the Unregulated Use of Artificial Intelligence in Higher Education

Faculty Leadership and the Unregulated Use of Artificial Intelligence in Higher Education Artificial intelligence is now a regular feature of higher education, yet faculty leaders often rely on it privately without disclosure. This presentation examines how unregulated AI use by faculty leaders creates ethical concerns for fairness, accuracy, and credibility in teaching, research, and evaluation. Key Findings Faculty leaders may plagiarize AI-generated content, misrepresenting authorship and diminishing trust. Selective use of AI in data analysis can distort evidence and compromise research integrity. Bypassing peer review allows errors and false citations to pass unchecked. Ghostwriting through AI removes transparency in authorship and reliability of instructional materials. Undisclosed AI grading compromises fairness and misleads students about evaluation standards. Intellectual theft occurs when AI reproduces existing work without attribution. Fabricated references introduce false sources into academic work. Inflated productivity metrics misrepresent faculty contributions in promotion and tenure evaluations. Leadership Frameworks Applied Leadership Ethics (Ciulla, 2020): Establishes the importance of consistency across public and private decisions. Ethical Leadership (Brown, Treviño, & Harrison, 2005): Emphasizes fairness, honesty, and responsibility in faculty practices. Resick, Hanges, Dickson, & Mitchelson (2006): Clarifies that ethical behavior must remain consistent in both disclosed and undisclosed actions. Proposed Leadership Model Require disclosure of AI use in teaching, research, and evaluation. Establish faculty review processes to verify accuracy of AI-generated content. Create institutional guidance that ensures accountability for private AI use. Develop training programs to prepare faculty leaders for responsible AI practices. Institutional Implications Strengthens trust between faculty and students. Increases transparency in grading, authorship, and research outputs. Protects credibility of academic programs. Supports fairness in promotion and tenure evaluations. This analysis builds on recent studies identifying ethical risks in unregulated AI use (Brown, Treviño, & Harrison, 2005; Resick et al., 2006; Reina Marín et al., 2025; Nguyen, 2025). By applying leadership frameworks, the study clarifies how institutions can respond to these challenges with integrity and responsibility.