Ethical Leadership and Governance in Higher Education Policy: Examining Generative AI Guidelines at Deakin University.

New Research Release: Ethical Leadership and AI Governance in Higher Education I am pleased to announce my latest scholarly work: Ethical Leadership and Governance in Higher Education Policy: Examining Generative AI Guidelines at Deakin University. This research examines how institutional policies articulate leadership responsibilities, define researcher obligations, and structure accountability in response to the increasing use of generative artificial intelligence in academic settings. Based on Ethical Leadership Theory (Brown, Treviño, & Harrison, 2005) and a qualitative analysis of policy language, the study evaluates how Deakin University’s Generative AI Policy communicates expectations for professional conduct and ethical decision-making. The research is organized around four core areas of inquiry: 1. Leadership Responsibility in Ethical AI Use The study examines how university leaders are expected to model integrity and fairness in their oversight of AI-related research and teaching practices. The policy assigns specific responsibilities to faculty and research supervisors, requiring them to promote ethical conduct and ensure that students understand the standards governing AI use (Chan, 2023; An, Yu, & James, 2025). 2. Institutional Accountability and Governance Structures The analysis considers how the policy defines institutional roles in monitoring compliance and supporting ethical research. While the document affirms shared responsibility, it lacks detailed protocols for enforcement, which may result in inconsistent interpretation across departments (Ullah, Naeem, & Kamel Boulos, 2024; Ghimire & Edwards, 2024). 3. Researcher Conduct and Professional Standards The policy outlines expectations for transparency in research outputs, including the disclosure of AI-assisted contributions. Researchers are required to adhere to existing codes of conduct and seek clarification when ethical boundaries are unclear. These provisions reinforce the importance of maintaining academic integrity and institutional credibility (Luo, 2024; Yusuf, Pervin, & Román-González, 2024). 4. Policy Clarity and Enforcement Gaps The study identifies areas where the policy lacks specificity, particularly in defining leadership roles and enforcement protocol. This absence of protocol explanation may hinder consistent application and reduce confidence in institutional oversight (Shata & Hartley, 2025; Sustaningrum & Haldaka, 2025). This study shows that ethical governance in environments involving generative AI depends on leadership that implements expectations with fairness, communicates responsibilities clearly, and supports faculty and researchers in upholding professional conduct. The findings contribute to ongoing efforts to improve institutional policies and leadership practices in higher education.