Higher Education Leadership Guided by Path–Goal Theory Addressing Systemic Bias in AI-Powered Practices of Teaching and Learning

The integration of artificial intelligence (AI) into higher education has reshaped teaching and learning through adaptive platforms, predictive analytics, automated grading, and administrative decision-making. While these technologies offer efficiency and personalization, they also present risks of systemic bias that threaten equity, fairness, and institutional integrity. Addressing these risks requires leadership strategies that extend beyond technical solutions to include ethical governance and accountability. This qualitative archival research examines how higher education leadership guided by Path–Goal Theory addresses systemic bias in AI-powered teaching and learning. Path–Goal Theory, developed by House (1971), emphasizes directive, supportive, participative, and achievement-oriented leadership behaviors that clarify goals, remove barriers, and align institutional practices with follower needs. Archival analysis of institutional documents from Harvard University, Maricopa Community Colleges, and the University of Southern Maine demonstrates that higher education leadership employs these behaviors to varying degrees in establishing AI governance. Findings indicate that directive leadership provides clear policies and accountability, supportive leadership aids faculty adaptation, participative leadership engages diverse stakeholders, and achievement-oriented leadership promotes ethical innovation. However, gaps remain in the balanced application of these behaviors, particularly in sustaining supportive and achievement-oriented practices. This study contributes to higher education leadership by linking Path–Goal Theory to the ethical governance of AI, offering a framework for leaders to mitigate systemic bias while advancing institutional missions. Implications highlight the need for professional development, inclusive stakeholder engagement, continuous review, and goal-setting practices that ensure AI adoption in higher education remains equitable, responsible, and mission-driven.