AI Managerialism in Higher Education: Algorithmic Governance, Shared Authority, and the Ethics of Institutional Control
TL;DR
It is concluded that effective AI governance requires institutionally grounded arrangements that ensure AI supports, rather than undermines, the educational mission of higher education.
Abstract
The rapid integration of artificial intelligence (AI) into higher education is transforming how universities are governed, managed, and held accountable. While existing scholarship has focused primarily on the pedagogical applications of AI and the ethical implications of algorithmic technologies, less attention has been devoted to how AI reshapes institutional governance and decision-making processes. Addressing this gap, this paper advances the concept of AI managerialism to explain the growing influence of algorithmic systems on university governance and organizational control.The study employs a critical narrative review and conceptual policy analysis, synthesizing scholarship on AI governance, managerialism, and higher education administration. It further examines three purposively selected cases representing key domains of algorithmic governance: the Ofqual algorithm controversy in the United Kingdom, Purdue University’s Course Signals learning analytics system, and the University of Sydney’s response to generative AI. Through cross-case thematic analysis, the study identifies recurring governance issues related to accountability, transparency, participation, and institutional autonomy.Findings suggest that AI-enabled systems can improve administrative efficiency, predictive capacity, and evidence-informed decision-making while simultaneously generating risks associated with opacity, surveillance, stakeholder exclusion, and the centralization of managerial authority. In response, the paper proposes an Ethical AI Governance Framework for Higher Education built on five principles: mission alignment, transparency and explainability, participatory governance, equity auditing, and bounded scope. Extending existing AI ethics frameworks, the model explicitly incorporates institutional mission, shared governance, and organizational accountability into AI oversight processes. The framework provides practical guidance for university leaders and policymakers seeking to balance technological innovation with academic values and democratic governance. The paper concludes that effective AI governance requires institutionally grounded arrangements that ensure AI supports, rather than undermines, the educational mission of higher education.