Aug 2026· American Journal of Education and Technology· 0 citations· 24 references
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.
This study synthesizes fragmented research on artificial intelligence (AI) in higher education governance and identifies key gaps for future research and policy and provides a useful lens for interpreting institutional adaptation.
Xinyi Jiang, Zuraidah Abdullah· Frontiers in Education· 3 citations
As artificial intelligence (AI) becomes increasingly embedded in higher education, empirical evidence on how institutional governance shapes its equitable and responsible implementation in South African universities remains limited. This study examined how institutional policies and governance practices influence the implementation and equitable use of AI in undergraduate education while contributing to international discourse on responsible AI governance. Guided by an interpretivist research paradigm, the study adopted a qualitative approach and employed a single-case study design within one public university in South Africa. Participants comprised university leaders, academic staff, professional support staff, and undergraduate students involved in or affected by AI governance and implementation. Data were collected through semi-structured interviews, focus group discussions, and document analysis and analysed using thematic analysis supported by inductive coding. The findings indicate that limited policy transparency, context-insensitive governance frameworks, unequal access to AI technologies, and weak institutional accountability can reinforce educational inequalities, particularly among first-generation and under-resourced students. Conversely, participatory governance, transparent decision-making, stakeholder engagement, and enhanced digital literacy promote more equitable and responsible AI implementation. The study proposes a multi-level governance model integrating institutional policy, stakeholder participation, and pedagogical practice to strengthen equitable AI adoption. It concludes that higher education institutions should develop context-sensitive AI governance frameworks, strengthen institutional capacity, and expand equitable access to AI technologies to advance fairness, inclusion, and responsible AI implementation.
R. Lumadi· International Journal of Stu...· 0 citations
This conceptual paper argues that traditional technology governance models are insufficient and that leaders must develop an augmented moral compass to navigate algorithmic decision-making, data governance, and equity and proposes an original Moral Compass Model.
The ability of institutions to leverage opportunities to transform governance in higher education depends on adopting anticipatory governance models that emphasize foresight and stakeholder engagement, as well as adopting changes to the traditional role of both leaders and educators to become data literate, inclusive, collaborative, and forward-thinking.
S. Baroudi· International Journal of Edu...· 2 citations
This mixed-methods study integrates survey data from 542 leaders across health care, finance, government, education, and nonprofits with twenty-two interviews to examine how digital literacy and ethical infrastructure shape trust in AI-mediated decision systems.
Ajmal Aminee· Organizational Cultures An I...· 0 citations
Generative artificial intelligence (GenAI) has emerged as a significant governance concern for higher education institutions, necessitating attention to responsible technology use, organisational capability, governance structures, resources, and ongoing oversight. This study investigates the institutionalisation of GenAI governance through a management-oriented qualitative document analysis of three documents from a focal higher education institution. Using the Technology–Organization–Environment (TOE) framework, the study identifies governance commitments articulated in the institution's AI policy and examines the technological, organisational, and environmental conditions influencing these commitments. The analysis employs a 13-construct TOE codebook to examine the institution's GenAI policy, the establishment of its responsible AI committee, and an earlier memorandum on AI in teaching and learning. The results demonstrate that the institution's governance response evolved through a progression from capability development to the establishment of formal governance structures and policy formalisation. Technological conditions created opportunities and practical requirements for GenAI governance, while organisational conditions—particularly leadership, structure, competence, resources, and readiness—were most extensively documented. Environmental conditions primarily defined regulatory and institutional boundaries. The findings indicate that GenAI governance becomes institutionalised not solely through policy formulation, but through organisational capacity to translate technological change into assigned responsibilities, coordinated structures, and sustained oversight. This study advances higher education management and AI governance by extending the TOE framework to institutional governance and by identifying a capability–structure–formalisation pattern for understanding how AI governance becomes embedded within university organisational arrangements.
Unknown authors· Journal of global economics,...· 0 citations
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