The study proposes a phased, ethically grounded governance framework tailored to Africa’s educational context, contributing new insights into readiness differentials, governance diffusion, and policy convergence, offering a foundation for inclusive, future-oriented AI policy in African higher education.
Abstract
As artificial intelligence (AI) reshapes global education systems, African higher education institutions (HEIs) face pressure to adopt and govern AI ethically and effectively. This study investigates five questions: (1) What is the status of AI governance in African HEIs? (2) How ready are institutions to adopt AI policy? (3) What ethical and operational risks are emerging? (4) How do institutional and national AI strategies align? and (5) What future-proof governance framework can be proposed? Using a desk-based meta-synthesis, the study analyzes over 30 publicly available institutional, national, and regional documents. The analysis is guided by Resource Dependence Theory, Diffusion of Innovation, and Complexity Theory. No human subjects were involved, and international ethical standards were followed; PRISMA guidelines were deemed not applicable due to the qualitative scope. Findings reveal wide disparities in policy development and readiness. South Africa, Nigeria, and Rwanda are early adopters, aligning institutional policies with national digital strategies (RQ1 & RQ4). Most institutions remain in aspirational phases, limited by infrastructure and human capacity (RQ2). Where policies exist, they emphasize academic integrity and responsible use, though enforcement is uneven and impact largely unevaluated (RQ3). Cross-national alignment varies, with regional frameworks lacking strong enforcement mechanisms (RQ4). Evidence suggests that institutions with structured policies invest more in training and faculty engagement (RQ2 & RQ5). The study proposes a phased, ethically grounded governance framework tailored to Africa’s educational context (RQ5). It contributes new insights into readiness differentials, governance diffusion, and policy convergence, offering a foundation for inclusive, future-oriented AI policy in African higher education.
This conceptual article examines the conditions under which AI-assisted decisions in education and public governance can strengthen institutional capacity without displacing human judgement, agency, or accountability. It employs a purposive conceptual synthesis of interdisciplinary scholarship and legal and policy materials and compares governance approaches in the European Union, the Republic of Korea, and the United States with regard to legal force, risk classification, human oversight, transparency, contestability, and institutional capacity. Rather than treating the technical system alone as the unit of ethical analysis, the article focuses on the AI-assisted decision episode: the sequence through which data, model outputs, human judgement, and institutional authority combine to affect a learner, citizen, or community. On this basis, it develops a six-element governance framework comprising legitimate purpose and proportionality; explicit allocation of roles and responsibility; traceable data, evidence, and uncertainty; competent human oversight and calibrated reliance; stakeholder participation, contestability, and redress; and continuous monitoring, audit, and institutional learning. Applied to educational assessment and public-service decisions, the framework demonstrates that a nominal human-in-the-loop is insufficient unless reviewers possess the competence, time, authority, alternative evidence, and records necessary to challenge model outputs. The article’s contribution lies in connecting legal safeguards, organisational capacity, and cognitive risks within a process-based model of human-centred AI. The framework is conceptual and requires empirical validation. Human-centred AI ultimately depends not only on technical accuracy but also on a decision architecture that preserves agency, provides effective remedies, and keeps responsibility visible.
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
The rapid integration of artificial intelligence (AI) in higher education presents both transformative opportunities and major governance challenges. Many institutions still lack structured mechanisms to ensure responsible and coordinated implementation. This study identifies and prioritizes key design principles for institution-wide AI integration in Malaysian higher education through the Fuzzy Delphi Method (FDM). A panel of twelve experts assessed proposed principles organized across five socio-technical domains: Governance and Strategy, Technology and Infrastructure, Data and Analytics, Ethics, Risk and Compliance and Change Management and Workforce Enablement. Consensus was established using a threshold value of d ≤ 0.2 with a minimum agreement rate of 75%. All five domains achieved consensus, with Governance and Strategy emerging as the highest priority, followed by Data and Analytics, Ethics, Risk and Compliance, Technology and Infrastructure and Change Management and Workforce Enablement. These findings suggest that sustainable AI adoption requires institutions to first establish robust governance structures, ensure data readiness and embed ethical safeguards before expanding technological investments. This study contributes a validated, prioritized socio-technical framework offering evidence-based guidance for university leaders, with particular relevance for resource-constrained higher education contexts in Malaysia and the broader Global South.
Nurul Aisyah Kamrozzaman, N. Muhiddin, Washima Che Dan et al.· International Research Journ...· 0 citations
The study concluded that effective governance requires combining research evidence, sectoral frameworks, and institutional policies, while translating general principles into clear procedures at the university and course levels, and revealed the need for more specialized policies that address privacy, linguistic equity, data protection, and the transparency.
Mohammed Al Mutawtah· Academic Journal of Research...· 0 citations
The framework demonstrates that the sustainable value derived from AI in higher education depends less on the level of the technology adopted than on the ethical bases and consistency of the leadership responsibility for its integration, offering higher education leaders and policymakers a structured path toward responsible AI governance and sustainable institutional transformation.
Asem S. Obied, Ahmed Raja Haj Ali· Frontiers in Education· 0 citations
Higher education is undergoing rapid transformation driven by artificial intelligence (AI), creating significant opportunities for sustainable development alongside complex governance and ethical challenges. Existing scholarship on AI in higher education can be broadly categorized into SDG-aligned frameworks, principle-based governance models, and value-based approaches; however, these strands remain insufficiently integrated at the institutional level and lack operational clarity across the AI lifecycle, particularly in procurement and post-deployment audit stages. This study examines how maqāṣid al-sharīʿah can be systematically operationalized as a framework for sustainable AI governance in Islamic higher education, and how such an approach supports socially sustainable and ethically aligned digital transformation under Saudi Vision 2030. The study adopts a systematic mapping approach with integrative synthesis, analyzing 143 peer-reviewed studies retrieved from Scopus, Web of Science, and ERIC (2015–2026). A combined deductive–inductive coding framework identifies structural gaps between ethical principles and institutional governance practices across four AI lifecycle stages: procurement, design, deployment, and audit. The findings reveal a persistent principle–implementation gap most pronounced in procurement and audit processes. In response, the study develops a maqāṣid-based governance framework that translates core ethical objectives into measurable, sustainability-oriented indicators embedded within lifecycle decision points. The framework is structured as a prototype institutional scorecard enabling university administrators and policymakers to evaluate AI adoption within risk management and decision-making workflows. The proposed model advances social sustainability by promoting accountability, equity, and culturally coherent AI integration, offering a structured pathway for aligning institutional AI practices with the strategic priorities of Saudi Vision 2030.
