Jul 2026· International Journal of Studies in Inclusive Education· 0 citations
Abstract
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.
Generative AI tools have entered university life faster than institutions have been able to govern them, prompting policy responses that range from outright prohibition to largely unguided adoption. This paper examines the shift from restrictive to empowerment-oriented AI governance in higher education through a convergent parallel mixed-methods study comprising a structured survey (n = 71) and twenty-five semi-structured interviews across eleven academic disciplines at a private university in Bangladesh. The study documents patterns of AI adoption, stakeholder attitudes toward institutional policy, and barriers to responsible use. Participants situated within more empowerment-oriented institutional environments, including those characterised by AI literacy support, clearer ethical guidance, and stronger faculty engagement, reported greater confidence in using AI responsibly and clearer understanding of acceptable practices. By contrast, participants operating under restriction-only policies more often described uncertainty, confusion, and rule evasion. Regression analysis further showed that AI familiarity, frequency of use, and policy awareness were significantly associated with stronger support for empowerment-oriented governance. These findings inform a five-pillar framework for responsible AI integration encompassing AI Literacy Integration, Stage-Based Access, Transparent Use Norms, Assessment Innovation, and Faculty Development. Informed by stakeholder evidence and refined in dialogue with existing literature, the framework offers a practical model for institutions navigating AI governance in resource-constrained contexts. The paper contributes empirical evidence from a developing-country setting that remains underrepresented in current scholarship and highlights how responsible AI governance can support more equitable and context-sensitive higher education.
A. Akib, Mohammad Aseer Intisar, Md. Sabbir Ahmed et al.· The Compass· 0 citations
As the education sector attempts to address rapid changes caused by Artificial Intelligence (AI), it becomes crucial to examine approaches to leadership at the organizational and system levels. This scoping review explores how anticipatory models of governance are conceptualised and operationalised globally within higher education settings in the context of AI-related transformations. Using a scoping review design, academic and grey literature published between 2020 and 2025 was searched across Scopus, EBSCOhost, ERIC, Google Scholar, and ProQuest. Nineteen sources were selected based on clear inclusion and exclusion criteria and analysed thematically. Findings revealed that while AI offers promising opportunities to transform governance in higher education, the ability of institutions to leverage these opportunities 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. However, a gap between theory and implementation remains evident, particularly due to weak policy frameworks and limited digital infrastructure in the Global South, including the Arab world, Sub-Saharan Africa, and Southeast Asia.
S. Baroudi· International Journal of Edu...· 0 citations
As generative AI becomes increasingly integrated into higher education, institutions face challenges in balancing its benefits with potential risks, including plagiarism, misinformation, bias and privacy concerns. This study examines the policies and guidelines on generative AI usage implemented by leading US universities ranking based best colleges in the world in the US News List using a qualitative document analysis approach. The study identifies key themes in generative AI governance, including responsible experimentation, transparency, AI literacy and faculty discretion in policy implementation. While universities recognise the potential of AI to enhance learning and research, policies vary significantly across regions, with European institutions adhering to stricter regulations and US universities granting faculty greater flexibility. The results also provide valuable insights for policymakers and educators seeking to develop responsible AI governance frameworks in higher education.
Abdullah Alotaibi, A. Aseery, Abdulaziz A. Alfayez et al.· British Educational Research...· 0 citations
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.
Dr. Sixbert Sangwa, Dennis Ngobi, Emmanuel Ekosse et al.· Artificial Intelligence and...· 11 citations· ⚡1
Background
Research-policy partnerships are widely used to support evidence-informed reform, yet how evidence actually shapes policy and practice in low-income education systems remains poorly understood. This article asks how, and under what relational and institutional conditions, evidence generated by Research on Improving Systems of Education (RISE) Ethiopia contributed to national policy on equity, learning and accountability.
Methods/data
The study uses qualitative data, drawing on 22 semi-structured interviews with senior federal policy makers, regional officials, development partners and local non-governmental organisations (NGOs), complemented by documentary analysis of RISE outputs, education sector plans and policy materials.
Approach
Interview transcripts and documents were analysed using a thematic analysis framework, combining deductive coding organised around bounded mutuality, sustained interactivity and policy adaptability with inductive coding of emergent evidence-to-policy contribution pathways.
Results
RISE evidence contributed to the bottom-up design of the four-year Education Transformation Programme and its implementation vehicle, Education Transformation Operation for Learning (2025-2029), supported a reframing of national discourse from schooling expansion towards foundational learning, helped develop an equity narrative grounded in observed learning gains among disadvantaged students within a school year, and informed COVID-19 school-reopening deliberations. Contributions were clearest where trusted relationships, timely decision windows and actionable evidence converged; where any of these elements was weak, policy impact was correspondingly less direct.
Conclusion
The article shows that policy contribution was enabled by relational conditions that allowed evidence to be heard, trusted and acted upon. It offers a transferable framework for embedding research in policy systems and underscores the need for sustained institutional investment in the capacity for evidence use.
M. Araya, Pauline Rose, D. Tiruneh et al.· Evidence & Policy: A Journal...· 0 citations
This study examines how policy silence functions as a governance mechanism in the context of generative artificial intelligence (AI) in K–12 education. While existing research has focused on how districts regulate or integrate AI, far less attention has been given to what happens when formal guidance is delayed, incomplete, or unresolved. Drawing on a single-district qualitative case study of a Texas public school district, the analysis uses interviews and document analysis as primary data sources, while student survey data provide descriptive context regarding patterns of AI access and guidance. Findings show that policy silence actively redistributes interpretive authority to educators and school leaders, shifting responsibility for ethical and instructional decision-making onto individual classrooms without corresponding institutional support. This redistribution produces uneven enactment and may contribute to disparities in student access, guidance, and learning opportunities. Applying Critical Policy Analysis and Jencks’ framework of educational opportunity, the study shows that policy silence is not the absence of governance but a governance choice, one that shapes how access, responsibility, and fairness are determined. Equitable AI integration requires policies that pair clarity with support for professional judgment, positioning AI governance as central to contemporary school improvement.
A. Miles, Khalid H. Arar· Improving Schools· 0 citations