Jul 2026· British Educational Research Journal· 0 citations· 30 references
TL;DR
The study identifies key themes in generative AI governance, including responsible experimentation, transparency, AI literacy and faculty discretion in policy implementation, including responsible experimentation, transparency, AI literacy and faculty discretion in policy implementation.
Abstract
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.
The findings suggest that academic integrity in the GenAI era is shifting from a primarily punitive model toward a pedagogy-first ecosystem that combines clear expectations, assessment redesign, equitable access to vetted tools, and iterative governance.
Yufeng Qian· International Journal for Ed...· 0 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
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
Regression analysis showed that AI familiarity, frequency of use, and policy awareness were significantly associated with stronger support for empowerment-oriented governance, which inform a five-pillar framework for responsible AI integration encompassing AI Literacy Integration, Stage-Based Access, Transparent Use Norms, Assessment Innovation, and Faculty Development.
A. Akib, Mohammad Aseer Intisar, Md. Sabbir Ahmed et al.· The Compass· 0 citations
Artificial intelligence (AI) literacy is being highlighted in education policy as essential due to the rapid incorporation of AI, particularly generative AI, in learning environments. This position paper argues that global AI literacy frameworks are not suitable for Ghana and similar contexts because they view AI literacy primarily as a curricular issue rather than a governance question. Using the Royal Society's framework as an example, it suggests that these models make assumptions about infrastructure and data governance that do not align with Ghanaian realities. The paper identifies blind spots regarding learner agency, data sovereignty, and platform dependence, proposing that AI literacy should be seen as a form of soft governance. It also advocates for ethno-AI literacy as a locally relevant alternative, prompting discussion on the risks of adopting global models that may hinder educational autonomy in the Majority World.
Dodzi Koku Hattoh, S. A. Addo, Abigail Oppong et al.· Annual Conference on Innovat...· 0 citations
Artificial intelligence (AI) is reshaping higher education worldwide, raising tensions between efficiency, equity, and autonomy. This paper examines these dynamics in South Africa and Kenya, two countries that illustrate distinct governance frameworks and infrastructural challenges within African higher education. Using qualitative document analysis of policy frameworks, scholarly literature, and institutional reports, the study investigates how AI integration offers opportunities for personalized learning, streamlined administration, and enhanced educational quality, while simultaneously exposing risks related to algorithmic bias, digital divides, and the erosion of student agency. The findings show that AI can improve efficiency and enrich student experiences, but without ethical safeguards it may reinforce existing inequalities and diminish learner autonomy. Through situating the analysis in South Africa and Kenya, the paper contributes to debates on AI in education by demonstrating that efficiency gains must be balanced with equity and autonomy considerations. The study concludes with recommendations for educators and policymakers on responsible AI adoption, emphasizing ethical literacy, inclusive infrastructure, and participatory approaches to ensure that technological innovation enhances rather than undermines social justice in higher education.
Mahlatse Sevhake, C. Hofisi· AI in Education· 0 citations