Jul 2026· Academic Journal of Research and Scientific Publishing· 0 citations
TL;DR
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.
Abstract
This study aimed to provide a comprehensive review of generative AI governance in higher education during the period 2022–2026, in light of the rapid expansion of the use of big language models and content generation applications within universities. The study adopted a comprehensive scope review methodology, guided by the SALSA framework for the research, evaluation, synthesis, and analysis phases, and by PRISMA-ScR guidelines for documenting the review procedures. The study analyzed peer-reviewed scientific literature, sectoral, institutional, and regulatory frameworks, and selected university policies, with particular attention to the Arab context and research and regulatory gaps. The results showed that generative AI governance is not limited to addressing issues of cheating and plagiarism, but encompasses interconnected dimensions, most notably: academic integrity, privacy, transparency, disclosure, fairness, capacity building, human oversight, risk management, and assessment redesign. The study also indicated that the global trend is moving toward responsible and conditional use rather than outright prohibition or unregulated adoption. 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. It also revealed the need for more specialized policies that address privacy, linguistic equity, data protection, and the transparency. The study recommends developing flexible university guidelines, disclosure models, data use controls, ongoing training programs, and periodic review mechanisms that ensure a balance between innovation, quality of learning, and academic integrity.
The rapid adoption of generative artificial intelligence (GenAI) in higher education has outpaced institutional readiness, creating urgent ethical and regulatory challenges that threaten academic integrity, data privacy, and educational equity. While global frameworks like UNESCO’s Guidance for Generative AI in Education (2023) advocate for human-centric design, and national laws such as FERPA mandate student data protection, no existing model systematically integrates these domains into a cohesive governance structure. This study addresses this critical gap by proposing the Ethico-Regulatory Governance (ERG) Framework, a conceptual model designed to bridge global ethics with local compliance. Developed through a systematic synthesis of 68 peer-reviewed studies, policy documents, and institutional guidelines, the ERG Framework consists of four interlocking layers: Foundational Principles (UNESCO values), Regulatory Anchors (FERPA/GDPR alignment), Institutional Mechanisms (audits, disclosure, training), and Pedagogical Integration (process-based assessment, prompt engineering). The framework transforms abstract principles into actionable practices, enabling institutions to move beyond reactive policies toward proactive, accountable governance. Key findings demonstrate that effective AI integration requires not only technical oversight but also stakeholder co-design, bias mitigation, and continuous feedback loops. By operationalizing ethics through enforceable mechanisms, the ERG Framework offers a scalable, adaptable solution for universities navigating the complexities of GenAI. Its implementation can safeguard core academic values while fostering innovation, ensuring that AI serves as a partner—not a replacement—for human judgment in teaching, learning, and research.
Christian Roberto Cabezas Freire, Nayana Desai· Revista Hambatu Science· 0 citations
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· 2 citations
It is concluded that in order for undergraduate education to continue to be relevant in a society where AI is pervasive, governance must change toward process-oriented evaluation and relational originality.
Joe Mutebi, Brian Mugisha, Ibrahim Adabara et al.· F1000Research· 0 citations
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...· 1 citation
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.
Abdullah Alotaibi, A. Aseery, Abdulaziz A. Alfayez et al.· British Educational Research...· 0 citations
The 2030 deadline for the United Nations Agenda for Sustainable Development is approaching. At the present universities’ field reveals a visible tension between stated commitment to sustainability and the structural change to fulfill it. Despite the institutional strategies and curriculum reform thoroughly examined by the literature, less attention has been given to the epistemic assumptions underlying indicator-driven sustainability agendas and to the ways managerial university governance may constrain more impactful approaches to sustainability. This study has two main aims: to examine how the SDGs are understood and used in teaching, research, and governance, including the structural and epistemic barriers reported by respondents; and to assess support for more critical and justice-oriented approaches to sustainability in higher education. Based on a cross-sectional survey of 54 academics recruited through transnational COST Action networks, the study combines descriptive statistics with thematic analysis of open-ended responses. Given the network-based sample, the findings are exploratory. They suggest that institutional engagement with the SDGs is often uneven and formed by existing organizational logics. A substantial majority of respondents support critical reformulation of the SDGs rather than unreflective agreement, pointing to a persistent tension between technocratic approaches to sustainability and broader consideration with epistemic justice. The findings also indicate that a more consequential approach on sustainability in higher education may require stronger recognition of Indigenous, local, community-based and intergenerational knowledge, as well as greater institutional capacity for critical and reflexive engagement beyond metric alignment.
Ivo Veiga, Diogo Guedes Vidal, Marie Rollo et al.· Open Research Europe· 0 citations