Aug 2026· OIDA International Journal of Sustainable Development· pp. 81· 0 citations· 15 references
TL;DR
The proposed GenAI Responsible Adoption Framework for Vietnamese Higher Education (GRAV-HE) comprises four interdependent dimensions: ethical policy and AI disclosure, data governance and secure infrastructure, pedagogical transformation, and comprehensive AI literacy.
Abstract
The rapid diffusion of generative artificial intelligence (GenAI) has created new opportunities and governance challenges for higher education, particularly in relation to academic integrity, assessment validity, data protection, equity, and AI literacy. In Vietnam, national strategies emphasize artificial intelligence and digital transformation, yet institutional-level guidance for the responsible use of GenAI in teaching, learning, and assessment remains fragmented. This paper develops a conceptual policy framework for responsible GenAI adoption in Vietnamese higher education. Using a conceptual policy analysis and framework development approach, the study synthesizes academic literature, international AI governance guidance, Vietnamese policy documents, and SDG 4 targets. The proposed GenAI Responsible Adoption Framework for Vietnamese Higher Education (GRAV-HE) comprises four interdependent dimensions: ethical policy and AI disclosure, data governance and secure infrastructure, pedagogical transformation, and comprehensive AI literacy. Rather than claiming to replace existing global guidance, GRAV-HE operationalizes Responsible AI and SDG 4 principles for institutional governance in a developing-country higher education context. The framework offers a practical basis for policy dialogue and future empirical validation in Vietnamese and comparable higher education systems.
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
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.
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 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.
Dr. Sixbert Sangwa, Dennis Ngobi, Emmanuel Ekosse et al.· Artificial Intelligence and...· 11 citations· ⚡1
Purpose: This paper develops a theory-informed conceptual framework for responsible AI entrepreneurship ecosystems in higher education. It addresses the limited integration of AI capabilities, entrepreneurship, institutional readiness, and governance in existing scholarship, particularly amid increasing AI adoption by higher education institutions (HEIs).
Methodology: The study adopts a conceptual research approach grounded in institutional, human capital, and entrepreneurial ecosystem theories. Relevant literature on AI, entrepreneurship, higher education, governance, and innovation ecosystems is synthesised to construct an integrated conceptual framework.
Results: The framework conceptualises responsible AI entrepreneurship as the intersection of AI capability, entrepreneurial innovation, ethical responsibility, and institutional governance. It identifies four interrelated dimensions: AI capability, entrepreneurial innovation, ethical and governance capability, and institutional readiness, and explains how leadership, pedagogy, technological infrastructure, governance systems, and ecosystem collaboration shape universities’ capacity to foster sustainable AI-driven innovation. Particular attention is given to challenges facing developing economies.
Novelty and Contribution: The study advances higher education scholarship by integrating AI capability development, institutional readiness, entrepreneurship, and ecosystem thinking into a unified conceptual model, providing a foundation for future empirical research on responsible AI entrepreneurship ecosystems.
Practical and Social Implications: The framework offers guidance for policymakers and university leaders seeking to strengthen AI governance, institutional readiness, and ecosystem collaboration. It supports the development of ethical, inclusive, and innovation-oriented higher education systems capable of preparing graduates and entrepreneurs for AI-driven economies.
Oluwatosin Omosolape Omodewu, M. Shokunbi· Elicit Journal of Economics...· 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
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.