Governing generative AI in higher education: a global Delphi study on policy and practice
A Global Framework for GenAI Governance in Higher Education
This Delphi study, drawing on expert perspectives from 22 countries, presents a comprehensive framework for governing Generative AI (GenAI) in higher education (HE). Recognizing GenAI's pervasive integration and the lack of unified guidance, the study outlines an eight-part policy framework: academic integrity, ethical and responsible use, privacy and data protection, equitable access, GenAI literacy, integration strategy, human oversight and accountability, and institutional support and infrastructure. Furthermore, a six-part procedural model is proposed to ensure policies remain current and adaptable amidst rapid technological evolution. This includes establishing a dedicated GenAI Committee, regular policy reviews, ongoing professional development, transparent communication, impact evaluation, and continuous monitoring of external developments. The framework emphasizes a hybrid approach combining formal policies with flexible guidelines, balancing innovation with academic integrity and pedagogical excellence.
Key Takeaways for Your Enterprise
Our analysis reveals critical metrics and insights from the study, providing a data-driven foundation for your AI strategy in educational contexts.
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Experts Advocating Hybrid Policy Model
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Core Policy Areas Identified
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Processes for Policy Adaptability
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Countries Represented in Delphi Study
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Policy Approach
Policy Evolution
Core Policy Areas
Implementation Challenges
The consensus favors a hybrid model: formal policies for foundational principles and flexible guidelines for nuanced application and adaptation.
88%
of experts recommend a hybrid policy & guidelines approach.
To combat obsolescence, policies require a structured, ongoing review and adaptation process, involving diverse stakeholders and external monitoring.
Enterprise Process Flow
Create Dedicated GenAI Committee
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Conduct Regular Policy Reviews
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Provide Ongoing Professional Development
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Communicate with All Stakeholders
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Evaluate Effectiveness & Impact of GenAI
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Monitor External Developments
The study identified eight critical areas for GenAI governance, ensuring a holistic approach to address both opportunities and risks in HE.
| Policy Area | Key Principles | Example Guidelines |
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| Academic Integrity |
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| Ethical & Responsible Use |
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| Privacy & Data Protection |
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| Equitable Access |
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| GenAI Literacy |
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| Integration Strategy |
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| Human Oversight & Accountability |
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| Institutional Support & Infrastructure |
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Implementing GenAI policies is not just about rules, but about fostering a culture of adaptability, continuous learning, and robust institutional support.
Translating Policy to Practice: The Iterative Journey
The study highlights that effective GenAI policy implementation transcends simple document publication, requiring an institutional change process. Universities need to move beyond isolated statements to coordinated actions across teaching, assessment, research, and student support. This means creating frameworks that are interpretable and applicable within diverse disciplinary traditions and student populations.
Impact: Successful implementation relies on ongoing faculty development, regular policy reviews, and transparent communication, ensuring policies remain relevant and integrated into the daily academic workflow, rather than being seen as static mandates.
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Your Enterprise AI Implementation Roadmap
Our strategic framework guides your institution through the essential phases of GenAI governance, ensuring a smooth and successful transition.
Phase 1: Foundation & Assessment
Establish a multidisciplinary GenAI Committee. Conduct an institutional audit of current GenAI usage, needs, and existing policies. Gather stakeholder feedback from faculty, students, and administration. Define core principles for GenAI integration.
Phase 2: Policy Development & Training
Draft initial hybrid policies (formal policies + flexible guidelines) addressing academic integrity, ethical use, privacy, and equitable access. Develop and pilot GenAI literacy training programs for faculty and students. Provide resources for pedagogical innovation using GenAI.
Phase 3: Integration & Support
Implement approved GenAI policies and guidelines across all departments. Roll out comprehensive professional development. Establish institutional support infrastructure, including approved GenAI tools and technical assistance. Begin preliminary evaluation of GenAI's impact on learning and operations.
Phase 4: Continuous Review & Adaptation
Establish a regular, scheduled review cycle for GenAI policies (e.g., annually). Monitor external developments in AI technology and regulatory changes. Collect ongoing feedback from all stakeholders. Iterate on policies and support mechanisms based on evaluation results and new insights, ensuring continuous alignment with educational goals and technological advancements.
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