Artificial intelligence policy challenges and institutional readiness in Omani higher education
Bibliographic Data
| ID | 22437255 |
|---|---|
| Authors | Abdelghani Benayoune (0000-0003-2354-3449, Qatar Science and Technology Park), Zouhaier Slimi (0000-0001-8521-2536, Universidad de Deusto, corresponding author), Amer Al Habsi (Muscat College) |
| Year | 2026 |
| Volume | 5 |
| Issue | 1 |
| Publication date | 2026-02-03 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Discover Education (JOURNAL) |
| Journal identifiers | ISSN: 2731-5525 • E-ISSN: 2731-5525 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s44217-026-01188-4 |
| OpenAlex | W7127344046 |
| Language | EN |
| References cited | 17 |
The rapid integration of artificial intelligence (AI) into higher education presents transformative opportunities alongside complex governance challenges, particularly in emerging economies. This mixed-methods study examines AI policy adoption, implementation barriers, and stakeholder priorities across 28 higher education institutions (HEIs) in Oman, a Gulf Cooperation Council (GCC) nation actively pursuing AI-driven economic diversification under Vision 2040. Combining quantitative surveys (n = 39 institutional leaders) and qualitative focus groups (n = 15 students), the research reveals a critical governance gap: only 5.1% of higher education institutions (HEIs) have formal AI policies, while 74.4% lack frameworks. Key challenges include academic integrity violations (e.g., undetectable AI-generated plagiarism), student over-reliance on generative tools (reported by 59% of faculty), and systemic deficiencies in training, infrastructure, and regulatory clarity. The thematic analysis identifies faculty-driven pedagogical innovations and institutional unpreparedness, with 69% of respondents citing “regulatory uncertainty” as the primary barrier. Applying DiMaggio and Powell’s (1983) institutional isomorphism framework, we expose how coercive (top-down policy enforcement gaps), mimetic (ad-hoc replication of regional models), and normative (training deficits) pressures perpetuate fragmentation. Findings underscore the urgency of context-sensitive strategies, proposing three actionable recommendations: (1) national ethical guidelines aligned with UNESCO’s AI Competency Framework, (2) mandatory faculty certification programs targeting 80% compliance by 2026, and (3) equitable resource allocation through Oman’s National AI Observatory. The study positions Oman as a GCC policy laboratory, offering transferable insights for mid-sized economies balancing AI innovation with cultural preservation. Limitations include sample size constraints (n = 28 HEIs) and the rapid evolution of AI, necessitating longitudinal research. This work contributes to global debates on AI governance by empirically linking institutional theory to policy implementation in Arab higher education contexts.
Accountability · Certification · Corporate governance · Enforcement · Higher education · Normative · Redress · Thematic analysis · Transformative learning · Artificial Intelligence in Healthcare and Education · Ethics and Social Impacts of AI · Online Learning and Analytics
| Highly cited | No |
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