TL;DR
Overall, the results show persistent readiness gaps alongside a shared movement toward more targeted guidance for integrating generative AI in education.
Artificial Intelligence is entering classrooms across South Asia, while progress towards Sustainable Development Goal 4 on quality education has stalled. This paper compares AI governance and readiness across four South Asian countries that have completed formal, data-driven AI readiness assessments in education: India, Bangladesh, Bhutan, and the Maldives. It then discusses Nepal’s National AI Policy, noting that the country has not yet run a scored readiness assessment or published baseline education data. Drawing on national AI policies, United Nations Educational, Scientific and Cultural Organization and United Nations Development Programme readiness reports, and United Nations Sustainable Development Goals (SDG) Report 2026, the study finds that only 36% of assessable SDG targets are on track globally, and that 273 million children and young people remain out of school worldwide. Across the four comparator countries, common gaps appear in curriculum integration, teacher training, and gender participation in STEM and AI. Nepal shares these same gaps, but lacks the baseline data its neighbours have already collected. The paper proposes a conceptual framework that other resource-constrained countries can use to build AI governance into education systems to support SDG 4.
National AI strategies increasingly guide governance, workforce development, innovation, and competitiveness, but less is known about how they frame education as a sector with pedagogical, cultural, ethical, and implementation demands. This study develops and applies an Education-Centered AI Policy Framework to analyze Ghana's National Artificial Intelligence Strategy, 2025-2035. Using critical qualitative policy document analysis, we examined the strategy through six components: policy purpose, teacher agency and professional learning, curriculum and assessment, language and culture, responsible AI and learner protection, and participation and implementation governance. Findings show that Ghana's strategy is ambitious and timely, especially in its emphasis on AI literacy, youth skills, TVET, workforce readiness, rural outreach, local language data, inclusion, and responsible AI governance. However, the education agenda is stronger on national AI readiness than on school-level implementation. Teacher agency, pre-service teacher education, curriculum progression, assessment guidance, AI disclosure, multilingual pedagogy, culturally responsive AI use, child-centered safeguards, and participatory governance remain underdeveloped. We also identify document-level concerns about transparency and coherence, including apparent AI-styled visual content without visible disclosure and a mismatch between a vision and mission figure and its textual explanation. We argue that Ghana needs a sector-specific, education-centered AI policy and implementation pathway that connects workforce readiness with teacher preparation, curriculum reform, assessment redesign, learner protection, infrastructure, local language instruction, culturally responsive pedagogy, locally responsive AI tools, and participatory governance.
Artificial intelligence (AI) has rapidly permeated higher education workplaces, yet a significant disconnect exists between employee adoption of AI tools and institutional policy awareness, governance structures, and strategic clarity. This study examines the emergent phenomenon of the "AI implementation gap" in higher education—the disparity between widespread AI tool usage and the institutional frameworks meant to guide such use. Drawing on recent survey data from nearly 2,000 higher education professionals and situating findings within broader theoretical frameworks of technology adoption, organizational change, and higher education governance, this article critically analyzes the current state of AI integration in higher education work environments. Key findings reveal that while 94% of higher education employees report using AI tools for work, only 54% are aware of relevant institutional policies, and more than half have used AI tools not sanctioned by their institutions. The analysis explores the risks, opportunities, and challenges associated with this implementation gap, including concerns about data privacy, misinformation, skill erosion, algorithmic bias, environmental impact, and the largely unmeasured return on investment of AI initiatives. The article also examines the roles of AI vendors, the ethical dimensions of AI adoption, and the implications of voluntary versus mandated technology use. The article concludes with recommendations for institutional leaders, policymakers, and researchers seeking to bridge the gap between AI adoption and governance in higher education contexts.
Governments' institutional capacity to harness artificial intelligence (AI) varies markedly across countries, and much of the existing literature attributes this disparity to digital infrastructure and income level. This study examines whether the accumulation of human capital through higher education explains additional variance in government AI readiness beyond these structural factors. Drawing on a cross-sectional panel of 158 countries that combines World Bank indicators, governance estimates, and a composite government AI readiness index, robust regression models (HC3), a non-parametric test of between-income-group differences, and a residual correlation analysis were estimated. Gross tertiary enrollment significantly predicted institutional AI readiness (β = 0.19, p = .001), even after controlling for GDP per capita, internet penetration, and government effectiveness. The marginal return of higher education on readiness was significantly smaller in low-income countries than in high-income countries, suggesting an absorptive-capacity threshold rather than a uniform linear effect. Countries whose educational performance exceeded what their income level would predict also showed, on average, higher-than-expected AI readiness performance (r = .32, p < .001). These findings qualify infrastructure-centered digital divide frameworks, engage with the literature on absorptive capacity and technological catch-up, and position higher education as a relevant, though not sufficient, institutional mechanism for explaining global inequality in AI governance.
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.
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.