Generative Artificial Intelligence Policy: A Qualitative UNESCO Framework Analysis

Synthesis: Adarkwah et al. (2026) conduct a qualitative policy analysis of Generative AI (GenAI) policies at 30 highly ranked universities (by 2024 QS rankings) across the top ten countries for AI preparedness, evaluating them through UNESCO's eight-component GenAI framework. The findings reveal significant disparities in policy robustness: core ethical and Governance principles are widely embraced, yet critical issues of inclusion, equity, and sustainability — such as internet access, gender parity in AI, and environmental impact — are often overlooked. Nordic countries and New Zealand cover UNESCO's elements more fully than some higher-ranked AI Preparedness Index (AIPI) countries, demonstrating that AIPI ranking does not guarantee strong GenAI Educational Policy AI. Notably, no public policies were found for German universities or Tallinn University of Technology. The study urges higher education leaders to develop more inclusive, future-oriented Higher Ed policies integrating social equity, interdisciplinary experimentation, and sustainability.

Key Findings

Uneven policy robustness: There are significant disparities in how leading universities regulate GenAI; while core ethical and Governance principles are broadly adopted, many policies remain declarative rather than operationally assured.
Neglect of inclusion and sustainability: Key UNESCO elements such as inclusion, equity, internet access, gender parity in AI, and environmental impact are frequently overlooked across policies.
AIPI ranking is not predictive: Nordic countries and New Zealand cover UNESCO's elements more fully than some higher-ranked AI Preparedness Index (AIPI) countries, showing that national AI readiness does not guarantee robust institutional policy.
Missing policies: No publicly accessible GenAI policies were found for German universities or Tallinn University of Technology; four of the 30 targeted universities were excluded for lacking public policies, leaving 26 in the final analysis.
Threat-focused framing: Reflecting broader Academic Integrity literature, many policies anchor in misconduct prevention and originality concerns, narrowing the pedagogical debate and under-addressing socio-political and moral dimensions.
Recommendation: Higher education leaders should develop more inclusive, future-oriented policies integrating social equity, interdisciplinary experimentation, and sustainability into Educational Policy AI.

Study Design & Method

The study employed a qualitative policy analysis using the European Training Foundation's four-step guide (framing the problem, collecting/describing evidence, interpreting evidence, formulating recommendations). A purposive sampling strategy selected the top ten countries for AI preparedness based on the IMF's AI Preparedness Index (AIPI), then the top three world-class universities per country using the 2024 QS global rankings.

A total of 159 documents (policy texts, institutional announcements, webpages, reports) were retrieved and analyzed via content analysis with open coding. Two researchers independently coded policies for two countries (six universities) and met to establish consensus before broader team review. Institutional-level GenAI policies were evaluated against UNESCO's eight-component GenAI framework, which spans: inclusion and cultural-linguistic diversity; human agency; monitoring and validation; learner competencies; educator and researcher capacity; pluralism and epistemic diversity; local experimentation and evidence accumulation; and long-term, interdisciplinary, intersectoral review. An indicator was deemed neglected if addressed by five or fewer universities. Data collection ran October 2024 to January 2025.

Implications for AI in Education

The study demonstrates that high academic reputation and national AI readiness do not automatically translate into robust Generative AI Governance at the institutional level. Policies tend to be fragmented, ethics-focused, and oriented toward Academic Integrity and misconduct prevention rather than operational assurance or pedagogical transformation. The findings support calls for Educational Policy AI to move beyond prohibition-oriented framing toward inclusive, equity-aware, sustainability-conscious design that builds educator and learner GenAI competencies. The prevalence of public-facing, declarative policy over enforceable assurance mechanisms highlights an urgent need for monitoring, validation, and review structures in Higher Ed institutions.

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Citation

Adarkwah, M. A., Ercan, A. M., Schneider, K., & Bayar, E. (2026). Generative artificial intelligence policy: a qualitative UNESCO framework analysis. Journal of University Teaching and Learning Practice, 23(2). https://doi.org/10.53761/801ryw41