Information from the abstract
The rapid integration of artificial intelligence (AI) within global higher education necessitates rigorous, empirically grounded regulatory frameworks. However, contemporary policy development remains highly fragmented across geopolitical landscapes. This study evaluates a comprehensive corpus of 1,500 global policy documents utilizing an integrated mixed-methods approach combining qualitative content analysis, inferential non-parametric statistics, and fractional-counting bibliometric network modeling. Quantitative evaluations confirm that regional variations in formal AI policy adoption, institutional enforcement, and baseline accessibility are highly pronounced and statistically significant, with mature compliance mechanisms heavily concentrated within European and North American tertiary sectors. A systematic thematic coding of the policy corpus revealed an uneven distribution of priorities: data privacy (41%) and algorithmic bias (28%) dominated the discourse, while equitable access received significantly less coverage (10%). A chi-square goodness-of-fit test confirmed that this distribution across the four policy domains deviates significantly from uniformity, χ²(3, N = 1,500) = 64.10, p < .001. Multiple linear regression modeling demonstrates that policy enforcement metrics (beta = 0.72, p < .001) and structural governance framework strength (beta = 0.68, p < .01) serve as the most powerful predictors of institutional AI readiness. Qualitatively, these dimensions reveal a nascent regime of data colonialism in which one-size-fits-all (and resource-intensive compliance metrics) are systematically prejudicial to periphery academic institutions in emerging economies (e.g. Thailand), whilst reinforcing historical geospatial knowledge hierarchies. Ultimately, the findings indicate that sustainable digital transformation rests on a policy paradigm change from compliance-based to flexible tiered implementation, with educational equity as the global benchmark for local accessibility metrics.
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Related topics: Ethics and Social Impacts of AI · Artificial Intelligence in Healthcare and Education · Online Learning and Analytics
Thai researcher and institutional participation
Mark Treve · Walailak University
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