Readiness gaps between high and low income countries persist despite converging AI adoption in education

Abstract

Artificial intelligence (AI) is rapidly transforming global education and educational technology. A large concern that has arisen from this transformation is the expanding educational disparities between high and low income countries that have different access to cutting-edge technologies due to a variety of factors including educational funding, digital infrastructure, and teacher training. Most existing research documents study the digital divide within the status quo, but rarely predict how structural readiness and access will evolve over time. To address this gap, a composite AI Education Readiness Index (AERI) was constructed from World Bank learning poverty data, GovTech maturity scores, and UNESCO ICT/STEM graduation rates across 99 countries. Indicators were normalized to a 0-100 scale and combined into three weighted subscores, Student, Government, and Talent Readiness, which were aggregated into an overall AERI. Each country’s AERI was projected forward using a Prophet time-series model, and countries were grouped by World Bank income tier to compare trends. The gender gap in tech-field participation was analyzed separately across income groups. AERI scores rose across all income tiers over the forecast period, yet the gap between higher and lower income countries remained nearly constant at approximately 27 points, indicating little convergence. The lowest-income tier stagnated or declined even as other groups improved. Notably, surface-level AI usage converged across income groups while the deeper readiness measure by AERI did not, suggesting that adoption rates mask persistent structural inequality. These findings indicate that rising AI adoption alone does not close the structural education gap between high and low income countries, underscoring the need for targeted investment in the lowest-income nations.