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<p>This study examines how national artificial intelligence (AI) strategies articulate educational priorities and readiness across countries with different income classifications. Using an integrated framework combining the Diffusion of Innovation (DOI) and the EdTech Readiness Index (ETRI), the study analyzes quantitative readiness indicators from 2020 to 2024 alongside qualitative policy content from national AI strategies. Findings reveal substantial cross-national variation in AI education readiness. High-income countries tend to adopt more comprehensive and coordinated approaches emphasizing governance, ethics, and advanced skills development, while lower-middle-income and upper-middle-income countries focus on foundational digital capacity, AI literacy, and access. Across income groups, policies prioritize higher education and workforce development over K–12 implementation. Overall, the results show persistent readiness gaps alongside a shared movement toward more targeted guidance for integrating generative AI in education.</p>

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Mapping AI Across World in Education: A Mixed-Methods Analysis of National Policies and Government AI Readiness

  • 期刊论文

  • 发表日期：

    2026年7月17日

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  • Jialin Yan,
  • Prattama Santoso Utomo,
  • John Curry

摘要

This study examines how national artificial intelligence (AI) strategies articulate educational priorities and readiness across countries with different income classifications. Using an integrated framework combining the Diffusion of Innovation (DOI) and the EdTech Readiness Index (ETRI), the study analyzes quantitative readiness indicators from 2020 to 2024 alongside qualitative policy content from national AI strategies. Findings reveal substantial cross-national variation in AI education readiness. High-income countries tend to adopt more comprehensive and coordinated approaches emphasizing governance, ethics, and advanced skills development, while lower-middle-income and upper-middle-income countries focus on foundational digital capacity, AI literacy, and access. Across income groups, policies prioritize higher education and workforce development over K–12 implementation. Overall, the results show persistent readiness gaps alongside a shared movement toward more targeted guidance for integrating generative AI in education.