October 9, 2025

AI policies in school education: a comparative study on China, Singapore, Finland, and the US

Puntos clave

All four countries share a common intent to integrate artificial intelligence into school education, indicating a global trend.
Nine thematic propositions highlight critical components of AI policy integration, including teacher training and equitable funding.
Implementation challenges such as equity gaps and teacher readiness are common across the countries studied.
Effective AI education frameworks must prioritize ethical safeguards and localized adaptability to support diverse school contexts.

Resumen

Purpose The purpose of this study is to compare artificial intelligence (AI)-integration strategies in school education across China, Singapore, Finland and the USA, aiming to uncover shared patterns and localized innovations that could inform a globally responsive AI education framework. Design/methodology/approach The qualitative desktop study draws on secondary data from five recent government policy documents: China’s New Generation Artificial Intelligence Development Plan, Singapore’s EdTech Masterplan 2030, Finland’s Age of Artificial Intelligence, California’s Computer Science Strategic Implementation Plan and Massachusetts’ Digital Literacy and Computer Science Standards. These were analyzed using the SMART criteria and a researcher-constructed “Nine-point framework of operational components in AI policy for schools.” Findings Despite varying governance models and socio-cultural contexts, all four countries share a common intent to integrate AI into school education. Nine thematic propositions emerged: “SMART” policy design, balanced vision, curriculum and ethics integration, dynamic teacher training, equitable funding, multi-stakeholder partnerships, adaptive monitoring, localized implementation and contextual alignment. Finland and Singapore demonstrate strong ethical and human-centered policies, while China and the USA lean toward innovation and workforce development. Implementation remains challenged by equity gaps, teacher readiness and contextual mismatches. Research limitations/implications These diverse models offer critical lessons: future global frameworks must prioritize ethical safeguards, localized adaptability, inclusive training and dynamic monitoring systems to ensure AI supports equity and relevance across school contexts. Originality/value This study offers original insights derived from systematic, comparative analysis of the national AIEd policies using a robust evaluative framework.

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