Small Learners, Big Data: Ethical and Policy Challenges of AI in Early Childhood Education in Global and U.S. Contexts - Wu, Yi (Language, Literature and Education: Research Updates Vol. 4, 2025)
As artificial intelligence (AI) becomes increasingly embedded in educational systems, its integration into early childhood education (ECE) presents both transformative opportunities and complex challenges. This literature review critically examines global and U.S. policy frameworks governing AI implementation in ECE, with a focus on ethical, legal, pedagogical, and equity-related dimensions. Drawing on theoretical foundations from developmental psychology, sociology of childhood, and educational technology, the paper synthesises empirical findings and policy analyses across diverse national contexts, including the European Union, China, Singapore, Australia, and the United States. The review identifies global best practices that emphasise ethical AI design, inclusive pedagogy, and robust educator training, contrasting these with the fragmented and innovation-driven U.S. landscape. Key challenges—ranging from data privacy and algorithmic bias to infrastructure gaps and professional readiness—underscore the need for developmentally informed, child-centred AI governance. The paper concludes by outlining critical research gaps, including the need for longitudinal studies, equity-driven implementation strategies, and pedagogical integration frameworks. By advancing a multidimensional policy perspective, this review offers actionable insights for educators, policymakers, and technologists working to ensure that AI enhances rather than undermines the promise of equitable and developmentally appropriate early learning.