A Review of Artificial Intelligence in Education: What is Ahead, What is Left Behind at Global Scale?

Résumé fourni par la source

This narrative review synthesizes recent research on artificial intelligence (AI) in education to address gaps in understanding its diverse roles, integration strategies, and pedagogical implications. The review aims to evaluate AI integration within curricula and instructional practices, examine AI literacy development, identify inclusive pedagogical strategies, compare adoption challenges including ethics and equity, and analyze AI's role in fostering critical thinking and creativity. Literature published between 2020 and 2025 was identified through searches of the Scopus, Web of Science, PubMed/MEDLINE, and arXiv databases, supplemented by AI-assisted semantic search, and a purposively selected body of 86 sources comprising peer-reviewed journal articles, books, conference proceedings, and preprints was retained as representative evidence for critical synthesis. A thematic analysis of empirical and theoretical studies across global K-12, higher education, and professional contexts was conducted, focusing on AI literacy and educator preparedness, curriculum design, pedagogical effectiveness, inclusivity, and ethics. Findings reveal that AI-enabled personalized and adaptive learning can significantly enhance student engagement and outcomes while supporting diverse learner needs; however, equitable access and infrastructure disparities limit inclusivity. Curriculum frameworks emphasize interdisciplinary AI literacy integrating ethical and technical competencies, yet standardization and comprehensive coverage remain insufficient. Educator readiness is critical but hindered by knowledge gaps and limited professional development, affecting effective AI adoption. Ethical concerns, including data privacy and algorithmic bias, are widely recognized but inadequately addressed in practice. Collectively, these findings underscore AI's transformative potential in education contingent on balanced curriculum design, ethical integration, and robust teacher support. The review informs educators, policymakers, and researchers on effective AI embedding strategies to prepare learners for an AI-driven future.

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