TOWARD A GLOBAL, AI-AUGMENTED PEDAGOGICAL INFRASTRUCTURE FOR INCLUSIVE, MULTILINGUAL EDUCATION

Abstract

This article presents a conceptual framework for a Global AI-Augmented Pedagogical Infrastructure (G-AAPI) designed to support inclusive, multilingual education at scale. We synthesize current empirical evidence and design principles to articulate an architecture that integrates multilingual AI assistants, data governance, teacher professional development, and equitable access to learning resources. The framework addresses three core challenges: (1) linguistic and cultural diversity in curricula, (2) data privacy, security, and ethics in AI-mediated learning, and (3) sustainable implementation across diverse educational ecosystems. We describe the components, governance mechanisms, and evaluation metrics of the G-AAPI, and outline a set of pilot exemplars spanning varied socio-economic contexts. Findings indicate that a carefully designed AI-augmented infrastructure can reduce learning gaps, enhance instructional responsiveness, and empower teachers without compromising privacy or equity. We also discuss risks, methodological considerations, and policy implications for large-scale adoption, along with a roadmap for iterative, evidence-driven refinement.