AI-Education Infrastructure Framework (AI-EIF): A Design-Based Model for Scalable System Reform

Artificial Intelligence (AI) is increasingly shaping educational reform, yet most implementations remain fragmented, tool-driven, and layered onto legacy systems that lack the capacity for scale or coordination. This paper introduces the AI-Education Infrastructure Framework (AI-EIF), a design-based conceptual model for orchestrating systemic AI integration in national education systems. Grounded in infrastructure theory, platform logic, and educational systems thinking, the framework proposes six interdependent layers, governance, institutional intelligence, learner feedback, instructional coordination, content delivery, and human facilitation, designed to enable ethically governed, pedagogically coherent, and adaptable orchestration of teaching and learning.Rather than presenting empirical findings, the paper offers a theoretically grounded architecture structured for staged, real-world implementation using a Design-Based Implementation Research (DBIR) approach. An illustrative use-case scenario in a UK school district demonstrates how institutions might incrementally activate orchestration layers within existing platforms, while a feasibility roadmap outlines how evaluation can occur through infrastructure-embedded system signals rather than invasive user tracking. The paper contributes a scalable design blueprint for AI adoption that prioritizes institutional agency, teacher-AI co-orchestration, and learner equity, while providing a structured agenda for future empirical research, policy translation, and cross-sector co-design.