GOVERNANCE BY DESIGN: AN AI–IOT READINESS AND EQUITY FRAMEWORK FOR LOW-RESOURCE SCHOOLS

Abstract

The rapid convergence of artificial intelligence (AI) and the Internet of Things (IoT) promises more intelligent, connected, and personalised learning environments, yet deployment in low- and middle-income countries (LMICs) remains uneven and weakly governed. Much of the available evidence derives from campus-based and comparatively well-resourced educational settings, while existing studies often treat AI and IoT separately and give insufficient attention to equity, environmental sustainability, and institutional readiness. This study advances a Governance by Design perspective and proposes an AI–IoT Readiness and Governance Framework for low-resource schools and open and distance learning (ODL) systems, including open universities, dual-mode institutions, school-based distance programmes, and community or non-formal initiatives. It addresses three research questions: what enabling conditions are required for AI– IoT readiness in ODL; how AI–IoT-rich ODL environments can be governed to balance personalisation, security, and equity; and what transferable lessons can be generalised from early deployments in low-resource contexts. Conceptualising AI and IoT as a layered ecosystem spanning devices and sensors, connectivity and platforms, analytics and AI services, applications and pedagogy, and governance and values, the framework operationalises six readiness dimensions with maturity levels: infrastructure and IoT ecosystem; data governance, privacy, and security; AI-driven personalisation and learning analytics; staff capacity and change management; equity and inclusion; and environmental sustainability. A structured evidence review of Scopus, Web of Science, ERIC, IEEE Xplore and the ACM Digital Library, supplemented by backward and forward citation searching, yielded 57 studies that were analysed through a two-cycle conceptual synthesis. The resulting framework is a conceptual planning and reflection tool rather than a validated measurement or certification instrument. The synthesis identifies proposed readiness conditions, governance considerations and design constraints to inform phased adoption, yielding a structured, privacy-aware, and inclusive pathway for AI–IoT implementation in low-resource education.