A Systematic Review of AI Integration Frameworks and the Emergence of the EDAIL Framework for Teachers in LMICs

This review critically synthesizes 43 empirical, conceptual, and policy-driven studies on AI integration frameworks in education (2021–2025), guided by PRISMA methodology, studies were selected for thematic synthesis based on peerreviewed literature sourced from Scopus, ERIC, and Web of Science. The review explores key domains of AI integration, key findings reveal thematic convergence around pedagogical alignment, ethical AI use, teacher capacity, contextual relevance for LMICs, and infrastructural considerations. Global frameworks such as UNESCO’s Competency Model and adaptations of TPACK and SAMR dominate the discourse, but often fall short of addressing LMIC-specific realities. The Educators’ Artificial Intelligence Literacy (EDAIL) framework emerges as a context-sensitive extension, developed through co-creation with African educators. EDAIL’s emphasis on the “What, Why, How, and When” of AI use offers a pragmatic, teacher-centered roadmap, bridging ethical imperatives and pedagogical integration. The study highlights the urgent need for adaptable, equity-oriented AI strategies in education and positions EDAIL as a scalable model to guide AI implementation in resource-constrained educational settings.