Educational Technology for Equity: A Framework for Effective Adoption to Improve Literacy and Access in Low-Income Communities—Insights from Mushin, Lagos State, Nigeria

Purpose: Nigeria’s literacy crisis is most acute in low-income urban districts such as Mushin, Lagos State. This study synthesises evidence and theory to design a context-sensitive framework for equitable educational-technology (edtech) adoption that can raise literacy and access in such settings. Design/methodology/approach: Guided by Diffusion of Innovations, the Technology Acceptance Model, Constructivist Learning Theory, and Social Cognitive Theory, we conducted a systematic desk-based review of peer-reviewed scholarship, policy documents, and programme reports published between 2018 and 2025. AI-assisted thematic coding linked sociotechnical determinants to implementation levers. Findings: Adoption in Mushin-like contexts is constrained by device scarcity, unreliable power, high data costs, and limited teacher digital pedagogy. Evidence shows that offline-first platforms, low-cost shared devices, local-language content, practice-embedded professional development, community stewardship, and transparent procurement can overcome these barriers. These insights inform a five-pillar framework that aligns Infrastructure and Access, Teacher Capability and Pedagogy, Contextualised Content, Community Engagement and Trust, and Governance and Data. Originality/value: The article extends diffusion and acceptance models with a capability-centred, justice-oriented lens, turning fragmented findings into a practical roadmap for policymakers, school leaders, and investors. By foregrounding offline-first strategies and local knowledge, it redefines effective adoption as the expansion of learners’ real opportunities to read, reason, and participate in knowledge creation. Practical implications: The framework offers clear levers for staged pilots, cost tracking, and learning analytics, and complies with Nigeria’s Data Protection Act as well as UNESCO guidance on generative AI in education.