SUSTAINABLE INCLUSIVE TECHNOLOGY ADOPTION IN NIGERIAN TERTIARY INSTITUTIONS: A CONCEPTUAL FRAMEWORK FOR AI-ENABLED LEARNING SPACES

Abstract

Nigerian colleges of education and polytechnics are facing persistent barriers to inclusive, AI-enabled learning, including unstable electricity, overcrowded classrooms, scarce devices, limited accessibility provision, and weak data governance. Using a qualitative multiple-case study of three institutions, grounded in documentary and publicly available institutional evidence from a teacher-training college, a polytechnic, and a federal school of statistics, this paper argues that these barriers are structural and participatory rather than purely technological. It contributes the Sustainable Inclusive Technology Adoption Framework (SITAF), a four-layer model spanning infrastructure, digital tools, human experience, and governance, supported by a five-stage implementation roadmap and proposed indicators for pilot testing. The three cases support analytic rather than statistical generalisation, and SITAF has not yet been empirically validated. The findings imply that institutional leaders, sector regulators, and funders must act together on infrastructure standards, accreditation requirements, and data governance if inclusive AI adoption is to survive administrative transitions and reach the learners most at risk of exclusion.