Hidden barriers of education and artificial intelligence: rethinking equity, affordances and ability

Abstract

Purpose This article investigates how artificial intelligence (AI) reshapes digital inequality in education by introducing the concepts of the AI divide and ability divide. It examines how exclusion emerges not only from unequal access or skills but from differential capacities to perceive, interpret and mobilize AI-mediated affordances. Design/methodology/approach The study is conceptual and theory-driven. It draws on transformative learning theory, critical disability studies and affordance theory to analyze how AI intersects with structural inequality, techno-ableist design and policy discourses of adaptability. Findings The analysis reveals that AI integration risks reinforcing normative expectations of capability and marginalizing learners who deviate from these standards. Denial to change introduced or mediated by AI is better understood as a socio-cultural response to disruption rather than a deficit. A co-evolutionary model of transformative education is proposed, positioning AI as a catalyst for rethinking purposes, norms and power relations in education. Originality/value The article advances the concept of the ability divide to capture emerging inequalities in AI-mediated learning. It contributes a novel theoretical synthesis of affordance theory, disability studies and transformative learning, providing a framework for designing equitable, inclusive and agency-affirming educational practices.