Responsible AI-Supported Inclusive Learning in Cameroon: A Human-Centred Framework for Educator Development and School Implementation

Abstract

Artificial intelligence (AI) may reduce educational barriers through captioning, speech and text conversion, alternative representations, adaptive scaffolding, communication support, and assisted content design. These capabilities do not become inclusive merely because a tool is labelled intelligent or accessible. Their educational value depends on barrier-responsive curriculum design, educator competence, enabling school conditions, local language performance, learner agency, and rights-based governance. This conceptual and policy article develops a human-centred framework for responsible AI-supported inclusive learning in Cameroon. It integrates international rights and policy guidance, Universal Design for Learning, technological pedagogical content knowledge, educator AI-readiness research, recent reviews of AI and disability, and scholarship on inclusive education in Cameroon. The synthesis distinguishes AI, generative AI, adaptive systems, and conventional assistive technologies; maps illustrative affordances to risks and required human controls; and proposes four interdependent implementation domains: barrier-responsive design, educator professional capacity, enabling school conditions, and rights-based governance. A six-domain professional-learning framework and a staged school implementation roadmap translate the conceptual model into operational guidance. The article argues that Cameroon should begin with bounded, accessibility-relevant use cases; co-design with learners with disabilities and educators; test tools under local linguistic and infrastructural conditions; protect sensitive data; and scale only after evidence of feasibility, accessibility, educational validity, and equitable benefit. The framework is a proposition for disciplined implementation and evaluation, not evidence that AI has already improved learner outcomes.