BACTERIOLOGICAL ANALYSIS OF RAW MUTTON RETAILED IN SELECTED OPEN MARKETS IN KAURA NAMODA, ZAMFARA STATE

Abstract

Artificial intelligence is increasingly built into educational technology as a mechanism for inclusive learning, but the evidence on AI-generated visual content is still scattered across disability categories and applications. This paper draws on a narrative review of peer-reviewed studies published between 2021 and 2026 to do three things: (a) synthesize empirical evidence across three applications, namely automated image description, text-to-image comprehension support, and virtual pedagogical agents; (b) evaluate their effectiveness, limitations, and equity implications against the Universal Design for Learning (UDL) framework and WCAG 2.2; and (c) draw practical, evidence-informed recommendations for the stakeholders involved. The findings point to real potential for personalizing and scaling inclusive education, but also to recurring problems: inconsistent description quality, uneven educator readiness, algorithmic bias, and a research base that under-represents learners in the Global South. Responsible deployment, the review concludes, depends on human-in-the-loop oversight, targeted teacher training, and co-design with disabled learners themselves. Recommendations are organized for curriculum developers, classroom teachers, technology designers, policymakers, and researchers working across AI, visual generation, and inclusive pedagogy.