Artificial Intelligence for Sustainable Development in Low-Resource African Contexts: A Structured Review and Adaptive Deployment Framework

Artificial intelligence (AI) is increasingly discussed as a tool for supporting sustainable development in Africa, particularly in healthcare, agriculture, education, and environmental management. However, the feasibility of AI deployment in low-resource contexts is shaped by fragmented data ecosystems, infrastructural limitations, governance capacity, human-capital constraints, and risks of digital exclusion. This study presents a structured literature review of AI for sustainable development in African contexts, with a specific focus on low-resource deployment conditions. The review synthesises evidence across sectoral applications, governance and ethical considerations, and resource-efficient AI approaches. It shows that AI systems may support prediction, classification, monitoring, and decision support, but technical potential does not automatically translate into sustained implementation or measurable sustainable development outcomes. In response, the study proposes a synthesis-based conceptual framework linking structural enablers, sectoral AI applications, potential sustainable development outcomes, and adaptive feedback mechanisms. The framework is intended as an analytical and decision-support tool for assessing whether AI systems can be responsibly deployed, maintained, governed, and adapted under different African deployment conditions. The research contributes to AI-for-sustainable-development research by shifting attention from general AI potential to context-sensitive deployment feasibility in low-resource African settings.