Beyond Language: Reframing LLMs in Africa Through Contextual Grounding

Abstract

This position paper reframes how Artificial Intelligence (AI) development in Africa is approached. Current efforts emphasize language-inclusion training for Large Language Models (LLMs) to speak African languages such as Swahili, Yoruba, and Zulu. However, linguistic inclusion alone does not ensure contextual understanding. LLMs trained primarily on Western data often misinterpret African idioms, social norms, and institutional realities.

We propose a multidimensional Framework of African Contextual Dimensions, cultural-linguistic, socioeconomic, historical-political, and epistemic, to guide the design of contextually grounded AI systems. Drawing on recent literature and case studies in healthcare, education, governance, and agriculture, the paper shows that context-blind AI can produce fluent yet culturally irrelevant or harmful outputs.

We argue that true inclusion requires contextual grounding, meaning AI systems must align with Africa’s lived realities, ethical values, and indigenous knowledge systems. The paper concludes with policy recommendations for governments, researchers, and developers to embed African context into every stage of the AI pipelinefrom dataset creation to deployment.

Keywords: African NLP, Contextual AI, Ethics, Large Language Models, Ubuntu, Indigenous Knowledge, AI Governance, Africa