Towards Inclusive Climate Solutions: Merging Indigenous African Knowledge and Artificial Intelligence in Rural Communities

Abstract

Climate change has been found to disproportionately affect rural communities across Africa, which, as a result, deepens existing socio-economic and environmental inequalities. As global efforts shift towards Artificial Intelligence (AI)-driven solutions to enhance climate resilience and adaptation, recent scholarship highlights their potential, such as predictive modeling, resource management, and disaster response. However, applying these solutions in African rural contexts, characterized by limited access to technology, infrastructure, and resources, remains a challenge. This challenge becomes more alarming when considering the human and social dimensions of climate vulnerability, where current AI-driven approaches often fail to engage with the lived realities, traditional knowledge systems, and unique challenges of rural populations.To address these gaps, this paper adopts a Complementary Framework that views AI and Indigenous Knowledge Systems not as opposing forces, but rather, as mutual which could coexist for productive outcomes. It does these shortcomings by critically evaluating the integration of AI-driven solutions into rural African settings. It emphasises the need for inclusivity and the recognition of indigenous knowledge and approaches through the use of machine learning. Hence, by identifying systemic barriers; technological, social, and cultural, that hinder effective implementation, the paper calls for a shift away from a purely techno-centric paradigm, arguing for a more inclusive and participatory model that considers the voices, needs, expertise, and overall reality of Africa’s vulnerable rural communities.

Suggested citation

Damilola Peter Olatade, Ridwan Ishola Mogaji (2025). Towards Inclusive Climate Solutions: Merging Indigenous African Knowledge and Artificial Intelligence in Rural CommunitiesJournal of Ecopolitics, Peace, and Sustainable Development, 1(1). https://doi.org/10.69798/84663696

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