ARTIFICIAL INTELLIGENCE AND GRASSROOTS BROADCASTING: CONTEXT-SENSITIVE COMMUNICATION FRAMEWORK FOR RADIO PROGRAMMING IN NIGERIA

Grassroots radio broadcasting remains the primary communication medium for rural and semi-urban populations in Nigeria, serving approximately 70% of communities where electricity supply is intermittent and internet penetration remains below 40%. Despite its critical role in development communication, grassroots broadcasting faces persistent structural challenges including inadequate funding, outdated technical infrastructure, limited human capacity, and difficulties in conducting systematic audience research. The emergence of artificial intelligence (AI) technologies presents both opportunities and risks for addressing these challenges. While AI tools such as automated transcription, content translation, programme archiving, and audience analytics could potentially enhance operational efficiency, most existing AI systems are designed for technologically advanced media environments and remain insensitive to local cultures, indigenous languages, and participatory communication traditions that define grassroots broadcasting in Nigeria. This article employs a qualitative analytical approach grounded in systematic literature review to examine how AI can be responsibly integrated into Nigerian grassroots radio programming without undermining cultural authenticity, community participation, or local ownership. Anchored theoretically on Participatory Communication Theory and Demystifying Emerging Media Theory, the paper argues that AI adoption in grassroots broadcasting must be participatory, transparent, culturally sensitive, and positioned as assistive technology rather than replacement for human judgement. The study concludes that AI can meaningfully support grassroots broadcasting in Nigeria only when it functions as a tool that strengthens rather than supplants human broadcasters, preserves indigenous communication practices, enhances community participation, and remains under local control. Responsible AI integration demands enabling policy frameworks, sustained investment in indigenous language technology, and community-centred governance structures that protect grassroots media autonomy.