Exploring the Potential of AI in News Translation for Rural Communities in Nigeria: Towards a Conceptual Framework

Abstract

Nigeria’s linguistic diversity presents significant challenges in news accessibility, particularly for rural communities where indigenous languages dominate daily communication. The predominance of English in media and governance creates barriers that limit civic engagement and economic participation for non-English speakers. This study explores the potential of AI-driven news translation as a solution to bridge this language gap, ensuring that critical news reaches rural populations in their native languages. A conceptual model is therefore proposed, positioning Automated Speech Recognition (ASR) and Neural Machine Translation (NMT) as the core translation mechanisms, with the Technology Acceptance Model (TAM) and its principles—perceived usefulness and ease of use—serving as mediating variables. This model examines the relationship between AI-driven translation and external factors influencing its adoption, ensuring that implementation is both practical and scalable. By analyzing how users perceive and interact with AI translation technologies, the study highlights key enablers and barriers to adoption. It further emphasizes the need for ethical AI deployment, policy-driven digital infrastructure, and stakeholder collaboration to enhance news accessibility in Nigeria’s multilingual landscape. Ultimately, this research provides a structured approach to integrating AI in news translation, ensuring that rural communities receive contextually relevant and linguistically accurate news, thereby fostering greater information inclusivity and civic participation.

Keywords: news, translation, news translation, artificial intelligence, AI, verification