AI Low-Resource Language Models for African Indigenous Languages: Bridging the Digital Divide Through Innovative AI Natural Language Processing Solutions.

This research investigates the development and optimization of natural language processing (NLP) models for African indigenous languages, addressing the critical digital divide that affects over 2 billion speakers across the continent. Through a comprehensive analysis of existing pre-trained language models and novel methodologies, this study examines the effectiveness of transformer-based architectures, specifically focusing on multilingual BERT variants, sentiment analysis systems, and speech recognition models for low-resource African languages. The research employs a design thinking framework to develop scalable solutions that enhance digital inclusion and educational accessibility. Our findings demonstrate that ensemble methods combining multiple pre-trained language models achieve superior performance, with weighted F1 scores exceeding 77% for closely related language families. The study contributes to the growing body of work in Afrocentric NLP by providing empirical evidence for effective cross-linguistic transfer learning techniques and proposing a framework for sustainable language model development in resource-constrained environments