Automated Identification of Missing and Incomplete Wikipedia Content on and about Zambia

The global digital knowledge ecosystem is characterized by significant geographical disparities, with over 80% of Wikipedia content originating from Europe and North America. This has resulted in a profound representation gap for African nations like Zambia, particularly in critical domains such as higher education and local governance. Current Wikipedia editing environments lack automated, context-aware tools to assist contributors from underrepresented regions in identifying and rectifying these content gaps. This project addresses this challenge by designing, developing, and evaluating a novel software solution comprising a Chrome Extension and a central Repository. The core innovation of the Chrome Extension is its use of a Large Language Model (LLM) to perform real-time analysis of Wikipedia articles. It benchmarks the content against a defined gold-standard of completeness for institutional articles and provides actionable, context-specific suggestions for improvement directly within the user's editing interface. All identified gaps are logged to a centralized Repository, which serves as a dashboard for visualizing content disparities across Zambian universities and municipal councils. The system was developed using an Agile methodology and will undergo rigorous evaluation involving Wikipedia editors from the Zambian context to assess its usability, effectiveness, and impact on editing behaviour. The proposed solution offers a scalable framework for promoting digital knowledge equity by empowering local contributors with intelligent tools to enhance the representation of their institutions on one of the world's most accessed information platforms.