Artificial Intelligent Based Student Priority and Determination System in Nigeria Tertiary Institutions

Abstract

Nigerian tertiary institutions face chronic delays in student support due to manual processes and fragmented knowledge. This paper presents an intelligent ticketing system that employs Knowledge Discovery in Databases (KDD) and a Random Forest classifier to automate ticket prioritisation and response. The system was trained on 5,000 real historical tickets from a Nigerian distance‑learning institution. Text features were extracted using TF‑IDF, and ticket relationships were modelled via a graph structure. The classifier achieved 91.2% accuracy in predicting priority. Recurring queries were answered automatically using a dynamically growing knowledge base, reducing average response time to 1.3 seconds. User satisfaction, measured through a 5‑point Likert survey (n = 50), averaged 4.7/5. The system is scalable to over 10,000 concurrent tickets and operates effectively in resource‑constrained environments.