Utilizing Artificial Intelligence to Enhance Personalized Learning at Zambian Universities: A Case Study of NIPA

Abstract

The integration of Artificial Intelligence (AI) in personalized learning offers significant potential to transform education in Zambian universities. Given the growing diversity of student populations and the challenges in traditional educational delivery, AI-driven adaptive learning systems present an innovative approach to customizing learning experiences to meet individual student needs. This study investigates the role of machine learning algorithms in analyzing student performance data to personalize course content, suggest relevant resources, and provide real-time feedback, thereby enabling individualized learning paths. Focusing on the National Institute of Public Administration (NIPA) as a case study, the research explores the impact of AI on student engagement, academic achievement, and retention in the context of Zambian higher education. The study also identifies challenges such as infrastructure limitations, concerns around data privacy, and the necessity for faculty training. The findings aim to offer valuable insights into the viability of AI-powered personalized learning systems in resource-limited environments, with practical recommendations for effectively integrating AI into university teaching practices to improve educational outcomes for both students and faculty.