Impact of Low Laboratory Assessment Weight on AI and IoT Skills in Engineering Education in Zambia: A Case Study of Electrical and Electronics Department

Abstract

Laboratory sessions in engineering education are very essential in giving students hands-on training to complement theoretical learning. Unfortunately, many Zambian universities attribute less than 10% of the overall course grades to practical work, leading to poor engagement and limited skill acquisition and transfer. This study investigates the impact of low laboratory weighting on student outcomes within Electrical and Electronics Engineering programs. Emphasis is placed on the increasing need for practical competence in implementing Artificial Intelligence (AI) and Internet of Things (IoT) systems, which are driving modern electrical engineering innovations. This paper analyses the engineering curriculum, case studies, and prototypes developed by final-year students, highlighting the consequences of minimal lab exposure leading to reduced innovation capacity and industry unpreparedness. A model is proposed that increases laboratory assessment weight to 30–50%, integrates interdisciplinary project-based learning, simulations, and aligns skill development with industry demands. The findings suggest that rebalancing theoretical components can significantly enhance students’ technical proficiency, system-level thinking, and readiness for AI- and IoT-driven environments.