Volume 16, Number 6

A Smart Framework for Aligning College Curricula with Labor Market Needs

  Authors

Khaled Ebrahim Almajed, Hazem M. El-Bakry, Samir Abd Elrazez, Rasha Elhadri, Mansoura University, Egypt

  Abstract

This study investigates the integration of intelligent systems—such as AI-driven platforms and automated analytics tools—to modernize college curricula and align academic programs with the evolving needs of the labor market. Through real-time analysis of job trends, emerging skill demands, and industry innovations, the study identifies key gaps in current curricula. It also explores the feasibility of automating curriculum review and updates using intelligent tools. A prototype framework is proposed, with a focus on ethical principles including fairness, transparency, and data privacy. Preliminary results demonstrate that intelligent systems offer a scalable and effective solution for bridging the gap between higher education and workforce requirements. These systems leverage machine learning, data analytics, and decision-support mechanisms to continuously monitor labor market trends and adapt educational content accordingly. Their role is not limited to automation, but extends to dynamic interaction with curriculum developers and institutional policymakers to ensure curricular relevance and agility.

  Keywords

Ensemble learning, Educational data mining, Hybrid model, Learning analytics, Machine learning, Online learning