A Predictive Model for Academic Major Selection Using AI and Labor Market Trends

Abstract

Selecting the right university major is a crucial decision that influences students' academic success and future careers. This study presents an AI-driven recommendation system using supervised machine learning, focusing on the Random Forest classifier. The system is trained on the "Arab University Graduate Data Set" (1,000 records), including features such as high school GPA, entrance exam scores, and employment rates. Hyperparameter tuning improved model performance, achieving 97.5% accuracy. Feature importance analysis highlighted GPA and employment rate as key factors. Unlike prior work focused on developed regions, this study explores AI’s potential in Yemeni universities with limited guidance resources. Findings support the use of AI tools to align educational decisions with labor market needs, improving student outcomes.