Predictive Modeling for University Major Selection: An AI-Driven Solution Using Arab Graduate Data

Abstract

Choosing an appropriate university major is crucial for students’ academic and career success. This study presents an AI-driven recommendation system using supervised machine learning, specifically the Random Forest algorithm, to support major selection based on academic performance (GPA, entrance exam scores) and labor market relevance (major-specific employment rates). The system was trained on 1000 student records from the "Arab University Graduate Data Set" and received 97% accuracy, with employment rate and GPAs appearing as the most effective predictions. Unlike previous studies focused on developed countries, this research emphasizes AI ability in the environment with limited resources as Yemeni universities. It provides a scalable solution to coordinate educational alternatives with labor market needs. Future work will integrate personal interests and socio-economic factors to increase privatization.