AI-Driven Classification of Academic and Psychological Challenges in Higher Education

Students enrolled in university experience academic along with mental stressors, which negatively impact their academic results. The research implements artificial intelligence methods for classifying and forecasting the difficulties which higher education students encounter within Jordanian universities. An electronic questionnaire was designed through structured procedures and received validation from eight academics before being distributed to 1020 students. Statistical analysis through Statistical Package for the Social Sciences (SPSS) validated the questionnaire data while testing the reliability of its findings. Students were classified into four categories—Academic Difficulties and Academic and Psychological Challenges and Psychological Distress alongside Normal—through the utilization of the GPT-4o mini API as a Large Language Model (LLM). Machine learning algorithms were applied to evaluate classification performance. Support Vector Machine (SVM) demonstrated the best result among classification models with an accuracy rate of 88.2% while Logistic Regression came second with 87.7% accuracy. A significant number of 54.7% students faced academic challenges while 60.8% of students reported psychological issues. The generated results will assist educational institutions by guiding their early prevention programs along with choosing appropriate assistance methods to enhance educational outcomes.