Artificial Intelligence-Based Classification and Prediction of Academic and Psychological Challenges in Higher Education

Background/purpose. University students in Jordan face numerous challenges that affect their lifestyle on campus and academic performance. The most common challenges can be summarized into two important categories: psychological and academic factors. Psychological factors, such as anxiety levels and daily sleep duration, and academic factors such as GPA and study hours, it is worth mentioning that these phenomena may have related influences on each other and along with such interactions may heighten negative effects. Furthermore, there is no solid research on the topic that can provide solutions to both dimensions in one study. This paper provides a novel analysis-based framework to help target students who face these challenges in the early stages to provide quality service and consultation.

Materials/methods. The framework was developed based on a questionnaire that was built based on consultation of psychological and academic expertise to extract features that are related to the important factors. The questionnaire was distributed to 1020 students from several Jordanian universities. The evaluation of data collected through questionnaires included three major sections about demographic, academic, and psychological factors using the SPSS statistical analysis tool to ensure validity and reliability. After that, the Framework categorizes each student's challenges using the Large Language Model (LLM) into academic difficulties, academic and psychological challenges, psychological distress, and normal students. Finally, multiple classifiers are applied to obtain the status of the students.

Results. The results show that the collected features from questionnaires work well with all classifiers with high accuracy. The contributions of this study include analyzing both academic and psychological factors and exploring their correlation through a case study conducted in Jordan. Also, using LLM for categorization along with classifiers provides an early intervention for students who suffer from academic, and psychological challenges or both.

Conclusion. These findings suggest that early interventions targeting both academic and psychological factors are critical for improving student well-being and academic success, providing valuable insights for university support services.

Keywords: Academic and psychological difficulties, large language models (LLM), Jordanian higher education, machine learning algorithms, student well-being, academic performance

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