As artificial intelligence (AI) becomes increasingly integrated into educational environments, adopting a human-centered approach is essential for enhancing student outcomes. This study investigates the role of personality factors in predicting academic performance, emphasizing the need for explainable and ethical AI systems. Utilizing the SAPEx-D (Student Academic Performance Exploration) dataset from Air University, Islamabad, which comprises 494 records, we explore how individual personality traits can impact academic success. We employed advanced regression models, including Gradient Boosting Regressor, K-Nearest Neighbors Regressor, Linear Regression, and Support Vector Regression, to predict students’ Cumulative Grade Point Average (CGPA). Our findings reveal that the Gradient Boosting Regressor achieved an R-squared value of 0.63 with the lowest Mean Squared Error (MSE); incorporating personality factors elevated the R-squared to 0.83, significantly improving predictive accuracy. For letter grade classification, the incorporation of personality factors improved the accuracy for distinct classes to 0.67 and to 0.85 for broader class categories. The integration of the Shapley Additive Explanations (SHAPs) technique further allowed for the interpretation of how personality traits interact with other factors, underscoring their role in shaping academic outcomes. This research highlights the importance of designing AI systems that are not only accurate but also interpretable and aligned with human values, thereby fostering a more equitable educational landscape. Future work will expand on these findings by exploring the interaction effects of personality traits and applying more sophisticated machine learning techniques.

Education is crucial for leading a productive life and obtaining necessary resources. Higher education institutions are progressively incorporating artificial intelligence into conventional teaching methods as a result of innovations in technology. As a high academic record raises a university’s ranking and increases student career chances, predicting learning success has been a central focus in education. Both performance analysis and providing high-quality instruction are challenges faced by modern schools. Maintaining high academic standards, juggling life and academics, and adjusting to technology are problems that students must overcome. In this study, we present a comprehensive dataset, SAPEx-D (Student Academic Performance Exploration), designed to predict student performance, encompassing a wide array of personal, familial, academic, and behavioral factors. Our data collection effort at Air University, Islamabad, Pakistan, involved both online and paper questionnaires completed by students across multiple departments, ensuring diverse representation. After meticulous preprocessing to remove duplicates and entries with significant missing values, we retained 494 valid responses. The dataset includes detailed attributes such as demographic information, parental education and occupation, study habits, reading frequencies, and transportation modes. To facilitate robust analysis, we encoded ordinal attributes using label encoding and nominal attributes using one-hot encoding, expanding our dataset from 38 to 88 attributes. Feature scaling was performed to standardize the range and distribution of data, using a normalization technique. Our analysis revealed that factors such as degree major, parental education, reading frequency, and scholarship type significantly influence student performance. The machine learning models applied to this dataset, including Gradient Boosting and Random Forest, demonstrated high accuracy and robustness, underscoring the dataset’s potential for insightful academic performance prediction. In terms of model performance, Gradient Boosting achieved an accuracy of 68.7% and an F1-score of 68% for the eight-class classification task. For the three-class classification, Random Forest outperformed other models, reaching an accuracy of 80.8% and an F1-score of 78%. These findings highlight the importance of comprehensive data in understanding and predicting academic outcomes, paving the way for more personalized and effective educational strategies.