Abstract
This article presents a machine learning-based approach to predicting the leadership index based on students’ learning activity, digital skills, and the level of use of artificial intelligence tools. In the course of the study, a synthetic dataset of 110 observations was formed, taking into account the statistical characteristics typical of educational analytics. Based on this data, an XGBoost regression model based on the gradient boosting algorithm was developed. The effectiveness of the model was assessed using the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE). The results obtained showed that the proposed model works with high accuracy and can effectively identify nonlinear and complex dependencies in the data. In the course of the study, factor importance, permutation analysis, and partial dependence methods were used to interpret the model results. The analysis results showed that the performance indicator is the main determinant in the formation of the leadership index, and the level of use of artificial intelligence tools was identified as a factor with an important but heterogeneous effect. The scientific novelty of the study is characterized by the provision of an integrated approach to predicting students’ complex competencies by combining educational data analysis and explanatory artificial intelligence methods. The proposed method can serve as a basis for developing decision-making support systems in the field of educational analytics, identifying risk groups, and forming personalized learning strategies.