Explainable Ensemble Learning for Identifying Digital Citizenship Drivers Among Generative AI Users: A SHAP-Based Behavioral Analysis

Abstract

The increasing integration of Generative Artificial Intelligence (Generative AI) into higher education has transformed how university students learn and interact within digital environments. However, the psychological and behavioral factors influencing responsible Digital Citizenship behavior among Generative AI users remain insufficiently understood. Existing studies have largely relied on descriptive, regression-based, or structural modeling approaches that may overlook complex nonlinear behavioral relationships. This study proposes an explainable ensemble learning framework to identify the dominant drivers of Digital Citizenship among university students using Generative AI technologies. A national-scale dataset consisting of 2,008 students from 41 universities across 22 provinces in Indonesia was analyzed using Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR). Model performance was evaluated using R², MAE, RMSE, and cross-validation procedures. To improve transparency and interpretability, SHAP (Shapley Additive Explanations) was employed to examine feature importance and nonlinear behavioral effects. The results showed that XGBoost achieved the highest predictive performance. SHAP analysis consistently identified technology anxiety, self-efficacy, and attitude toward behavior as the most influential predictors of Digital Citizenship. The findings further revealed nonlinear effects of technology anxiety on responsible AI participation. This study contributes to explainable educational analytics by providing interpretable insights into responsible AI behavior and supporting evidence-based AI governance and digital education policy in higher education.