Abstract
Academic stress constitutes one of the main emotional challenges among university students due to the increasing academic and digital pressure present in contemporary higher education. This study aimed to develop an artificial intelligence model for mitigating academic stress in management students through machine learning techniques and predictive simulation. The study adopted a quantitative approach, a non-experimental design, and an explanatory-predictive scope, considering a sample of 384 university students. Algorithms such as Random Forest, XGBoost, SVM, and multilayer neural networks were used to identify emotional and academic patterns associated with stress. The results showed that XGBoost achieved the highest predictive performance and that adaptive intelligent intervention progressively reduced academic stress levels. It is concluded that artificial intelligence has strong potential to strengthen preventive strategies and emotional support systems in higher education.
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