Resumen
El estrés académico constituye uno de los principales problemas emocionales en estudiantes universitarios debido a la creciente presión académica y digital presente en la educación superior contemporánea. La presente investigación tuvo como objetivo desarrollar un modelo de inteligencia artificial para la mitigación del estrés académico en estudiantes de administración mediante técnicas de aprendizaje automático y simulación predictiva. El estudio presentó un enfoque cuantitativo, diseño no experimental y alcance explicativo-predictivo, considerando una muestra de 384 estudiantes universitarios. Se emplearon algoritmos como Random Forest, XGBoost, SVM y redes neuronales multicapa para identificar patrones emocionales y académicos asociados al estrés. Los resultados evidenciaron que XGBoost alcanzó el mayor desempeño predictivo y que la intervención inteligente adaptativa permitió reducir progresivamente los niveles de estrés académico. Se concluyó que la inteligencia artificial posee elevado potencial para fortalecer estrategias preventivas y sistemas de apoyo emocional en educación superior.
Citas
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