Athenea Engineering sciences journal

Modeling and Simulation of Intelligent Systems for the Early Detection of Academic Stress in University Students Using Machine Learning

Jose Calizaya Lopez — 2026-05-12

This study develops an intelligent system based on machine learning for the early detection of academic stress in university students, focusing on the areas of Engineering and Social Sciences. Based on a quantitative and simulation-based approach, a structured dataset was built integrating academic, behavioral, and psychosocial variables. Predictive models were implemented, including Random Forest, Support Vector Machines, and XGBoost, and their performance was evaluated using multiclass and binary classification metrics. The results show that ensemble models achieved the highest levels of accuracy, exceeding 90% in the detection of stress levels. Likewise, key variables such as fatigue, concentration, pending assignments, and hours of sleep were identified. Scenario simulation demonstrated that combined interventions generate significant reductions in the probability of high stress. The study provides a predictive-explanatory approach that contributes to decision-making in educational environments oriented toward student well-being.

Computational Models for Reducing Cognitive Overload in Business Administration Students

Karold Roxana Caceres Gomez — 2026-05-21

The study developed a predictive computational model to identify and reduce cognitive overload in undergraduate business administration students in digital environments. A quantitative approach was applied using academic, cognitive, and technological variables related to digital fatigue, academic stress, and the use of virtual platforms. Machine learning algorithms such as logistic regression, SVM, and Random Forest were implemented and complemented with computational simulation. The results showed that Random Forest achieved the best predictive performance and made it possible to detect critical scenarios of cognitive saturation. It is concluded that computational models can contribute to the design of intelligent educational systems oriented toward academic well-being.

Design of an Artificial Intelligence-Based Platform for the Adaptive Management of Anxiety in Digital University Environments

Ferdinand Eddington Ceballos Bejarano — 2026-05-29

The digital transformation of higher education has increased the need to develop technological strategies and engineering designs aimed at supporting students' emotional well-being in virtual environments. The objective of this study was to develop an engineering design to support anxiety management in digital university environments. The study adopted a quantitative approach with a quasi-experimental design and involved 240 university students distributed into an experimental group and a control group. The platform integrated emotional monitoring, predictive analysis, and adaptive intervention mechanisms through machine learning algorithms. The results showed a significant reduction in anxiety, stress, and cognitive load in the experimental group, as well as high levels of predictive accuracy in the implemented models. It is concluded that adaptive artificial intelligence represents a viable alternative for strengthening more human-centered and preventive digital educational ecosystems.