Análisis Comparativo del Burnout Universitario según el Área de Estudio mediante Estadística Inferencial e IA Generativa - Ruiz-Jurado, A.C., A.C. (RIIIT Revista Internacional de Investigación e Innovación Tecnológica, 2025)
University burnout syndrome affects students’ academic performance and mental health. This study examines its relationship with the field of study using the Maslach Burnout Inventory – General Survey for Students (MBI-GSS) and advanced statistical analysis techniques. For data collection, processing and analysis the ANALYTIQ SURVEYS platform was specifically developed, for this study. This system automates survey data collection, performs data cleaning and preprocessing, generates descriptive and inferential statistics, and interprets results using generative artificial intelligence. Statistical tests such as ANOVA were applied to identify significant differences among disciplines. Findings reveal that medical students exhibit the highest levels of emotional exhaustion, but maintain a healthier level of professional efficacy compared to Engineering and Business Administration students, who show a greater impact in this dimension. AI-driven analysis enabled the identification of key patterns and the generation of personalized recommendations, facilitating preventive and intervention strategies in educational institutions.