Artificial Intelligence Conversational Agent for Early Detection of Socioemotional Risk in Upper Secondary Education
Keywords:
Conversational agent, Artificial intelligence, Socioemotional risk, Upper secondary education, Student well-beingAbstract
This study aimed to design and implement an artificial intelligence-based pedagogical conversational agent to support the early detection of socio-emotional risk among upper secondary education students. The research adopted a mixed-methods approach with a descriptive-exploratory scope and an applied case study design conducted at CONALEP Lerma Campus, State of Mexico. A total of 74 students participated, and their open-ended responses were analyzed using Natural Language Processing (NLP), sentiment analysis, and rule-based classification techniques implemented in Python and RStudio. The results identified patterns associated with anxiety, sadness, emotional isolation, and academic stress, while also classifying different socio-emotional risk levels. Furthermore, the system generated empathetic responses aimed at preventive support. The findings suggest that artificial intelligence can strengthen institutional strategies for socio-emotional well-being and early detection processes in technical upper secondary education settings.
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