Intelligent Virtual Assistant for students based on personalized scheduling and diagnostic assessment

 

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Bibliografische gegevens
Auteurs: Arauz, Mateo, Roble, Carlos, Wu, Cristhian, Troetsch, Daniel, Vega, Daniel, Villarreal, Vladimir, Chavarría, Miguel
Formaat: artículo original
Status:Versión publicada
Publicatiedatum:2026
Omschrijving:TutorIA, an intelligent virtual assistant, is being developed to improve the academic performance of university students through personalized study recommendations. This initiative arises from the difficulties many students face in organizing their time, selecting appropriate materials, and adapting to increasing academic demands. The system is implemented using a microservices architecture, where a backend service is designed to manage user and intelligent agent data, while these services are consumed by a mobile application developed in Flutter. The agent integrates a reinforcement learning model using the Deep Q-Learning algorithm, which progressively adapts study guides based on student performance. This performance is evaluated using tests proposed by the agent itself, allowing for the identification of areas of difficulty and the adjustment of future recommendations in a personalized manner. The result of this project has largely met its initial objectives, including: the generation of a diagnostic assessment test, the development of a machine capable of learning from student errors, the implementation of mechanisms to explain subject-specific content, and the creation of a personalized study schedule tailored to the user’s availability and needs.
Land:Portal de Revistas TEC
Instelling:Instituto Tecnológico de Costa Rica
Repositorio:Portal de Revistas TEC
Taal:Español
OAI Identifier:oai:ojs.pkp.sfu.ca:article/8749
Online toegang:https://revistas.tec.ac.cr/index.php/tec_marcha/article/view/8749
Keyword:Apredizaje por refuerzo
asistente virtual
evaluación diagnóstica
inteligencia artificial
planificación personalizada
Artificial intelligence
diagnostic assesment
personalized planning
reinforcemente learning
virtual assistant