Intelligent Educational Agent for Education Support Using Long Language Models Through Langchain

Pedro Neira-Maldonado, Diego Quisi-Peralta, Juan Salgado-Guerrero, Jordan Murillo-Valarezo, Tracy Cárdenas-Arichábala, Jorge Galan-Mena, Daniel Pulla-Sanchez

Producción científica: Capítulo del libro/informe/acta de congresoContribución de conferenciarevisión exhaustiva


This paper explores the development of an Intelligent Educational Agent (IEA) with a focus on enhancing the learning experience for university students. In an era where online education is on the rise, there is a growing demand for personalized learning tools. IEAs, powered by artificial intelligence, offer a solution by providing tailored support, explanations, answers to queries, and content adaptation. This study leverages advanced AI technologies, including the LangChain framework and the GPT-3.5 Turbo model from OpenAI, to create an adaptive educational assistant. LangChain facilitates Natural Language Processing and information analysis, while GPT-3.5 Turbo ensures context-aware responses through prompt-tuning. The research methodology involves defining functional requirements, implementing the LangChain framework for NLP, integrating the OpenAI API, and establishing an architecture with three main actors: students, teachers, and tutors/assistants. Results indicate the IEA’s ability to generate precise multiple-choice tests and comprehensive academic plans. The system exhibits contextual understanding and resource generation capabilities. In conclusion, despite challenges like data quality and infrastructure requirements, developing an IEA for content adaptation based on large language models shows great promise. It has the potential to revolutionize education by providing personalized learning experiences and generating educational resources. Collaboration among education experts, developers, and researchers is crucial to fully harness this transformative potential.

Idioma originalInglés
Título de la publicación alojadaInformation Technology and Systems - ICITS 2024
EditoresAlvaro Rocha, Jorge Hochstetter Diez, Carlos Ferras, Mauricio Dieguez Rebolledo
EditorialSpringer Science and Business Media Deutschland GmbH
Número de páginas11
ISBN (versión impresa)9783031542343
EstadoPublicada - 2024
Publicado de forma externa
EventoInternational Conference on Information Technology and Systems, ICITS 2024 - Temuco, Chile
Duración: 24 ene. 202426 ene. 2024

Serie de la publicación

NombreLecture Notes in Networks and Systems
Volumen932 LNNS
ISSN (versión impresa)2367-3370
ISSN (versión digital)2367-3389


ConferenciaInternational Conference on Information Technology and Systems, ICITS 2024

Nota bibliográfica

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.


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