A Sustainable Educational Tool for Engineering Education Based on Learning Styles, AI, and Neural Networks Aligning with the UN 2030 Agenda for Sustainable Development

Lauren Genith Isaza Domínguez (), Fabian Velasquez Clavijo, Antonio Robles-Gómez and Rafael Pastor-Vargas
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Lauren Genith Isaza Domínguez: Programa de Doctorado en Tecnologías Industriales, Escuela Internacional de Doctorado UNED (EIDUNED), Universidad Nacional de Educación a Distancia (UNED), 28015 Madrid, Spain
Fabian Velasquez Clavijo: Facultad de Ciencias Básicas e Ingeniería, Universidad de los Llanos UNILLANOS, Villavicencio 500017, Colombia
Antonio Robles-Gómez: Escuela Técnica Superior de Ingeniería Informática (ETSI Informática), Universidad Nacional de Educación a Distancia (UNED), 28015 Madrid, Spain
Rafael Pastor-Vargas: Escuela Técnica Superior de Ingeniería Informática (ETSI Informática), Universidad Nacional de Educación a Distancia (UNED), 28015 Madrid, Spain

Sustainability, 2024, vol. 16, issue 20, 1-24

Abstract: This study addresses the United Nations 2030 Agenda Sustainable Development Goals 4, 8, 10, and 12 by developing a resource-efficient tool that promotes equitable quality education and lifelong learning opportunities, supports decent work and economic growth, reduces inequalities, and ensures sustainable consumption and production patterns. This study contributes to sustainable education by providing a tool that is designed to be easy to use, easy to modify, and resource-efficient, making it accessible to institutions with limited technological resources. The tool uses artificial intelligence and a long short-term memory (LSTM) neural network to provide personalized teaching, adapting to the unique learning styles of its users. A custom survey adapted from the Felder–Silverman model was used to track weekly learning style transitions among 72 engineering students at the Faculty of Engineering at the University of Los Llanos. These data were used to build the LSTM model to predict learning style transitions over a 16-week semester. Two interfaces were created: one for instructors, integrating the LSTM model, and one for students, incorporating a custom survey. An OpenAI API-powered chat was also built into both interfaces to provide study advice to students according to their styles and enable professors to personalize their teaching methodologies in engineering education.

Keywords: engineering education; long short-term memory network (LSTM); personalized education; artificial intelligence (AI); Sustainable Development Goals (SDGs) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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