Assistance System for the Teaching of Natural Numbers to Preschool Children with the Use of Artificial Intelligence Algorithms

William Villegas-Ch. (), Angel Jaramillo-Alcázar and Aracely Mera-Navarrete
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William Villegas-Ch.: Escuela de Ingeniería en Tecnologías de la Información, FICA, Universidad de Las Américas, Quito 170125, Ecuador
Angel Jaramillo-Alcázar: Escuela de Ingeniería en Tecnologías de la Información, FICA, Universidad de Las Américas, Quito 170125, Ecuador
Aracely Mera-Navarrete: Departamento de Sistemas, Universidad Internacional del Ecuador, Quito 170411, Ecuador

Future Internet, 2022, vol. 14, issue 9, 1-18

Abstract: This research was aimed at designing an image recognition system that can help increase children’s interest in learning natural numbers between 0 and 9. The research method used was qualitative descriptive, observing early childhood learning in a face-to-face education model, especially in the learning of numbers, with additional data from literature studies. For the development of the system, the cascade method was used, consisting of three stages: identification of the population, design of the artificial intelligence architecture, and implementation of the recognition system. The method of the system sought to replicate a mechanic that simulates a game, whereby the child trains the artificial intelligence algorithm such that it recognizes the numbers that the child draws on a blackboard. The system is expected to help increase the ability of children in their interest to learn numbers and identify the meaning of quantities to help improve teaching success with a fun and engaging teaching method for children. The implementation of learning in this system is expected to make it easier for children to learn to write, read, and conceive the quantities of numbers, in addition to exploring their potential, creativity, and interest in learning, with the use of technologies.

Keywords: artificial intelligence; perceptron; ICT in education (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
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