Brain, Digital, & Learning

 Open access, Peer Reviewed

Indexed in KCI

pISSN 2384-2474
eISSN 2586-7490

RESEARCH ARTICLE

Development of Elementary AI•Science Convergence Education Program Using An AI Classification Model

Young Saeng Cho1,2, Yu Chen2, Jung Bog Kim2*

1Seoul Jamsil Elementary School
2Korea National University of Education

Correspondence to Jung Bog Kim, jbkim@knue.ac.kr

Brain, Digital, & Learning. Volume 11, Number 2, 227‒243, June 2021. https://doi.org/10.31216/BDL.20210015
Received on May 7, 2021, Revised on May 27, 2021, Accepted on June 3, 2021, Published on June 30, 2021.
Copyright © 2021 Institute of Brain·AI based Education, Korea National University of Education. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The purpose of this study is to design an artificial intelligence classification model using an AI education platform, and an AI•science convergence program that can be applied to elementary education with the AI classification model. First, an AI classification model was created that has been trained with image data of four representative rock types in the elementary science curriculum. With a recognition rate of 89.1% for the rock samples, it can be fully utilized in the classroom. Based on it, a six-hour-program was developed for elemontary school education. This program was developed based on the 2015 Science Curriculum and the artificial intelligence area of the Standard Model of Next-generation Software Education, and was reviewed by three elementary science education experts and three software education experts to ensure feasibility. The program is easy to be applied to classrooms and will meet the expectations of improving students’ science core competencies and artificial intelligence performance expectation on the Standard Model of Next-generation Software Education.
Keywords

Artificial Intelligence, Teachable Machine, Ai·science convergence education, standard model of next-generation software education

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