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Measuring AI Education Performance with Flipped Learning Based on Bloom's Taxonomy Objectives

Authors:
Hyo-Jin Kim
Dongju Kim

Keywords: Artificial intelligence; Education; Flipped learning; Young job seekers

Abstract:
As the importance of Artificial Intelligence (AI) capabilities increases across industries, the demand for AI education for young job seekers is also increasing. However, non-majors in related fields face difficulties in learning due to the lack of specialized learning content and prior learning opportunities. This study aimed to implement an AI education program through flipped learning for young job seekers and evaluate its effectiveness. The study included 80 students from a South Korean university AI training program, randomly assigned into control and experimental groups with 40 majors and 40 non-majors in each. In the end, the experimental group achieved higher academic results than the control group, particularly on higher-level items.

Pages: 15 to 17

Copyright: Copyright (c) IARIA, 2023

Publication date: November 13, 2023

Published in: conference

ISBN: 978-1-68558-087-2

Location: Valencia, Spain

Dates: from November 13, 2023 to November 17, 2023