AI융합교육의 효과에 대한 메타분석

• 영문명: A Meta-Analysis of the Effects of AI Convergence Education
• 발행기관: 한국공학교육학회
• 저자명: 문명현(Myunghyun Moon) 이선복(Sunbok Lee) 이동환(Donghwan Lee)조현명(Hyunmyung Jo) 우예진(Yejin Woo) 김지언(Jieon Kim)
• 간행물 정보: 『공학교육연구』제28권 제3호, 3~14쪽, 전체 12쪽
• 주제분류: 공학 > 기타공학
• 파일형태: PDF
• 발행일자: 2025.05.31

국문 초록

This study conducted a meta-analysis to investigate the impact of AI convergence education on students’ cognitive and affective outcomes. A total of 96 effect sizes were extracted from 26 domestic studies published between 2010 and 2024. The overall effect size (Hedges' g) was .653, indicating a moderate positive effect on academic achievement and affective factors such as attitudes and motivation. We performed moderator analyses to identify the conditions under which AI convergence education is most effective. The effects were more pronounced among elementary school students and in programs lasting more than 13 sessions. Science and mathematics subjects yielded the highest effect sizes, and among technological features, chatbot-based applications demonstrated the greatest impact. We also found that personalized learning approaches significantly enhanced educational outcomes. Through this study, we made a unique contribution by systematically synthesizing over a decade of domestic research and empirically identifying key moderators—educational level, duration, subject domain, and technological characteristics—that influence the effectiveness of AI convergence education. Our findings offer practical implications for the optimal design and implementation of AI-based programs, emphasizing the need for structured and personalized instructional approaches.

목차

Ⅰ. 서 론

Ⅱ. 이론적 배경

Ⅲ. 분석 방법

Ⅳ. 분석 결과

Ⅴ. 결론 및 논의

참고문헌

키워드

AI convergence education Meta-analysis