토픽모델링 기반의 인공지능 교육 연구 동향 분석
An Analysis of Artificial Intelligence Education Research Trends Based on Topic Modeling
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초록
본 연구의 목적은 국내 인공지능 교육의 최근 연구 동향을 분석하여 향후 인공지능 교육의 방향성을 모색하는 것이다. 2016년부터 2023년 11월까지 RISS(Research Information Sharing Service)에게재된 논문 중 인공지능 교육 관련 논문 697편을 대상으로 워드 클라우드(Word Cloud)와 LDA 토픽 모델링(Latent Dirichlet Allocation Topic Modeling) 기법을 활용하여 분석하였다. 분석결과, 주요 토픽으로는 생성형 인공지능 활용 교육, 인공지능 윤리 교육, 인공지능 융합 교육, 인공지능활용에 대한 교사 인식과 역할, 대학 교육에서 인공지능 리터러시(Literacy) 개발, 인공지능 기반교육과 연구 방향으로 여섯 가지가 도출되었다. 분석결과를 토대로, (1) 다양한 교과목에 생성형인공지능 활용 확대, (2) 인공지능 사용을 위한 윤리적 지침, (3) 인공지능 교육의 장기적 영향 평가, (4) 고등교육에서 교사의 인공지능 활용 역량, (5) 대학의 인공지능 교육과정 다양화, (6) 인공지능 연구 추이 분석 및 교육 플랫폼(Platform) 개발 등을 제안하였다.
Abstract
This study aimed to analyze recent research trends in Artificial Intelligence (AI) education within South Korea with the overarching objective of exploring the future direction of AI education. For this purpose, an analysis of 697 papers related to AI education published in Research Information Sharing Service (RISS) from 2016 to November 2023 were analyzed using word cloud and Latent Dirichlet Allocation (LDA) topic modeling technique. As a result of the analysis, six major topics were identified: generative AI utilization education, AI ethics education, AI convergence education, teacher perceptions and roles in AI utilization, AI literacy development in university education, and AI-based education and research directions. Based on these findings, I proposed several suggestions, (1) including expanding the use of generative AI in various subjects, (2) establishing ethical guidelines for AI use, (3) evaluating the long-term impact of AI education, (4) enhancing teachers' ability to use AI in higher education, (5) diversifying the curriculum of AI education in universities, (6) analyzing the trend of AI research, and developing an educational platform.
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