Advanced Industrial SCIence (산업과 과학)

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A Study on Content Elements for Technology-Ethics Integrated AI Education in K-12 Schools Based on Artificial Intelligence Ethics Standards

인공지능 윤리기준을 기반으로 한 초중등 AI 기술-윤리 통합교육의 내용요소 연구

  • Jeongah Choe (Intelligent Software Education Research Institute, Jeju National University) ;
  • Jungwon Cho (Department of Computer Education, Jeju National University)
  • 최정아 (제주대학교 지능소프트웨어교육연구소) ;
  • 조정원 (제주대학교 컴퓨터교육과)
  • Received : 2025.05.24
  • Accepted : 2025.07.20
  • Published : 2025.07.30
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Abstract

This study hierarchically organized domestic artificial intelligence ethics standards by school level and derived content elements for technology-ethics integrated education. Using qualitative content analysis based on abstraction, complexity, and practicability, elementary schools focused on human rights protection, privacy protection, safety, and respect for diversity; middle schools on data management, transparency, non-infringement, and safety; and high schools on solidarity, publicness, accountability, and transparency. Content elements were configured according to school-level characteristics: elementary schools centered on concrete experiences, middle schools on principle understanding and problem-solving, and high schools on abstract concepts and social responsibility. The learner's developmental stage-based hierarchical approach and technology-ethics integrated education framework are expected to contribute to developing learners' ethical judgment and practical competencies in the AI era.

본 연구는 인공지능 윤리교육의 체계화를 위해 국내 인공지능 윤리기준 10대 핵심요건을 학교급별로 위계화하고, AI기술과 윤리의 통합교육을 위한 내용요소를 도출하였다. 질적 내용분석 방법을 통해 인공지능 윤리기준을 추상성, 복잡성, 실천가능성 기준으로 분석하여 학교급별 중점 요건을 선정하였다. 초등학교는 인권보장, 프라이버시 보호, 안전성, 다양성 존중, 중학교는 데이터 관리, 투명성, 침해금지, 안전성, 고등학교는 연대성, 공공성, 책임성, 투명성을 중점 요건으로 도출하였다. 2022 개정 교육과정과 연계하여 학교급별 특성에 맞는 내용요소를 구성하였으며, 초등학교는 구체적 경험 중심, 중학교는 원리 이해와 문제해결 중심, 고등학교는 추상적 개념과 사회적 책임 중심으로 교육내용을 체계화하였다. 본 연구가 제시한 학습자의 발달 단계별 위계적 접근과 AI 통합교육 프레임워크는 AI 시대 학습자의 윤리적 판단력과 실천 역량 함양에 기여할 것으로 기대된다.

Keywords

Acknowledgement

This work was supported by the Ministry of Education and the National Research Foundation of Korea.(NRF-2021S1A5C2A04088646)

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