RISS 학술연구정보서비스

검색

인기 검색어

    다국어 입력

    http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.

    변환된 중국어를 복사하여 사용하시면 됩니다.

    예시)
    • 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
    • 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
    닫기

    UNESCO AI 역량 프레임워크에 기반한 2022 개정 교육과정 개선 방안 탐색

    한글로보기

    https://www.riss.kr/link?id=T17374491

    • 0

      상세조회
    • 0

      다운로드
    서지정보 열기
    • 내보내기
    • 내책장담기
    • 공유하기
    • 오류접수
    인용문이 복사되었습니다.

    부가정보

    국문 초록 (Abstract) kakao i 다국어 번역

    본 연구는 인공지능(AI) 기술의 급속한 발전과 함께 교육 현장에서 필수 역량으로 주목받고 있는 AI 리터러시의 효과적 교육을 위한 이론적 토대를 마련하고자 진행되었다. 또한 AI 리터러시의 중요성에 주목하여 현 초등학교 교육과정에서 요구되는 핵심 AI 역량의 반영도와 중요도를 실증적으로 분석하는데 목적이 있다. AI 리터러시는 단순한 문해력을 넘어서 AI의 개념, 활용, 윤리적·사회적 영향에 대한 이해와 창의적 문제 해결 능력을 포함하는 복합적 역량이며, 전 세계적으로 다양한 교육 프레임워크가 제안되어 있다. 본 연구에서는 해당 분야를 대표하는 4개의 AI 리터러시 프레임워크를 선정하여, 글로벌 적용성, 구조적 구성, 윤리성 강조, 융합적 실무 활용 가능성이라는 네 가지 평가 기준에 따라 체계적으로 분석하였다. 해당 프레임워크는 단순 기술 활용을 넘어 서 윤리, 시민성, 문제 해결 등 미래 사회의 핵심 역량이 포함된 지표로 이를 바탕으로 UNESCO AI 역량 프레임워크가 본 연구의 기준으로서 학술적, 정책적 타당성이 높다고 판단되었다. 이러한 프레임워크를 적용함으로써 국내 교육과정의 현황, 강점과 보완점을 글로벌 교육과정과 비교해 보고 AI 리터러시의 실질적 실현을 위한 우선순위를 제시할 수 있다는 점이 본 연구에서 주목할 만한 점이다. 이를 위해 초등 교사 및 석박사와 재학생을 대상으로 중요도-반영도를 묻는 설문을 시행하였고 Borich 요 구도 및 The Locus of Focus 모델을 적용하였다. 분석 결과 인간 중심적 사고방식 영역과 AI 윤리 영역이 1사분면으로 최우선 개선 필요하다는 점이 도출되었다. 반면 AI 시스템 설계와 AI 기술 및 응용 영역은 타 영역에 비해 비교적 충실히 반영되어 저우선 또는 점진적으로 개선 수준으로 도출되었다. 본 연구는 AI 리터러시 및 글로벌 프레임워크 기반의 분석을 통해 향후 교육과정 개선, 성취 기준 보완에 실증적 근거와 시사점을 제공하며 앞으로의 현장 적용 측면에 중요한 의의가 있다.
    번역하기

    본 연구는 인공지능(AI) 기술의 급속한 발전과 함께 교육 현장에서 필수 역량으로 주목받고 있는 AI 리터러시의 효과적 교육을 위한 이론적 토대를 마련하고자 진행되었다. 또한 AI 리터러시...

    본 연구는 인공지능(AI) 기술의 급속한 발전과 함께 교육 현장에서 필수 역량으로 주목받고 있는 AI 리터러시의 효과적 교육을 위한 이론적 토대를 마련하고자 진행되었다. 또한 AI 리터러시의 중요성에 주목하여 현 초등학교 교육과정에서 요구되는 핵심 AI 역량의 반영도와 중요도를 실증적으로 분석하는데 목적이 있다. AI 리터러시는 단순한 문해력을 넘어서 AI의 개념, 활용, 윤리적·사회적 영향에 대한 이해와 창의적 문제 해결 능력을 포함하는 복합적 역량이며, 전 세계적으로 다양한 교육 프레임워크가 제안되어 있다. 본 연구에서는 해당 분야를 대표하는 4개의 AI 리터러시 프레임워크를 선정하여, 글로벌 적용성, 구조적 구성, 윤리성 강조, 융합적 실무 활용 가능성이라는 네 가지 평가 기준에 따라 체계적으로 분석하였다. 해당 프레임워크는 단순 기술 활용을 넘어 서 윤리, 시민성, 문제 해결 등 미래 사회의 핵심 역량이 포함된 지표로 이를 바탕으로 UNESCO AI 역량 프레임워크가 본 연구의 기준으로서 학술적, 정책적 타당성이 높다고 판단되었다. 이러한 프레임워크를 적용함으로써 국내 교육과정의 현황, 강점과 보완점을 글로벌 교육과정과 비교해 보고 AI 리터러시의 실질적 실현을 위한 우선순위를 제시할 수 있다는 점이 본 연구에서 주목할 만한 점이다. 이를 위해 초등 교사 및 석박사와 재학생을 대상으로 중요도-반영도를 묻는 설문을 시행하였고 Borich 요 구도 및 The Locus of Focus 모델을 적용하였다. 분석 결과 인간 중심적 사고방식 영역과 AI 윤리 영역이 1사분면으로 최우선 개선 필요하다는 점이 도출되었다. 반면 AI 시스템 설계와 AI 기술 및 응용 영역은 타 영역에 비해 비교적 충실히 반영되어 저우선 또는 점진적으로 개선 수준으로 도출되었다. 본 연구는 AI 리터러시 및 글로벌 프레임워크 기반의 분석을 통해 향후 교육과정 개선, 성취 기준 보완에 실증적 근거와 시사점을 제공하며 앞으로의 현장 적용 측면에 중요한 의의가 있다.

    더보기

    목차 (Table of Contents)

    • 목차
    • 국문 초록················································································································· i
    • I. 서론······················································································································ 1
    • 1. 연구 배경과 목적·························································································1
    • 2. 연구 문제·······································································································3
    • 목차
    • 국문 초록················································································································· i
    • I. 서론······················································································································ 1
    • 1. 연구 배경과 목적·························································································1
    • 2. 연구 문제·······································································································3
    • 3. 연구의 제한점·······························································································3
    • 4. 연구의 기대효과···························································································4
    • II. 이론적 배경······································································································5
    • 1. AI 프레임워크 분석····················································································5
    • 가. UNESCO AI 역량 프레임워크···························································5
    • 나. AILit Framework ··················································································6
    • 다. AI4K12 5 Big Ideas··············································································8
    • 라. DigCompEdu (EU Digital Competence for Educators) ···············9
    • 2. AI 리터러시································································································10
    • 3. 디지털 리터러시·························································································11
    • 4. 2022 개정 교육과정 초등 실과 교육과정 ···········································12
    • III. 연구의 내용 및 방법···················································································15
    • 1. 프레임워크 비교 분석···············································································15
    • 2. UNESCO AI 역량 프레임워크 선행 연구 분석 ·······························17
    • 3. 2022 개정 교육과정 초등 실과 교육과정 성취 기준 분석 ·············23
    • 4. UNESCO AI 역량 프레임워크와 2022 개정 교육과정 초등 실과
    • 교육과정 간의 매핑 분석··········································································27
    • 5. 전문가 집단 중요도-반영도 조사···························································31
    • 가. 설문 주제 및 연구 절차·······································································31
    • 나. 설문 구성·································································································31
    • 다. 응답자 배경·····························································································32
    • 라. 분석 방법 ·······························································································32
    • IV. 연구 결과····································································································38
    • 1. 분석 결과·································································································38
    • 가. 기본 개념 인지 유무·········································································38
    • 나. 각 역량에 대한 중요도와 반영도 분석·········································39
    • 다. 종합 분석·····························································································44
    • Ⅴ. 결론············································································································· 46
    • 1. 결론 및 시사점·······················································································46
    • 참고문헌············································································································ 48
    • 영문 초록·········································································································· 50
    • 부록···················································································································· 51
    더보기

    분석정보

    View

    상세정보조회

    0

    Usage

    원문다운로드

    0

    대출신청

    0

    복사신청

    0

    EDDS신청

    0

    동일 주제 내 활용도 TOP

    더보기

    주제

    연도별 연구동향

    연도별 활용동향

    연관논문

    연구자 네트워크맵

    공동연구자 (7)

    유사연구자 (20) 활용도상위20명

    이 자료와 함께 이용한 RISS 자료

    나만을 위한 추천자료

    해외이동버튼