DOI QR코드

DOI QR Code

Development of checklist questions to measure AI capabilities of elementary school students

초등학생의 AI 역량 측정을 위한 체크리스트 문항 개발

  • Eun Chul Lee (Dept. of Child Education, Baekseok University) ;
  • YoungShin Pyun (Dept. of Child Education, Baekseok University)
  • 이은철 (백석대학교 사범학부) ;
  • 변영신 (백석대학교 사범학부)
  • Received : 2024.05.21
  • Accepted : 2024.06.14
  • Published : 2024.06.30

Abstract

The development of artificial intelligence technology changes the social structure and educational environment, and the importance of artificial intelligence capabilities continues to increase. This study was conducted with the purpose of developing a checklist of questions to measure AI capabilities of elementary school students. To achieve the purpose of the study, a Delphi survey was used to analyze literature and develop questions. For literature analysis, two domestic studies, five international studies, and the Ministry of Education's curriculum report were collected through a search. The collected data was analyzed to construct core competency measurement elements. The core competency measurement elements consisted of understanding artificial intelligence (6 elements), artificial intelligence thinking (4 elements), artificial intelligence ethics (4 elements), and artificial intelligence social-emotion (3 elements). Considering the knowledge, skills, and attitudes of the constructed measurement elements, 19 questions were developed. The developed questions were verified through the first Delphi survey, and 7 questions were revised according to the revision opinions. The validity of 19 questions was verified through the second Delphi survey. The checklist items developed in this study are measured by teacher evaluation based on performance and behavioral observations rather than a self-report questionnaire. This has the implication that the measurement results of competency are raised to a reliable level.

인공지능 기술의 발전은 사회의 구조와 교육환경을 변화시키며, 인공지능 역량의 중요성이 지속적으로 증가하고 있다. 이에 본 연구는 초등학생의 AI 역량 측정을 위한 체크리스트 문항을 개발하는 목적으로 수행되었다. 연구의 목적을 달성하기 위해서 문헌 분석과 문항개발 델파이 조사를 사용하였다. 문헌 분석을 위해 검색을 통해 국내 연구 2편, 국외 연구 5편, 교육부의 교육과정 보고서를 수집하였다. 수집된 자료를 분석해서 핵심역량 측정 요소를 구성하였다. 핵심역량 측정 요소는 인공지능의 이해(6개 요소), 인공지능 사고(4개 요소), 인공지능 윤리(4개 요소), 인공지능 사회-정서(3개 요소)로 구성하였다. 구성된 측정 요소의 지식과 기능 그리고 태도를 고려하여, 19개 문항을 개발하였다. 개발된 문항은 1차 델파이 조사를 통해서 검증하였고, 수정의견에 따라 7개의 문항을 수정하였다. 2차 델파이 조사를 통해서 19개 문항의 타당성을 검증하였다. 본 연구에서 개발한 체크리스트 문항은 자기보고식 설문이 아닌 수행 및 행동 관찰을 기반으로 교사의 평가에 의해서 측정된다. 이에 역량의 측정 결과가 신뢰할 수 있는 수준으로 높아진다는 시사점을 가지고 있다.

Keywords

References

  1. E.C.Lee and J.S.Han, "Research on AI education content system composition for early childhood education," Journal of Internet of Things and Convergence, Vol.9 No.5, pp.31-37, 2023.
  2. Ministry of Education, "Practical (Technical Home) Information Science curriculum,"Ministry of Education, 2022.
  3. Ministry of Education, "Opening the era of 1:1 personalized education with artificial intelligence (AI) digital textbooks,"Ministry of Education, 2023.
  4. D.T.K.Ng, J.K.L.Leung and S.K.W.Chu and M.S.Qiao, "Conceptualizing AI literacy: An exploratory review," Computers and Education: Artificial Intelligence, Vol.2, pp.1-11, 2021.
  5. S.C.Kong, M.Y.W.Cheung and O.Tsang, "Developing an artificial intelligence literacy framework: Evaluation of a literacy course for senior secondary students using a project-based learning approach," Computers and Education: Artificial Intelligence, Vol.6, pp.1-11, 2024.
  6. T.K.F.Chiu, Z.Ahmad, M.Ismailov and I.S.Sanusi, "What are artificial intelligence literacy and competency? A comprehensive framework to support them," Computers and Education Open, Vol.6, pp.1-9, 2024.
  7. M.Y.Ryu and S.K.Han, "The Study on Test Standard for Measuring AI Literacy," Journal of the Korea Computer and Information Society, Vol.28 No.7, pp. 39-46, 2023.
  8. S.K.Jo and M.S.Choi, "Modeling core competencies for elementary artificial intelligence education," Core Competency Education Research, Vol.7 No.1, pp.43-75,2022.
  9. E.C.Lee and Y.S.Pyun, "Research on the composition of AI core competency elements for early childhood AI education," Journal of Internet of Things and Convergence, Vol.9 No.5, pp.55-60, 2023.
  10. M.C.Laupichler, A.Aster, N.Haverkamp and T.Raupach, "Development of the "Scale for the assessment of non-experts' AI literacy" - An exploratory factor analysis," Computers in Human Behavior Reports, Vol.12, pp.1-10, 2023.
  11. S.Davies, M.Janus, E.Duku and A.Gaskin, "Using the Early Development Instrument to examine cognitive and non-cognitive school readiness and elementary student achievement," Early Childhood Research Quarterly, vol.35 pp.63-75, 2016.
  12. A.Carolus, M.J.Koch, S.Straka, M.E.Latoschik and C.Wienrich, "MAILS - Meta AI literacy scale: Development and testing of an AI literacy questionnaire based on well-founded competency models and psychological change- and meta-competencies," Computers in Human Behavior: Artificial Humans, Vol.1 No.2, pp.1-10, 2023.
  13. N.Knoth, M.Decker, M.C.Laupichler, M.Pinski, N.Buchholtz, K.Bata and B.Schultz, "Developing a holistic AI literacy assessment matrix - Bridging generic, domain-specific, and ethical competencies," Computers and Education Open, Vol.6, pp.1-14, 2024.
  14. M.C.Laupichle, A.Aster and T.Raupach, "Delphi study for the development and preliminary validation of an item set for the assessment of non-experts' AI literacy," Computers and Education: Artificial Intelligence, Vol.4, pp.1-10, 2023.
  15. J.H.Choi and Y.S.Jeon, "Development and validation of a digital literacy competency assessment tool to foster digital competency in the artificial intelligence era," Research in University Teaching and Learning, Vol.16 No.3, pp.95-122, 2023.