본문 바로가기
KERD
논문KCI 등재

A Study on Artificial Intelligence Education Design for Business Major Students

서응교|박소현
한국유통과학회 (Korea Distribution Science Association)|2021.08|산경연구논집 (The Journal of Industrial Distribution & Business)|제12권 제8호|pp. 21-32

1

조회

0

다운로드

2

인용

0

참고문헌

키워드

Public DataData LiteracyAI EducationBusiness Major

초록

Purpose: With the advent of the era of the 4th industrial revolution, called a new technological revolution, the necessity of fostering future talents equipped with AI utilization capabilities is emerging. However, there is a lack of research on AI education design and competency-based education curriculum as education for business major. The purpose of this study is to design AI education to cultivate competency-oriented AI literacy for business major in universities. Research design, data and methodology: For the design of AI basic education in business major, three expert Delphi surveys were conducted, and a demand analysis and specialization strategy were established, and the reliability of the derived design contents was verified by reflecting the results. Results: As a result, the main competencies for cultivating AI literacy were data literacy, AI understanding and utilization, and the main detailed areas derived from this were data structure understanding and processing, visualization, web scraping, web crawling, public data utilization, and concept of machine learning and application. Conclusions: The educational design content derived through this study is expected to help establish the direction of competency-centered AI education in the future and increase the necessity and value of AI education by utilizing it based on the major field.

Abstract

Purpose: With the advent of the era of the 4th industrial revolution, called a new technological revolution, the necessity of fostering future talents equipped with AI utilization capabilities is emerging. However, there is a lack of research on AI education design and competency-based education curriculum as education for business major. The purpose of this study is to design AI education to cultivate competency-oriented AI literacy for business major in universities. Research design, data and methodology: For the design of AI basic education in business major, three expert Delphi surveys were conducted, and a demand analysis and specialization strategy were established, and the reliability of the derived design contents was verified by reflecting the results. Results: As a result, the main competencies for cultivating AI literacy were data literacy, AI understanding and utilization, and the main detailed areas derived from this were data structure understanding and processing, visualization, web scraping, web crawling, public data utilization, and concept of machine learning and application. Conclusions: The educational design content derived through this study is expected to help establish the direction of competency-centered AI education in the future and increase the necessity and value of AI education by utilizing it based on the major field.

참고문헌 (34)

  1. Cronbach, L. J., "My Current Thoughts on Coefficient Alpha and Successor Procedures", Educational and Psychological Measurement, 64(3), pp.391~418 (2004)
  2. Diez, D., "OpenIntro Statistics" (2016)
  3. Frey, C. B., "The future of employment:How susceptible are jobs to computerization", Technological forecasting and social change, 114(c), pp.254~280 (2017)
  4. "The global state of the art in engineering education" (2018)[URL]
  5. 홍지연, "초등학생의 데이터 리터러시 함양을 위한 AI 데이터 과학 교육 프로그램 개발", 정보교육학회논문지, 24(6), pp.633~641 (2020), 한국정보교육학회
  6. Japanese AI policy isomorphism and implementation strategy, "Institute of Information and Communications Technology Planning and Evaluation", IITP Global ICT R&D Policy, 1~24 (2017)
  7. 조헌국, "4차 산업혁명에 따른 대학교육의 변화와 교양교육의 과제", 교양교육연구, 11(2), pp.53~89 (2017), 한국교양교육학회
  8. Future Job Prospects of the 4th Industrial Revolution, "Kim, D. G.", Korea Employment Information Service Report, ~ (2017)
  9. 김기봉, "인공지능 시대 인문학과 교양으로서 역사", 역사문화연구, (67), pp.241~268 (2018), 역사문화연구소[DOI]
  10. 김신애, "인공지능 시대의 교육을 위한 ‘또 하나의 관점’", 교육원리연구, 24(1), pp.83~105 (2019), 한국교육원리학회
  11. Kim, Y. S., "Global Trend of AI Talent Development Policy in Major Countries", Monthly Software Oriented Society, 62(8), pp.29~36 (2019)
  12. 2019 Global SW Education Conference Issue Paper: Rethinking SW Education in the Era of AI, "Korea Foundation for the Advancement of Science and Creativity", Korea Science and Creativity Foundation, ~ (2019)
  13. Lawshe, C. H., "A quantitative approach to content validity", Personnel psychology, 28(4), pp.563~575 (1975)
  14. 이은경, "국내외 초·중등학교 인공지능 교육과정 분석", 컴퓨터교육학회 논문지, 23(1), pp.37~44 (2020), 한국컴퓨터교육학회
  15. Long, D., "What is AI literacy? Competencies and design considerations", Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (2020), Association for Computing Machinery
  16. Okoli, C., "The Delphi method as a research tool: an example, design considerations and applications", Information & Management, 42(1), pp.15~29 (2004)
  17. 박대륜, "머신러닝 플랫폼을 활용한 소프트웨어 교수-학습 모형 개발", 정보교육학회논문지, 24(1), pp.49~57 (2020), 한국정보교육학회
  18. 박소현, "비전공자 소프트웨어 기초교육을 위한 프로그래밍 언어 결정에 관한 연구", 정보시스템연구, 28(4), pp.403~424 (2019), 한국정보시스템학회[DOI]
  19. 박소현, "비전공자를 위한 알고리즘씽킹 기반 소프트웨어 기초교육 설계", 산경연구논집, 10(11), pp.71~80 (2019), 한국유통과학회
  20. Rauch, W. G., "The decision Delphi", Technological Forecasting and Social Change, 15, pp.159~169 (1979)
  21. Richardson, I., "Problem based learning in the software engineering classroom", Proceedings of the 22nd Conference on Software Engineering Education and Training IEEE, pp.174~181 (2009)
  22. Roll, I., "Evolution and revolution in artificial intelligence in education", International Journal of Artificial Intelligence Education, 26(2), pp.582~599 (2016)
  23. Rowe, G., "Delphi-a reevaluation of research and theory", Technological Forecasting and Social Change, 39(3), pp.235~251 (1991)
  24. 류혜인, "4P기반의 K-12 대상 인공지능 교육을 위한 교육체계 개발", 디지털융복합연구, 19(1), pp.141~149 (2021), 한국디지털정책학회[DOI]
  25. 류미영, "딥러닝 개념을 위한 인공지능 교육 프로그램", 정보교육학회논문지, 23(6), pp.583~590 (2019), 한국정보교육학회
  26. Streiner, D. L., "Health measurement scales: A practical guide to their development and use" (2015), Oxford
  27. 성정숙, "인문계열 학생을 위한 SW교육에서의초보 학습자 특성 분석", 컴퓨터교육학회 논문지, 18(3), pp.25~35 (2015), 한국컴퓨터교육학회[DOI]
  28. "The artificial intelligence national strategy" (2019)[URL]
  29. "World Economic Forum Annual Meeting 2016: Mastering the Fourth Industrial Revolution" (2016)[URL]
  30. "The Future of Jobs Report 2018" (2018)[URL]
  31. 우호성, "SW중심대학의 인공지능 교육과정 현황분석", 컴퓨터교육학회 논문지, 23(2), pp.13~20 (2020), 한국컴퓨터교육학회[DOI]
  32. Woudenberg, F., "An evaluation of Delphi", Technological Forecasting and Social Change, 40, pp.131~150 (1991)
  33. 유인환, "AI교육의 필요성 분석에 따른 미래 방향 탐색", 정보교육학회논문지, 24(5), pp.423~431 (2020), 한국정보교육학회[DOI]
  34. "The Zhilong X Plan allows artificial intelligence to empower the whole process of education" (2019)[URL]

관련 논문AI추천

비전공자 인문계열을 위한 인공지능(AI) 보편적 교육 설계백수진, 신윤희한국디지털정책학회 · 디지털융복합연구2021 · 제19권 제5호AI 교육을 위한 전공별 맞춤형(RAS) 교육과정 개발연구백란한국공학교육학회 · 공학교육연구2022 · 제25권 제5호A Study on the Artificial Intelligence Curriculum for Non-majors권정인, 이광수인문사회과학연구소 · 인문사회과학연구2023 · 제66권 제3호Analyzing the Effects of AI Education Program based on AI ToolsAeri Lee동북아학술저널연합 · Robotics & AI Ethics2021 · 제6권 제2호컴퓨터 비전공자의 AI 교육 과정 연구조미영, 한옥영한국교양교육학회 · 교양교육연구2022 · 제16권 제3호The Effect of ARTIFICIAL INTELLIGENCE Literacy Education on University Students' Ethical Consciousness of Artificial IntelligenceAeri Lee동북아학술저널연합 · Robotics & AI Ethics2021 · 제6권 제3호

이 간행물의 다른 논문 (3건)

A Study on Audit Regulation Engagement Interview and Audit QualityHong YIN, Yanbin DUpp. 7-19The Influencing of Aging on Time Preference in Indonesia김도형pp. 33-39Effects of Wage on FDI Inflows Based on the Threshold of Institutional QualitySunhae Lee, Young-Hoon JEONpp. 41-52
원문 보기