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Digital Content to Improve Artificial Intelligence Literacy Ability

  • Han, Sun Gwan (Dept. of Computer Education, Gyeongin National University of Education)
  • Received : 2020.12.22
  • Accepted : 2020.12.24
  • Published : 2020.12.31

Abstract

This study aims to design and develop effective digital contents to improve the ability for artificial intelligence literacy. First, we defined AI literacy and analyzed the competencies required for artificial intelligence literacy. After selecting the educational elements for AI ability, we composed 10 educational programs. To confirm the appropriateness of designed contents, we verified through content validity test by 10 experts. The CVI value was over 0.75, which was highly valid. The developed content was installed on the online system and applied to 55 AI beginners for 4 weeks. The learners showed a positive result of at least 3.85 in the items of content difficulty, understanding, effectiveness, and learning challenge. As a result of this analysis, we can see that the developed content is positive for helping many people understand AI and improving AI literacy.

이 연구는 인공지능 소양 능력을 향상시키기 위해 효과적인 디지털 콘텐츠의 설계와 개발을 목적으로 한다. 인공지능 소양에 대한 정의를 내리고 인공지능 소양에 필요한 역량을 분석하였다. 인공지능 역량에 맞는 교육 요소를 선정한 뒤에 이에 맞는 10개의 소양교육 콘텐츠로 구성하였다. 콘텐츠의 설계와 개발 내용은 전문가 10명에게 내용타당도 검사를 실시하여 검증하였다. CVI 값은 0.75 이상으로 타당성이 높게 나왔다. 개발된 콘텐츠는 온라인 시스템에 탑재하여 55명의 AI 초보자들에게 적용하였다. 학습자들은 콘텐츠의 난이도, 이해도, 효과성, 학습도전성의 검사 항목에서 최소 3.85 이상의 긍정적인 결과를 보였다. 본 연구에서 개발된 콘텐츠가 많은 사람들에게 인공지능의 이해에 도움을 주고 인공지능 소양을 향상시키는데 긍정적임을 확인할 수 있었다.

Keywords

References

  1. D. Long, B. Magerko, What is AI Literacy? Competencies and Design Considerations, Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 2020, pp. 1-16
  2. J. H. Kim, Opportunities and Threats of Artificial Intelligence, 2019 SPRi Fall Conference, 2019 https://spri.kr/posts/view/22828?code=conference
  3. S. Russell, P. Norvig, Artificial Intelligence: A Modern Approach. 2020, New Jersey: Prentice Hall.
  4. P. Freire. 1972. Pedagogy of the oppressed. Herder and Herder, New York.
  5. C. D'Ignazio. Creative data literacy. Information Design Journal, 2017, vol23, no1, pp. 6-18. https://doi.org/10.1075/idj.23.1.03dig
  6. D. Bawden. Origins and concepts of digital literacy, Digital literacies: Concepts, policies and practices 2008. vol.30, pp.17-32, Peter Lang publisher.
  7. S. Druga, S. Vu, E. Likhith, and T. Qiu. 2019. Inclusive AI literacy for kids around the world, 2019 Proceedings of FabLearn, 2019 pp.104-111.
  8. ACM, IEEE-CS, Draft Report of the Computing Curricula 2020 Project, 2020, https://www.cc2020.net
  9. M. Y. Ryu, S. K. Han, AI Education Programs for Deep-Learning Concepts, Journal of The Korean Association of Information Education, 2019, Vol.23, No.6, pp.583-590. https://doi.org/10.14352/jkaie.2019.23.6.583
  10. M. Y. Ryu, S. K. Han, Development of Digital Contents to Improve Computational Thinking, Journal of The Korea Society of Computer and Information, Vol. 22 No. 12, 2017, pp. 87-93. https://doi.org/10.9708/JKSCI.2017.22.12.087
  11. E. S. Jang, J. Kim, Development of Artificial Intelligence Education Contents based on Tensor-flow for Reinforcement of SW Convergence Gifted Teacher Competency, Journal of Korean Society for Internet Information, 2019, vol 20. no 6, pp.167-178.
  12. J. A. Yu, A study on AI Education in Graduate School through IPA, Journal of The Korean Association of Information Education, 2019, Vol.23 no6, pp.675-687 https://doi.org/10.14352/jkaie.2019.23.6.675
  13. S. G. Han. AI education framework, 2020 http://computing.or.kr/wp-content/uploads/2020/02/AI-Education-Framework-1-for-Korea.pdf.
  14. M. R. Lynn, Determination and quantification of content validity, Nursing Research, 1986, vol 35. no 6, pp.382-385
  15. V. P. Tilden, C. A. Nelson, and B. A. May, Use of qualitative methods to enhance content validity, Nursing Research, 1990, vol 39 no 3, pp. 172-5.