Cited by 148Artificial Intelligence in EducationDigital literacy in educationTeaching and Learning Programming

Why and What to Teach: AI Curriculum for Elementary School

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Kim, Seonghun, Jang, Yeonju, Kim, Woojin, Choi, Seongyune, Jung, Heeseok, Kim, Soohwan, Kim, Hyeoncheol·2021·Proceedings of the AAAI Conference on Artificial Intelligence

Abstract

With the rapid technological change of society with Artificial Intelligence, elementary schools' goal should be to prepare the next generations according to competencies. We propose an AI curriculum to cultivate students' AI literacy to answer the question of ‘why and what to teach’ on AI. The proposed AI curriculum focuses on achieving AI literacy based on three competencies: AI Knowledge, AI Skill, and AI Attitude. We anticipate that the proposed curriculum will equip students with core competencies for the future with AI.

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Research works printed but not linked: 16

  1. Buolamwini, J.; and Gebru, T. 2018. Gender shades: Inter- sectional accuracy disparities in commercial gender classifi- cation. In Conference on fairness, accountability and trans- parency, 77-91.
  2. Callan, R. 2003. Artificial Intelligence. Palgrave MacMillan.
  3. Charniak, E.; and McDermott, D. 1987. Introduction to Ar- tificial Intelligence. USA: Addison-Wesley Longman Pub- lishing Co., Inc. ISBN 0201119455.
  4. Han, A.; Kim, J.; and Wohn, K. 2015. Entry: visual pro- gramming to enhance children's computational thinking. In Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers, 73-76.
  5. Luger, G. F. 2007. Artificial intelligence: structures and strategies for complex problem solving. Pearson education.
  6. Manzini, T.; Lim, Y. C.; Tsvetkov, Y.; and Black, A. W. 2019. Black is to criminal as caucasian is to police: De- tecting and removing multiclass bias in word embeddings. arXiv preprint arXiv:1904.04047 . · arXiv
  7. Neapolitan, R. E.; and Jiang, X. 2018. Artificial intelligence: With an introduction to machine learning. CRC Press.
  8. Nilsson, N. J. 1998. The quest for artificial intelligence. Cambridge University Press.
  9. Poole, D. L.; and Mackworth, A. K. 2010. Artificial Intel- ligence: foundations of computational agents. Cambridge University Press.
  10. McGraw-Hill Higher Education. ISBN 0070522634.
  11. Russell, S.; and Norvig, P. 2002. Artificial intelligence: a modern approach. Prentice Hall.
  12. Sabuncuoglu, A. 2020. Designing One Year Curriculum to Teach Artificial Intelligence for Middle School. In Proceed- ings of the 2020 ACM Conference on Innovation and Tech- nology in Computer Science Education, 96-102.
  13. Sahami, M. 2015. Computer science curricula 2013 (CS2013) AI and the intelligent systems knowledge area. AI Matters 1(3): 4-5.
  14. Touretzky, D.; Gardner-McCune, C.; Martin, F.; and See- horn, D. 2019. Envisioning AI for K-12: What should every child know about AI? In Proceedings of the AAAI Confer- ence on Artificial Intelligence, volume 33, 9795-9799.
  15. Wing, J. M. 2006. Computational thinking. Communica- tions of the ACM 49(3): 33-35.
  16. Winston, P. H. 1992. Artificial Intelligence (3rd Ed.). USA: Addison-Wesley Longman Publishing Co., Inc. ISBN 0201533774.

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Authors
Kim, S., Jang, Y., Kim, W., Choi, S., Jung, H., Kim, S., & Kim, H.
Affiliations
Korea University (KR)
Venue
Proceedings of the AAAI Conference on Artificial Intelligence
Published
2021-05-18
Volume
35
Issue
17
Pages
15569-15576
ISSN
  • 2159-5399 (ISSN-L)
  • 2374-3468
OpenAlex
W3174307976
Cited by
148
References
2
Citations by year
2026: 30 · 2025: 51 · 2024: 29 · 2023: 16 · 2022: 21 · 2021: 1
FWCI
13.48
Type
conference paper
Language
en
Open access
Locations
Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
Funders
  • Iran Telecommunication Research Center
  • Ministry of Science and ICT, South Korea (IITP-2018, IITP-2018-0-01405)
  • Institute for Information and Communications Technology Promotion (IITP-2018-0-01405)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Physical Sciences › Computer Science › Information Systems › Digital literacy in education
  • Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
Keywords
artificial intelligence in education, elementary school curriculum, AI literacy, AI knowledge, AI competencies, attitudes toward AI, competency-based education

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