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Lessons Learned for AI Education with Elementary Students and Teachers

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Abstract

With accelerating advances in artificial intelligence, it is clear that introducing K-12 students to AI is essential for preparation to interact with and potentially develop AI technologies. To succeed as the workers, creators, and innovators of the future, we argue students should encounter core concepts of AI as early as elementary school. However, building a curriculum that introduces AI content to K-12 students presents significant challenges, such as connecting to prior knowledge, developing curricula that are meaningful for students, and creating content that teachers feel confident to teach. To lay the groundwork for elementary AI education, we investigated the everyday experiences and ideas of students in grades 4 and 5 (ages 9 to 11) about AI to inform possible entry points for learning. This yielded themes around student conceptions, examples, and ethics of AI. For each theme, we juxtapose the student ideas with the teachers’ reflections on those ideas as frames of reference to consider in co-designing curricular approaches.

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References

  • Arzi, H. J., & White, R. T. (2008). Change in teachers’ knowledge of subject matter: A 17-year longitudinal study.Science Education, 92(2),221–251. https://doi.org/10.1002/sce.20239

  • Caixiong, Z. (2019). July5).Guangzhou schools will start pilot AI courses in September.ChinaDaily. https://www.chinadaily.com.cn/a/201907/05/WS5d1e8414a3105895c2e7bc3d.html

  • Creswell., J. (2012). Educational research: Planning, conducting, and evaluating quantitative and qualitative research (4th ed.).Pearson Education

  • Davis, N., Preston, C., & Sahin, I. (2009). Training teachers to use new technologies impacts multiple ecologies: Evidence from a national initiative.British Journal of Educational Technology, 40(5),861–878. https://doi.org/10.1111/j.1467-8535.2008.00875.x

  • diSessa, A. A. (2014). The construction of causal schemes: Learning mechanisms at the knowledge level.Cognitive Science, 38(5),795–850. https://doi.org/10.1111/cogs.12131

  • Duncan, R. G., & Rivet, A. E. (2013). Science learning progressions. Science, 339(6118),396–397.https://doi.org/10.1126/science.1228692

  • Goldman, S. R. (2022). Making teacher and researcher learning visible: Collaborative design as a context for professional growth.Cognition and Instruction, 40.27–54.https://doi-org.proxyiub.uits.iu.edu/https://doi.org/10.1080/07370008.2021.2010212

  • Gomoll, A., Hmelo-Silver, C. E., & Sabanovic, S. (2022). Co-constructing professional vision: Teacher and researcher learning in co-design.Cognition and Instruction, 40,7–26.https://doi-org.proxyiub.uits.iu.edu/https://doi.org/10.1080/07370008.2021.2010210

  • Harlen, W., & Holroyd, C. (1997). Primary teachers’ understanding of concepts of science: Impact on confidence and teaching.International Journal of Science Education, 19(1),93–105

  • Harry, E., Sturges, K. M., & Klingner, J. K. (2005). Mapping the process: An exemplar of process and challenge in grounded theory analysis.Educational Researcher,34(2),3–13. https://doi.org/10.3102/0013189X034002003

  • Hill, H. C., Rowan, B., & Ball, D. L. (2005). Effects of teachers’ mathematical knowledge for teaching on Student Achievement.American Educational Research Journal, 42(2),371–406

  • Innovation and Science Australia (2017). Australia 2030: Prosperity through innovation. Australian Government. Canberra

  • Israel, M., & Lash, T. (2020). From classroom lessons to exploratory learning progressions: Mathematics+ computational thinking. Interactive Learning Environments, 28(3), 362–382

  • Kandlhofer, M., Steinbauer, G., Hirschmugl-Gaisch, S., & Huber, P. (2016). October).Artificialintelligenceandcomputerscienceineducation:Fromkindergartentouniversity.In2016 IEEE Frontiers in Education Conference (FIE)(pp.1–9).IEEE. https://doi.org/10.1007/s11251-010-9152-3

  • Kim, S., Jang, Y., Kim, W., Choi, S., Jung, H., Kim, S., & Kim, H. (2021). May).Why and what to teach: AI curriculum for elementary school. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 17, pp.15569–15576

  • Lauermann, F., Karabenick, S. A., Carpenter, R., & Kuusinen, C.(2017). Teacher motivation and professional commitment in the United States: The role of motivations for teaching, teacher self-efficacy and sense of professional responsibility.InH. M. G. Watt, P. W. Richardson, & K. Smith (Eds.), Current perspectives in social and behavioral sciences. Global perspectives on teacher motivation(p.322–348).Cambridge University Press. https://doi.org/10.1017/9781316225202.011

  • Leary, H., Lee, V. R., & Recker, M. (2014). Morethanjustplainoldtechnologyadoption:Understandingvariationsinteachers’useofanonlineplanningtool.In2014 Proceedings of International Conference of the Learning Sciences (pp.110–117).ICLS

  • Liu, D. (2020). January 4). China ramps up tech education in bid to become artificial intelligence leader.NBC News. https://www.nbcnews.com/news/world/china-ramps-tech-education-bid-become-artificial-intelligence-leader-n1107806

  • Long, D., & Magerko, B. (2020). WhatisAIliteracy?Competenciesanddesignconsiderations.InConference on Human Factors in Computing Systems - Proceedings(pp.1–16).Honolulu. https://doi.org/10.1145/3313831.3376727

  • Männikkö, I., & Husu, J. (2019). Examining teachers’ adaptive expertise through personal practical theories.Teaching and Teacher Education,77,126–137.https://doi.org/10.1016/j.tate.2018.09.016

  • National Research Council (2007). Taking science to school: Learning and teaching science in grades K-8.Washington, DC:The National Academies Press.https://doi.org/10.17226/11625.

  • National Research Council (2012). A Framework for K-12 Science Education: Practices, Crosscutting Concepts, and Core Ideas.Washington, DC:The National Academies Press.https://doi.org/10.17226/13165

  • Oda, M., Noborimoto, Y., & Horita, T. (2021). International Trends in K-12 Computer Science Curricula through Comparative Analysis: Implications for the Primary Curricula. International Journal of Computer Science Education in Schools, 4(4),n4.https://doi.org/10.21585/ijcses.v4i4.102

  • Ottenbreit-Leftwich, A. T., & Biggers, M. (2017). Status of K-14 computer science education in Indiana: Landscape Report.Submitted to the NSF’s ECEP Alliance, the Indiana Department of Education, Governor of Indiana, Code.org, and Indiana legislators. http://bit.ly/CSforINFinalReport

  • Ozturk, Z., Dooley, C. M., & Welch, M. (2018). Finding the hook: Computer science education in elementary contexts.Journal of Research on Technology in Education, 50(2),149–163. https://doi.org/10.1080/15391523.2018.1431573

  • Pedro, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development. http://repositorio.minedu.gob.pe/handle/MINEDU/6533

  • Rich, K. M., Yadav, A., & Schwarz, C. V. (2019). Computational thinking, mathematics, and science: Elementary teachers’ perspectives on integration.Journal of Technology and Teacher Education, 27(2),165–205

  • Rich, K. M., Yadav, A., & Larimore, R. A. (2020). Teacher implementation profiles for integrating computational thinking into elementary mathematics and science instruction.Education and Information Technologies, 25(4),3161–3188. https://doi.org/10.1007/s10639-020-10115-5

  • Rivet, A. E., & Krajcik, J. S. (2008). Contextualizing instruction: Leveraging students’ prior knowledge and experiences to foster understanding of middle school science. Journal of Research in Science Teaching, 45(1),79–100.https://doi.org/10.1002/tea.20203

  • Roschelle, J., Penuel, W., & Shechtman, N. (2006). Co-design of Innovations with Teachers: Definition and Dynamics. In Barab, S. A., Hay, K. E., & Hickey, D. T. (Eds.), Proceedings of ICLS 2006, Vol. 2(pp.606–612).International Society of the Learning Sciences. https://doi.org/10.22318/icls2006.606

  • Saldaña, J. (2016). The coding manual for qualitative researchers. (3rded.).Sage Publications

  • Sandholtz, J., Ringstaff, C., & Dwyer, D. (1997). Teaching with technology: Creating student-centered classrooms.New York:Teachers College Press

  • Scardamalia, M., & Bereiter, C. (2006). Knowledge building.TheCambridge

  • Smith, I. I. I., DiSessa, J. P.,A.A.,&, & Roschelle, J. (1994). Misconceptions reconceived: A constructivist analysis of knowledge in transition.The Journal of the Learning Sciences, 3(2),115–163

  • Tissenbaum, M., & Ottenbreit-Leftwich, A. (2020). A vision of K–12 computer science education for 2030.Communications of the ACM, 63(5),42–44.https://doi.org/10.1145/3386910

  • Touretzky, D. (2020 August 1). What every child (and teacher) should know about artificial intelligence. [Key note presentation] InProceedings of the 10th IEEE Integrated STEM Education Conference. Virtual

  • Touretzky, D., Gardner-McCune, C., Martin, F., & Seehorn, D. (2019). EnvisioningAIforK-12:WhatshouldeverychildknowaboutAI?InProceedings of the AAAI Conference on Artificial Intelligence(Vol.33,pp.9795–9799).Honolulu.https://doi.org/10.1609/aaai.v33i01.33019795

  • Vartiainen, H., Tedre, M., & Valtonen, T. (2020). Learning machine learning with very young children: Who is teaching whom?International Journal of Child-Computer Interaction,25,1–11. https://doi.org/10.1016/j.ijcci.2020.100182

  • Williams, R., Park, H. W., Oh, L., & Breazeal, C. (2019). Popbots: Designing an artificial intelligence curriculum for early childhood education. In 33rd AAAI Conference on Artificial Intelligence(pp.9729–9736). https://doi.org/10.1609/aaai.v33i01.33019729

  • Wong, G. K., Ma, X., Dillenbourg, P., & Huan, J. (2020). Broadening artificial intelligence education in K-12: where to start? ACM Inroads, 11(1), 20–29. https://doi.org/10.1145/3381884

    Article  Google Scholar 

  • Yadav, A., Gretter, S., Good, J., & McLean, T. (2017). Computational thinking in teacher education.In P. J. Rich& C. B. Hodges (Eds.), Emerging research, practice, and policy on computational thinking(pp.205–220). Springer International Publishing. https://doi.org/10.1007/978-3-319-52691-1_13

  • Yoon, S. A. (2018). Complex systems and the learning sciences: Implications for learning, theory, and methodologies. In International Handbook of the Learning Sciences(pp.157–166). Routledge

  • Yoon, S. A., Goh, S. E., & Park, M. (2018). Teaching and learning about complex systems in K–12 science education: A review of empirical studies 1995–2015.Review of Educational Research,88(2),285–325. https://doi.org/10.3102/0034654317746090

  • Zhou, X., Van Brummelen, J., & Lin, P. (2020). Designing AI learning experiences for K-12: Emerging works, future opportunities and a design framework. https://arxiv.org/abs/2009.10228

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Acknowledgements

This work was supported by National Science Foundation Grants DRL-1934128 and DRL-1934153. Any opinions, findings, and conclusions expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. We are grateful to the elementary teachers who have worked with us as part of this work.

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Authors and Affiliations

  1. Indiana University, Bloomington, United States

    Anne Ottenbreit-Leftwich, Krista Glazewski, Minji Jeon, Katie Jantaraweragul, Cindy E. Hmelo-Silver & Adam Scribner

  2. North Carolina State University, Raleigh, United States

    Seung Lee, Bradford Mott & James Lester

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  1. Anne Ottenbreit-Leftwich
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  2. Krista Glazewski
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Correspondence to Anne Ottenbreit-Leftwich.

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The work presented in this paper was approved by Indiana University’s Internal Review Board number 2001835213.

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All student participants signed an assent form to participate in the interviews. Written consent from their parents was obtained prior to the interviews. Students were told they could withdraw at any time during the interviews. All teachers provided consent to participate in the interviews and were told that they could withdraw at the beginning of the interview if they preferred.

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Ottenbreit-Leftwich, A., Glazewski, K., Jeon, M. et al. Lessons Learned for AI Education with Elementary Students and Teachers. Int J Artif Intell Educ 33, 267–289 (2023). https://doi.org/10.1007/s40593-022-00304-3

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