[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

Tangible computing tools in AI education: Approach to improve elementary students' knowledge, perception, and behavioral intention towards AI

  • Published:
Education and Information Technologies Aims and scope Submit manuscript

Abstract

The popularity of artificial intelligence (AI) has highlighted the necessity of K-12 AI education, particularly at the elementary level. However, the lack of a comprehensive and age-appropriate AI curriculum integrated into school subjects, along with the abstract and complex nature of AI concepts, exacerbates student inequalities. Researchers addressed this by developing an AI curriculum using tangible computing tools and assessed its effectiveness in improving students' AI knowledge, perception, and behavioral intention. The study involved 60 elementary students from the US Midwest. The effectiveness of the curriculum and the students’ learning experiences were investigated. The results demonstrated the success of the curriculum among all students, with improved AI knowledge, perception, and behavioral intention after using tangible computing tools. Four themes about learning experiences were identified: (1) Augmentation of cognitive learning gains, (2) Augmentation of affective attributes, (3) Advantages of utilizing tangible computing tools for AI education, and (4) Obstacles encountered in the process of learning AI. The practical and theoretical contributions and implications of this study are discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

The datasets generated during and/or analyzed during the current study are not publicly available due to the data such as pre-posttests, surveys, videos, and photos containing a teacher and students’ personal identification information that could compromise research participant privacy and consent but are available from the corresponding author on reasonable request.

Abbreviations

AI :

Artificial Intelligence

ML :

Machine Learning

CV :

Computer Vision

NLP :

Natural Language Processing

CT :

Computational Thinking

CS :

Computer Science

STEM :

Science, Technology, Engineering and Mathematics

MANOVA :

Multivariate Analysis of Variance

FGI :

Focus Group Interview

References

  • Ackermann, E. (2001). Piaget’s constructivism, Papert’s constructionism: What’s the difference? Future of Learning Group Publication, 5(3), 438–449.

    Google Scholar 

  • Allen, M. (Ed.). (2017). The SAGE encyclopedia of communication research methods. SAGE publications.

    Google Scholar 

  • Barsalou, L. W. (2008). Grounded cognition. Annual Review of Psychology, 59(1), 617–645. https://doi.org/10.1146/annurev.psych.59.103006.093639

    Article  Google Scholar 

  • Battal, A., AfacanAdanır, G., & Gülbahar, Y. (2021). Computer science unplugged: A systematic literature review. Journal of Educational Technology Systems, 50(1), 24–47. https://doi.org/10.1177/00472395211018801

    Article  Google Scholar 

  • Bers, M. U. (2018). Coding, playgrounds, and literacy in early childhood education: The development of KIBO robotics and Scratch Jr. In Proceedings of 2018 IEEE global engineering education conference (EDUCON) (pp. 2094–2102). The Association of Machinery. https://doi.org/10.1109/EDUCON.2018.8363498

  • Brainin, E., Shamir, A., & Eden, S. (2022). Robot programming intervention for promoting spatial relations, mental rotation and visual memory of kindergarten children. Journal of Research on Technology in Education, 54(3), 345–358. https://doi.org/10.1080/15391523.2020.1858464

    Article  Google Scholar 

  • Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa

    Article  Google Scholar 

  • CarpioCañada, J., Mateo Sanguino, T. J., MereloGuervós, J. J., & Rivas Santos, V. M. (2015). Open classroom: Enhancing student achievement on artificial intelligence through an international online competition. Journal of Computer Assisted Learning, 31(1), 14–31. https://doi.org/10.1111/jcal.12075

    Article  Google Scholar 

  • Chai, C. S., Lin, P. Y., Jong, M. S. Y., Dai, Y., Chiu, T. K., & Huang, B. (2020). Factors influencing students' behavioral intention to continue artificial intelligence learning. In Proceedings of 2020 international symposium on educational technology (ISET) (pp. 147–150). IEEE. https://doi.org/10.1109/ISET49818.2020.00040

  • Chai, C. S., Lin, P. Y., Jong, M. S. Y., Dai, Y., Chiu, T. K., & Qin, J. (2021). Perceptions of and behavioral intentions towards learning artificial intelligence in primary school students. Educational Technology & Society, 24(3), 89–101. Retrieved August 21, 2023, from https://www.jstor.org/stable/27032858

    Google Scholar 

  • Chen, X., Xie, H., & Hwang, G. J. (2020). A multi-perspective study on artificial intelligence in education: Grants, conferences, journals, software tools, institutions, and researchers. Computers and Education: Artificial Intelligence, 1, 100005. https://doi.org/10.1016/j.caeai.2020.100005

    Article  Google Scholar 

  • Cresswell, J. W., & Clark, V. L. P. (2018). Designing and conducting mixed methods research (3rd ed.). SAGE Publications.

    Google Scholar 

  • Creswell, J. W., & Guetterman, T. C. (2019). Educational research: Planning, conducting, and evaluating quantitative and qualitative research (6th ed.). Pearson.

    Google Scholar 

  • Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334. https://doi.org/10.1007/BF02310555

    Article  Google Scholar 

  • Dai, Y., Chai, C. S., Lin, P. Y., Jong, M. S. Y., Guo, Y., & Qin, J. (2020). Promoting students’ well-being by developing their readiness for the artificial intelligence age. Sustainability, 12(16), 6597. https://doi.org/10.3390/su12166597

    Article  Google Scholar 

  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Mis Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

    Article  Google Scholar 

  • DeLyser, R. L., & Born, C. (2021). The future of problem-solving with data and intelligence: Increasing artificial intelligence and data science education across the US. New York, NY.

  • Denzin, N. K., & Lincoln, Y. S. (Eds.). (2011). The Sage handbook of qualitative research. Sage.

    Google Scholar 

  • Dipaola, D., Payne, B. H., & Breazeal, C. (2022). Preparing children to be consumers and designers of AI technologies. In S. C. Kong & H. Abelson (Eds.), Computational thinking education in K-12: Artificial intelligence literacy and physical computing (pp. 182–205). MIT Press.

    Google Scholar 

  • Druga, S., Vu, S. T., Likhith, E., & Qiu, T. (2019). Inclusive AI literacy for kids around the world. In Proceedings of FabLearn, 2019 (pp. 104–111). The Association for Computing Machinery. https://doi.org/10.1145/3311890.3311904

  • Dugard, P., & Todman, J. (1995). Analysis of pre-test-post-test control group designs in educational research. Educational Psychology, 15(2), 181–198. https://doi.org/10.1080/0144341950150207

    Article  Google Scholar 

  • Eguchi, A., Okada, H., & Muto, Y. (2021). Contextualizing AI education for K-12 students to enhance their learning of AI literacy through culturally responsive approaches. KI-Künstliche Intelligenz, 35(2), 153–161. https://doi.org/10.1007/s13218-021-00737-3

    Article  Google Scholar 

  • Fanchamps, N., Specht, M., Slangen, L., & Hennissen, P. (2022). Toward a research agenda for developing computational thinking skills by sense-reason-act programming with robots. In S. C. Kong & H. Abelson (Eds.), Computational thinking education in K-12: Artificial intelligence literacy and physical computing (pp. 229–244). MIT Press.

    Chapter  Google Scholar 

  • Fields, D. A., & Kafai, Y. B. (2020). Hard fun with hands-on constructionist project-based learning. In S. Grover (Ed.), Computer science in K-12: An A-to-Z handbook on teaching programming (pp. 75–82). Edfinity.

    Google Scholar 

  • Fishbein, M., & Ajzen, I. (2010). Predicting and changing behavior: The Reasoned action approach. Psychology Press.

    Google Scholar 

  • Gall, M. D., Gall, J. P., & Borg, W. R. (2006). Experimental research: Designs, part 1. In M. D. Gall, J. P. Gall, & W. R. Borg (Eds.), Educational research: An introduction (8th ed., pp. 379–414). Pearson.

    Google Scholar 

  • García-Campos, M. D., Canabal, C., & Alba-Pastor, C. (2020). Executive functions in universal design for learning: Moving towards inclusive education. International Journal of Inclusive Education, 24(6), 660–674. https://doi.org/10.1080/13603116.2018.1474955

    Article  Google Scholar 

  • Greenwald, E., Leitner, M., & Wang, N. (2021). Learning artificial intelligence: Insights into how youth encounter and build understanding of AI concepts. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 15526–15533. https://doi.org/10.1609/aaai.v35i17.17828

    Article  Google Scholar 

  • Hajjar, S. T. (2018). Statistical analysis: Internal-consistency reliability and construct validity. International Journal of Quantitative and Qualitative Research Methods, 6(1), 27–38.

    Google Scholar 

  • Harlow, D. B., Dwyer, H. A., Hansen, A. K., Iveland, A. O., & Franklin, D. M. (2018). Ecological design-based research for computer science education: Affordances and effectivities for elementary school students. Cognition and Instruction, 36(3), 224–246. https://doi.org/10.1080/07370008.2018.1475390

    Article  Google Scholar 

  • Harry, B., 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

    Article  Google Scholar 

  • Hayes, J. C., & Kraemer, D. J. M. (2017). Grounded understanding of abstract concepts: The case of STEM learning. Cognitive Research: Principles and Implications, 2(1), 7. https://doi.org/10.1186/s41235-016-0046-z

    Article  Google Scholar 

  • Horn, M., & Bers, M. (2019). Tangible computing. In S. A. Fincher & A. V. Robins (Eds.), The Cambridge Handbook of Computing Education Research (pp. 663–678). Cambridge University Press.

    Chapter  Google Scholar 

  • Hsu, T. C., Abelson, H., & Van Brummelen, J. (2022). The effects on secondary school students of applying experiential learning to the conversational AI learning curriculum. International Review of Research in Open and Distributed Learning, 23(1), 82–103. https://doi.org/10.19173/irrodl.v22i4.5474

    Article  Google Scholar 

  • Huang, X. (2021). Aims for cultivating students’ key competencies based on artificial intelligence education in China. Education and Information Technologies, 26(5), 5127–5147. https://doi.org/10.1007/s10639-021-10530-2

    Article  Google Scholar 

  • Hutchins, E., & Nomura, S. (2011). Collaborative construction of multimodal utterances. In J. Streeck, C. Goodwin, & C. LeBaron (Eds.), Embodied interaction: Language and body in the material world (pp. 29–43). Cambridge Press.

    Google Scholar 

  • Ishii, H., & Ullmer, B. (1997). Tangible bits: towards seamless interfaces between people, bits, and atoms. In Proceedings of the ACM SIGCHI Conference on Human factors in computing systems (pp. 234–241). The Association of Computing Machinery. https://doi.org/10.1145/258549.258715

  • Kafai, Y. B., & Burke, Q. (2014). Connected code: Why children need to learn programming. MIT Press.

    Book  Google Scholar 

  • Kandlhofer, M., Steinbauer, G., Hirschmugl-Gaisch, S., & Huber, P. (2016). Artificial intelligence and computer science in education: From kindergarten to university. In Proceedings of 2016 IEEE Frontiers in Education Conference (FIE) (pp. 1–9). The Association for Computing Machinery. https://doi.org/10.1109/FIE.2016.7757570

  • Kaspersen, M. H., Bilstrup, K. E. K., & Petersen, M. G. (2021). The machine learning machine: A tangible user interface for teaching machine learning. In R. Wimmer (Ed.), Proceedings of the Fifteenth International Conference on Tangible, Embedded, and Embodied Interaction (pp. 1–12). The Association of Computing and Machinery. https://doi.org/10.1145/3430524.3440638

    Chapter  Google Scholar 

  • Kim, C., Dinç, E., Lee, E., Baabdullah, A., Zhang, A. Y., & Belland, B. R. (2023a). Revisiting analogical reasoning in computing education: Use of similarities between robot programming tasks in debugging. Journal of Educational Computing Research. https://doi.org/10.1177/07356331221142912

    Article  Google Scholar 

  • Kim, K., Kwon, K., Ottenbreit-Leftwich, A., Bae, H., & Glazewski, K. (2023b). Exploring middle school students’ common naive conceptions of Artificial Intelligence concepts, and the evolution of these ideas. Education and Information Technologies, 28, 9827–9854. https://doi.org/10.1007/s10639-023-11600-3

    Article  Google Scholar 

  • Knox, J. (2020). Artificial intelligence and education in China. Learning, Media, and Technology, 45(3), 298–311. https://doi.org/10.1080/17439884.2020.1754236

    Article  Google Scholar 

  • Kokotsaki, D., Menzies, V., & Wiggins, A. (2016). Project-based learning: A review of the literature. Improving Schools, 19(3), 267–277. https://doi.org/10.1177/1365480216659733

    Article  Google Scholar 

  • Kong, S. C., & Abelson, H. (Eds.). (2022). Computational thinking education in K-12: Artificial intelligence literacy and physical computing. MIT Press.

    Google Scholar 

  • Lacerda Queiroz, R., Ferrentini Sampaio, F., Lima, C., & Machado Vieira Lima, P. (2021). AI from concrete to abstract. AI & Society, 36(3), 877–893. https://doi.org/10.1007/s00146-021-01151-x

    Article  Google Scholar 

  • Lane, D. (2021). Machine learning for kids: A project-based introduction to artificial intelligence. No Starch Press.

    Google Scholar 

  • Lee, V. R., & Recker, M. (2018). Paper circuits: A tangible, low threshold, low-cost entry to computational thinking. TechTrends, 62(2), 197–203. https://doi.org/10.1007/s11528-017-0248-3

    Article  Google Scholar 

  • Lee, I., & Perret, B. (2022). Preparing high school teachers to integrate AI methods into STEM classrooms. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12783–12791. https://doi.org/10.1609/aaai.v36i11.21557

    Article  Google Scholar 

  • Lesgold, A. M. (2019). Learning for the age of artificial intelligence: Eight education competencies. Routledge.

    Book  Google Scholar 

  • Lin, P., & Van Brummelen, J. (2021). Engaging Teachers to Co-Design Integrated AI Curriculum for K-12 Classrooms. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1–12). The Association for Computing Machinery. https://doi.org/10.48550/arXiv.2009.11100

  • Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. SAGE Publications.

    Book  Google Scholar 

  • Lindh, J., & Holgersson, T. (2007). Does Lego training stimulate pupils’ ability to solve logical problems? Computers & Education, 49(4), 1097–1111. https://doi.org/10.1016/j.compedu.2005.12.008

    Article  Google Scholar 

  • Liu, R., Luo, F., & Israel, M. (2023). Technology-integrated computing education in early childhood: A systematic literature review. Journal of Educational Computing, 61(6), 1275–1311. https://doi.org/10.1177/07356331231170383

    Article  Google Scholar 

  • Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–16). The Association of Computing Machinery. https://doi.org/10.1145/3313831.3376727

  • Long, D., Padiyath, A., Teachey, A., & Magerko, B. (2021). The role of collaboration, creativity, and embodiment in AI learning experiences. In Proceedings of C&C '21: Creativity and Cognition (pp. 1–10). The Association of Computing Machinery. https://doi.org/10.1145/3450741.3465264

  • Lui, D., Fields, D., & Kafai, Y. (2019). Student maker portfolios: Promoting computational communication and reflection in crafting e-textiles. In Proceedings of 8th Annual Conference on Creativity and Fabrication in Education (FabLearn 2019) (pp. 10–17) The Association of Computing Machinery. https://doi.org/10.1145/3311890.3311892

  • Marshall, P., Price, S., & Rogers, Y. (2003). Conceptualizing tangibles to support learning. In Proceedings of the 2003 Conference on Interaction Design and Children (pp. 1–109). The Association for Computing Machinery. https://doi.org/10.1145/953536.953551

  • Miles, M. B., Huberman, A. M., & Saldana, J. (2013). Qualitative data analysis, A methods sourcebook (3rd ed.). Sage Publications, Inc.

    Google Scholar 

  • Morse, J. M. (1991). Approaches to qualitative-quantitative methodological triangulation. Nursing Research, 40(2), 120–123.

    Article  Google Scholar 

  • Narahara, T., & Kobayashi, Y. (2018). Personalizing homemade bots with plug & play AI for STEAM education. In Proceedings of SIGGRAPH Asia 2018 Technical Briefs (pp. 1–4). The Association for Computing Machinery. https://doi.org/10.1145/3283254.3283270

  • Nathan, M. J. (2021). Foundations of embodied learning: A paradigm for education. Routledge.

    Book  Google Scholar 

  • Neuberger, L. (2016). Self-reports of information seeking: Is social desirability in play? Atlantic Journal of Communication, 24(4), 242–249. https://doi.org/10.1080/15456870.2016.1208661

    Article  Google Scholar 

  • Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy an exploratory review. Computers and Education Artificial Intelligence, 2, 100041. https://doi.org/10.1016/j.caeai.2021.100041

    Article  Google Scholar 

  • Ng, D. T. K., Lee, M., Tan, R. J. Y., Hu, X., Downie, J. S., & Chu, S. K. W. (2023). A review of AI teaching and learning from 2000 to 2020. Education and Information Technologies, 28(7), 8445–8501. https://doi.org/10.1007/s10639-022-11491-w

    Article  Google Scholar 

  • Ottenbreit-Leftwich, A., Glazewski, K., Jeon, M., Jantaraweragul, K., Hmelo-Silver, C. E., Scribner, A., ... & Lester, J. (2023). Lessons learned for AI education with elementary students and teachers. International Journal of Artificial Intelligence in Education, 33(2), 267–289. https://doi.org/10.1007/s40593-022-00304-3

  • Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. Basic Books.

    Google Scholar 

  • Papert, S. (1991). Situating constructionism. In I. Harel & S. Papert (Eds.), Constructionism (pp. 1–11). Ablex Publishing.

    Google Scholar 

  • Resnick, M. (2017). Lifelong kindergarten: Cultivating creativity through projects, passion, peers, and play. MIT press.

    Book  Google Scholar 

  • Resnick, M., & Silverman, B. (2005). Some reflections on designing construction kits for kids. In Proceedings of the 2005 Conference on Interaction Design and Children (pp. 117–122). The Association of Computing Machinery. https://doi.org/10.1145/1109540.1109556

  • Rhodes, R. E., & Courneya, K. S. (2004). Differentiating motivation and control in the theory of planned behavior. Psychology, Health & Medicine, 9(2), 205–215. https://doi.org/10.1080/13548500410001670726

    Article  Google Scholar 

  • Rodríguez-García, J. D., Moreno-León, J., Román-González, M., & Robles, G. (2021). Evaluation of an online intervention to teach artificial intelligence with learning ml to 10–16-year-old students. In Proceedings of the 52nd ACM technical symposium on computer science education (pp. 177–183). The Association of Computing Machinery. https://doi.org/10.1145/3408877.3432393

  • Rubio, M. A., Romero-Zaliz, R., Mañoso, C., & Angel, P. (2015). Closing the gender gap in an introductory programming course. Computers & Education, 82, 409–420. https://doi.org/10.1016/j.compedu.2014.12.003

    Article  Google Scholar 

  • Sabuncuoglu, A. (2020). Designing a one-year curriculum to teach artificial intelligence for middle school. In Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education (pp. 96–102). The Association for Computing Machinery. https://doi.org/10.1145/3341525.3387364

  • Sakulkueakulsuk, B., Witoon, S., Ngarmkajornwiwat, P., Pataranutaporn, P., Surareungchai, W., Pataranutaporn, P. & Subsoontorn, P. (2018). Kids making AI: Integrating machine learning, gamification, and social context in STEM education. In Proceedings of 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (pp. 1005–1010). IEEE. https://doi.org/10.1109/TALE.2018.8615249

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

    Google Scholar 

  • Sanusi, I. T., Oyelere, S. S., Agbo, F. J., & Suhonen, J. (2021). Survey of resources for introducing machine learning in the K-12 context. In Proceedings of 2021 IEEE Frontiers in Education Conference (FIE) (pp. 1–9). The Association for Computing Machinery. https://doi.org/10.1109/FIE49875.2021.9637393

  • Sanusi, I. T., Omidiora, J. O., Oyelere, S. S., Vartiainen, H., Suhonen, J., & Tukiainen, M. (2023). Preparing middle schoolers for a machine learning-enabled future through design-oriented pedagogy. IEEE Access. https://doi.org/10.1109/ACCESS.2023.3269025

    Article  Google Scholar 

  • Schwandt, T. A. (2001). Dictionary of qualitative inquiry. SAGE Publications.

    Google Scholar 

  • Sentance, S., & Childs, K. (2020). X-ing boundaries with physical computing. In S. Grover (Ed.), Computer science in K-12: An A-to-Z handbook on teaching programming (pp. 250–258). Edfinity.

    Google Scholar 

  • Sentance, S., Waite, J., Hodges, S., MacLeod, E., & Yeomans, L. (2017). “Creating Cool Stuff” Pupils' Experience of the BBC micro: bit. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education (pp. 531–536). The Association for Computing Machinery. https://doi.org/10.1145/3017680.3017749

  • Shin, N., Bowers, J., Krajcik, J., & Damelin, D. (2021). Promoting computational thinking through project-based learning. Disciplinary and Interdisciplinary Science Education Research, 3(1), 1–15. https://doi.org/10.1186/s43031-021-00033-y

    Article  Google Scholar 

  • Southworth, J., Migliaccio, K., Glover, J., Reed, D., McCarty, C., Brendemuhl, J., & Thomas, A. (2023). Developing a model for AI Across the curriculum: Transforming the higher education landscape via innovation in AI literacy. Computers and Education: Artificial Intelligence, 4, 100127. https://doi.org/10.1016/j.caeai.2023.100127

    Article  Google Scholar 

  • Su, J., Yang, W., & Zhong, Y. (2023). Influences of gender and socioeconomic status on children’s use of robotics in early childhood education: A systematic review. Early Education and Development, 34(4), 910–926. https://doi.org/10.1080/10409289.2022.2078617

    Article  Google Scholar 

  • Sullivan, F. R. (2022). Examining the multidimensional learning affordances of robotics for computational thinking and science inquiry. In S. C. Kong & H. Abelson (Eds.), Computational thinking education in K-12: Artificial intelligence literacy and physical computing (pp. 209–228). MIT Press.

    Chapter  Google Scholar 

  • Sullivan, A., & Bers, M. U. (2013). Gender differences in kindergarteners’ robotics and programming achievement. International Journal of Technology and Design Education, 23(3), 691–702. https://doi.org/10.1007/s10798-012-9210-z

    Article  Google Scholar 

  • Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Pearson.

    Google Scholar 

  • Tang, D. (2019). Empowering novices to understand and use machine learning with personalized image classification models, intuitive analysis tools, and MIT App Inventor (Doctoral dissertation, Massachusetts Institute of Technology).

  • Tedre, M., Toivonen, T., Kahila, J., Vartiainen, H., Valtonen, T., Jormanainen, I., & Pears, A. (2021). Teaching machine learning in K–12 classroom: Pedagogical and technological trajectories for artificial intelligence education. IEEE Access, 9, 110558–110572. https://doi.org/10.1109/ACCESS.2021.3097962

    Article  Google Scholar 

  • Thurmond, V. A. (2001). The point of triangulation. Journal of Nursing Scholarship, 33(3), 253–258. https://doi.org/10.1111/j.1547-5069.2001.00253.x

    Article  Google Scholar 

  • Touretzky, D., & Gardner-McCune, C. (2022). Artificial intelligence thinking in K-12. In S. C. Kong & H. Abelson (Eds.), Computational thinking education in K-12: Artificial intelligence literacy and physical computing (pp. 153–180). MIT Press.

    Chapter  Google Scholar 

  • Touretzky, D., Gardner-McCune, C., Martin, F., & Seehorn, D. (2019). Envisioning AI for K-12: What should every child know about AI? Proceedings of the AAAI Conference on Artificial Intelligence, 33, 9795–9799. https://doi.org/10.1609/aaai.v33i01.33019795

    Article  Google Scholar 

  • Trilles, S., & Granell, C. (2020). Advancing preuniversity students’ computational thinking skills through an educational project based on tangible elements and virtual block-based programming. Computer Applications in Engineering Education, 28(6), 1490–1502. https://doi.org/10.1002/cae.22319

    Article  Google Scholar 

  • Van Brummelen, J., Heng, T., & Tabunshchyk, V. (2021). Teaching tech to talk: K-12 conversational artificial intelligence literacy curriculum and development tools. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 15655–15663. https://doi.org/10.1609/aaai.v35i17.17844

    Article  Google Scholar 

  • Vartiainen, H., Toivonen, T., Jormanainen, I., Kahila, J., Tedre, M., & Valtonen, T. (2021). Machine learning for middle schoolers: Learning through data-driven design. International Journal of Child-Computer Interaction, 29, 100281. https://doi.org/10.1016/j.ijcci.2021.100281

    Article  Google Scholar 

  • Wang, D., Zhang, L., Xu, C., Hu, H., & Qi, Y. (2016). A tangible embedded programming system to convey event-handling concept. In Proceedings of the TEI ’16: Tenth International Conference on Tangible, Embedded, and Embodied Interaction (pp. 133–140). The Association for Computing Machinery. https://doi.org/10.1145/2839462.2839491

  • Warshaw, P. R., & Davis, F. D. (1985). Disentangling behavioral intention and behavioral expectation. Journal of Experimental Social Psychology, 21(3), 213–228. https://doi.org/10.1016/0022-1031(85)90017-4

    Article  Google Scholar 

  • Weisberg, S. M., & Newcombe, N. S. (2017). Embodied cognition and STEM learning: An overview of a topical collection in CR: PI. Cognitive Research: Principles and Implications, 2, 1–6. https://doi.org/10.1186/s41235-017-0071-6

    Article  Google Scholar 

  • Williams, R., Park, H. W., & Breazeal, C. (2019). A is for artificial intelligence: the impact of artificial intelligence activities on young children's perceptions of robots. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1–11). The Association for Computing Machinery. https://doi.org/10.1145/3290605.3300677

  • Williams, R., Ali, S., Devasia, N., DiPaola, D., Hong, J., Kaputsos, S. P., ... & Breazeal, C. (2023). AI+ ethics curricula for middle school youth: Lessons learned from three project-based curricula. International Journal of Artificial Intelligence in Education, 33(2), 325–383. https://doi.org/10.1007/s40593-022-00298-y

  • Xia, Q., Chiu, T. K., Lee, M., Sanusi, I. T., Dai, Y., & Chai, C. S. (2022). A self-determination theory (SDT) design approach for inclusive and diverse artificial intelligence (AI) education. Computers & Education, 189, 104582. https://doi.org/10.1016/j.compedu.2022.104582

    Article  Google Scholar 

  • Yang, W. (2022). Artificial intelligence education for young children: Why, what, and how in curriculum design and implementation. Computers and Education: Artificial Intelligence, 3, 100061. https://doi.org/10.1016/j.caeai.2022.100061

    Article  Google Scholar 

  • Zhou, X., Van Brummelen, J., & Lin, P. (2020). Designing AI learning experiences for K-12: Emerging works, future opportunities, and a design framework. arXiv preprint arXiv:2009.10228. https://doi.org/10.48550/arXiv.2009.10228

  • Zimmermann-Niefield, A., Shapiro, R. B., & Kane, S. (2019). Machine learning: How young people can use data from their own bodies to learn about machine learning. XRDS: Crossroads, the ACM Magazine for Students, 25(4), 44–49. https://doi.org/10.1145/3331071

    Article  Google Scholar 

Download references

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Author information

Authors and Affiliations

  1. Instructional Systems Technology, Indiana University, 201 N. Rose Ave., Bloomington, IN, 47405, USA

    Keunjae Kim & Kyungbin Kwon

Authors
  1. Keunjae Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kyungbin Kwon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Keunjae Kim.

Ethics declarations

Competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, K., Kwon, K. Tangible computing tools in AI education: Approach to improve elementary students' knowledge, perception, and behavioral intention towards AI. Educ Inf Technol 29, 16125–16156 (2024). https://doi.org/10.1007/s10639-024-12497-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10639-024-12497-2

Keywords

Search

Navigation