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Exploring middle school students’ common naive conceptions of Artificial Intelligence concepts, and the evolution of these ideas

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Abstract

This study aims to explore the middle schoolers' common naive conceptions of AI and the evolution of these conceptions during an AI summer camp. Data were collected from 14 middle school students (12 boys and 2 girls) from video observations and learning artifacts. The findings revealed 6 naive conceptions about AI concepts: (1) AI was the same as automation and robotics; (2) AI was a cure-all solution; (3) AI was created to be smart; (4) All data can be used by AI; and (5) AI had nothing to do with ethical considerations. The evolution of students’ conceptions of AI was captured throughout the summer camp. This study will contribute to clarifying what naive conceptions of AI were common in young students and investigating design considerations for the AI curriculum in K-12 settings to address them effectively.

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Data availability statement

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

References

  • Ali, S., DiPaola, D., Lee, I., Sindato, V., Kim, G., Blumofe, R., & Breazeal, C. (2021). Children as creators, thinkers, and citizens in an AI-driven future. Computers and Education Artificial Intelligence, 2, 100040. https://doi.org/10.1016/j.caeai.2021.100040

    Article  Google Scholar 

  • Badham, R., Clegg, C., & Wall, T. (2000). Socio-technical theory. John Wiley.

    Google Scholar 

  • Basili, P. A., & Sanford, J. P. (1991). Conceptual change strategies and cooperative group work in chemistry. Journal of Research in Science Teaching, 28(4), 293–304. https://doi.org/10.1002/tea.3660280403

    Article  Google Scholar 

  • Benson, D. L., Wittrock, M. C., & Baur, M. E. (1993). Students preconceptions of the nature of gases. Journal of Research in Science Teaching, 30(6), 587–597. https://doi.org/10.1002/tea.3660300607

    Article  Google Scholar 

  • Borenstein, J., & Howard, A. (2021). Emerging challenges in AI and the need for AI ethics education. AI and Ethics, 1, 61–65. https://doi.org/10.1007/s43681-020-00002-7

    Article  Google Scholar 

  • Borghi, A. M., Binkofski, F., Castelfranchi, C., Cimatti, F., Scorolli, C., & Tummolini, L. (2017). The challenge of abstract concepts. Psychological Bulletin, 143(3), 263–292. https://doi.org/10.1037/bul0000089

    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 

  • Van Brummelen, J., Heng, T., & Tabunshchyk, V. (2021). Teaching tech to talk: K-12 conversational artificial intelligence literacy curriculum and development tools. In M. Neumann, P. Virtue & M. Guerzhoy (Eds.), Proceedings of 2021 AAAI Symposium on Educational Advances in Artificial Intelligence. AAAI. https://doi.org/10.48550/arXiv.2009.05653

  • Cave, S., & Dihal, K. (2019). Hopes and fears for intelligent machines in fiction and reality. Nature Machine Intelligence, 1(2), 74–78. https://doi.org/10.1038/s42256-019-0020-9

    Article  Google Scholar 

  • 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. https://www.jstor.org/stable/27032858

  • Champagne, A., Gunstone, R., & Klopfer, L. (1983). Naive knowledge and science learning. Research in Science and Technological Education, 1(2), 173–183. https://eric.ed.gov/?id=ED225852

  • Chiodini, L., Moreno Santos, I., Gallidabino, A., Tafliovich, A., Santos, A. L., & Hauswirth, M. (2021). A curated inventory of programming language misconceptions. In Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education V. 1 (pp. 380–386). Association for Computing Machinery. https://doi.org/10.1145/3430665.3456343

  • Chiu, T. K., Meng, H., Chai, C. S., King, I., Wong, S., & Yam, Y. (2021). Creation and evaluation of a pretertiary artificial intelligence (AI) curriculum. IEEE Transactions on Education, 65(1), 30–39. https://doi.org/10.1109/TE.2021.3085878

    Article  Google Scholar 

  • Clark, J. M., & Paivio, A. (1991). Dual coding theory and education. Educational Psychology Review, 3, 149–210. https://doi.org/10.1007/BF01320076

    Article  Google Scholar 

  • Clement, J. (1993). Using bridging analogies and anchoring intuitions to deal with students’ preconceptions in physics. Journal of Research in Science Teaching, 30(10), 1241–1257. https://doi.org/10.1002/tea.3660301007

    Article  Google Scholar 

  • Coeckelbergh, M. (2020). AI ethics. MIT Press.

    Book  Google Scholar 

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

    Google Scholar 

  • Creswell, J. W., & Poth, C. N. (2016). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). Sage Publications.

    Google Scholar 

  • Dang, J., & Liu, L. (2021). Robots are friends as well as foes: Ambivalent attitudes toward mindful and mindless AI robots in the United States and China. Computers in Human Behavior, 115, 106612. https://doi.org/10.1016/j.chb.2020.106612

    Article  Google Scholar 

  • Davis, B. G. (1997). Misconceptions as barriers to understanding science. In National Research Council (Eds.), Science teaching reconsidered: A handbook. (pp. 27–32). National Academies Press.

  • Dipaola, D., Payne, B. H., & Breazeal, C. (2022). Preparing children to be conscientious 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. 181–205). MIT Press.

    Chapter  Google Scholar 

  • Driver, R., & Easley, J. (1978). Pupils and paradigms: A review of literature related to concept development in adolescent science students. Studies in Science Education, 5(1), 61–84. https://doi.org/10.1080/03057267808559857

    Article  Google Scholar 

  • Druga, S., Vu, S. T., Likhith, E., & Qiu, T. (2019). Inclusive AI literacy for kids around the world. In P. Blikstein & N. Holbert (Eds.), Proceedings of FabLearn 2019 8th Annual Conference on Maker Education (pp. 104–111). The Association for Computing Machinery. https://doi.org/10.1145/3311890.3311904

  • Emmert-Streib, F., Yli-Harja, O., & Dehmer, M. (2020). Artificial intelligence: A clarification of misconceptions, myths, and desired status. Frontiers in Artificial Intelligence, 3, 524339. https://doi.org/10.3389/frai.2020.524339

    Article  Google Scholar 

  • Fisher, K. M. (1985). A misconception in biology: Amino acids and translation. Journal of Research in Science Teaching, 22(1), 53–62. https://doi.org/10.1002/tea.3660220105

    Article  MathSciNet  Google Scholar 

  • Gilbert, J., Osborne, R., & Fensham, P. (1982). Children’s science and its consequences for teaching. Science Education, 66, 623–633. https://eric.ed.gov/?id=EJ266160

  • Ginat, D., Menashe, E., & Taya, A. (2013). Novice difficulties with interleaved pattern composition. In I. Diethelm & R. T. Mittermeier (Eds.) Proceedings of International Conference on Informatics in Schools: Situation, Evolution, and Perspectives (pp. 57–67). ISSEP. https://doi.org/10.1007/978-3-642-36617-8

  • Glenberg, A., de Vega, M., & Graesser, A. C. (2008). Framing the debate. In M. de Vega, A. Glenberg, & A. C. Graesser (Eds.), Symbols and embodiment: Debates on meaning and cognition (pp. 1–10). Oxford University Press.

    Google Scholar 

  • Gonzalez, M. F., Liu, W., Shirase, L., Tomczak, D. L., Lobbe, C. E., Justenhoven, R., & Martin, N. R. (2022). Allying with AI? Reactions toward human-based, AI/ML-based, and augmented hiring processes. Computers in Human Behavior, 130, 107179. https://doi.org/10.1016/j.chb.2022.107179

    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 

  • Greiff, S., Molnár, G., Martin, R., Zimmermann, J., & Csapó, B. (2018). Students’ exploration strategies in computer-simulated complex problem environments: A latent class approach. Computers & Education, 126, 248–263. https://doi.org/10.1016/j.compedu.2018.07.013

    Article  Google Scholar 

  • Gunkel, D. J. (2012). The machine question: Critical perspectives on AI, robots, and ethics. MIT Press.

    Book  Google Scholar 

  • Gurel, D. K., Eryilmaz, A., & McDermott, L. C. (2015). A review and comparison of diagnostic instruments to identify students’ misconceptions in science. Eurasia Journal of Mathematics, Science and Technology Education, 11(5), 989–1008. https://doi.org/10.12973/eurasia.2015.1369a

    Article  Google Scholar 

  • Hagendorff, T. (2020). The ethics of AI ethics: An evaluation of guidelines. Minds and Machines, 30(1), 99–120. https://doi.org/10.1007/s11023-020-09517-8

    Article  Google Scholar 

  • Hake, R. R. (1992). Socratic pedagogy in the introductory physics laboratory. The Physics Teacher, 30, 546–552. https://doi.org/10.1119/1.2343637

    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 

  • Hashweh, M. (1988). Descriptive studies of students’ conceptions in science. Journal of Research in Science Teaching, 25(2), 121–134. https://doi.org/10.1002/tea.3660250204

    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 

  • Holmes, W., Bialik, M., & Fadel, C. (2020). Artificial Intelligence in Education. Center for curriculum redesign.

    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 

  • Impey, C., Buxner, S., & Antonellis, J. (2012). Non-scientific beliefs among undergraduate students. Astronomy Education Review, 11(1), 1–12. https://doi.org/10.3847/AER2012016

    Article  Google Scholar 

  • Kaczmarczyk, L. C., Petrick, E. R., East, J. P., & Herman, G. L. (2010). Identifying student misconceptions of programming. In G. Lewandowski, S. Wolfman, T. J. Cortina, & E. L. Walker, (Eds.), Proceedings of the 41st ACM technical symposium on Computer science education (pp. 107–111). Association of Computing Machinery. https://doi.org/10.1145/1734263.1734299

  • Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15–25. https://doi.org/10.1016/j.bushor.2018.08.004

    Article  Google Scholar 

  • Karpudewan, M., Zain, A. N. M., & Chandrasegaran, A. L. (Eds.). (2017). Overcoming students’ misconceptions in science. Springer.

    Google Scholar 

  • Kippers, W. B., Poortman, C. L., Schildkamp, K., & Visscher, A. J. (2018). Data literacy: What do educators learn and struggle with during a data use intervention? Studies in Educational Evaluation, 56, 21–31. https://doi.org/10.1016/j.stueduc.2017.11.001

    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 

  • Kong, S. C., Cheung, W. M. Y., & Tsang, O. (2022). Evaluating an artificial intelligence literacy programme for empowering and developing concepts, literacy and ethical awareness in senior secondary students. Education and Information Technologies, 1–22. https://doi.org/10.1007/s10639-022-11408-7

  • Kreinsen, M., & Schulz, S. (2021). Students' conceptions of artificial intelligence. In Berges. M., Mühling. A, & Armoni, M. (Eds.), Proceedings of the 16th Workshop in Primary and Secondary Computing Education (pp. 1–2). Association for Computing Machinery. https://doi.org/10.1145/3481312.3481328

  • Kwon, K. (2017). Novice programmer’s misconception of programming reflected on problem-solving plans. International Journal of Computer Science Education in Schools, 1(4), 1–12. https://doi.org/10.21585/ijcses.v1i4.19

    Article  Google Scholar 

  • Kwon, K., Jeon, M., Guo, M., Yan, G., Kim, J., Ottenbreit-Leftwich, A. T., & Brush, T. A. (2021). Computational thinking practices: Lessons learned from a problem-based curriculum in primary education. Journal of Research on Technology in Education, 1–18. https://doi.org/10.1080/15391523.2021.2014372

  • 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 

  • Lai, Y. H., Chen, S. Y., Lai, C. F., Chang, Y. C., & Su, Y. S. (2021). Study on enhancing AIoT computational thinking skills by plot image-based VR. Interactive Learning Environments, 29(3), 482–495. https://doi.org/10.1080/10494820.2019.1580750

    Article  Google Scholar 

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

    Google Scholar 

  • Leaper, C., Farkas, T., & Brown, C. S. (2012). Adolescent girls’ experiences and gender-related beliefs in relation to their motivation in Math/Science and English. Journal of Youth and Adolescence, 41, 268–282. https://doi.org/10.1007/s10964-011-9693-z

    Article  Google Scholar 

  • Lin, P., & Van Brummelen, J. (2021). Engaging teachers to co-design integrated AI curriculum for K-12 classrooms. In Y. Kitamura, A. J. Quigley, K. Isbister, T. Igarashi, P. Bjorn, & S. Drunker (Eds.), Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1–12). Association for Computing Machinery. https://doi.org/10.1145/3411764.3445377

  • Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Sage publications.

    Book  Google Scholar 

  • Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In R. Bernhaupt, F. F. Muller, D. Verweij & J. Andres (Eds.), Proceedings of the 2020 CHI conference on human factors in computing systems (pp. 1–16). Association for Computing Machinery. https://doi.org/10.1145/3313831.3376727

  • Mandinach, E. B., & Schildkamp, K. (2021). Misconceptions about data-based decision making in education: An exploration of the literature. Studies in Educational Evaluation, 69, 100842. https://doi.org/10.1016/j.stueduc.2020.100842

    Article  Google Scholar 

  • Marques, L. S., Gresse von Wangenheim, C., & Hauck, J. C. (2020). Teaching machine learning in school: A systematic mapping of the state of the art. Informatics in Education, 19(2), 283–321. https://doi.org/10.15388/infedu.2020.14

    Article  Google Scholar 

  • Marx, E., Leonhardt, T., Baberowski, D., & Bergner, N. (2022). Using matchboxes to teach the basics of machine learning: An analysis of (possible) misconceptions. In Proceedings of the Second Teaching Machine Learning and Artificial Intelligence Workshop (pp. 25–29). PMLR. https://proceedings.mlr.press/v170/marx22a

  • Maxwell, J. A. (2013). Qualitative research design: An interactive approach. Sage Publications.

    Google Scholar 

  • McCauley, R., Fitzgerald, S., Lewandowski, G., Murphy, L., Simon, B., Thomas, L., & Zander, C. (2008). Debugging: A review of the literature from an educational perspective. Computer Science Education, 18(2), 67–92. https://doi.org/10.1080/08993400802114581

    Article  Google Scholar 

  • McCloskey, M., Caramazza, A., & Green, B. (1980). Curvilinear motion in the absence of external forces: Naive beliefs about the motion of objects. Science, 210(4474), 1139–1141. https://doi.org/10.1126/science.210.4474.113

    Article  Google Scholar 

  • McDermott, L. C., & Shaffer, P. S. (1992). Research as a guide for curriculum development: An example from introductory electricity. Part I: Investigation of student understanding. American journal of physics, 60(11), 994–1003. https://doi.org/10.1119/1.17003

    Article  Google Scholar 

  • Mertala, P., Fagerlund, J., & Calderon, O. (2022). Finnish 5th and 6th-grade students' pre-instructional conceptions of artificial intelligence (AI) and their implications for AI literacy education. Computers and Education: Artificial Intelligence, 100095. https://doi.org/10.1016/j.caeai.2022.100095

  • Morrison, G. R., Ross, S. J., Morrison, J. R., & Kalman, H. K. (2019). Designing effective instruction. John Wiley & Sons.

    Google Scholar 

  • Nagarajan, A., Minces, V., Anu, V., Gopalasamy, V., & Bhavani, R. R. (2020). There's data all around you: Improving data literacy in high schools through STEAM-based activities. In Proceedings of Fablearn Asia 2020 (pp. 17–20). The Association for Computing Machinery. https://par.nsf.gov/biblio/10166600

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

    Book  Google Scholar 

  • National Research Council. (1997). Science teaching reconsidered: A handbook. National Academies Press. https://doi.org/10.17226/5287

    Book  Google Scholar 

  • National Research Council. (2004). How students learn: History, mathematics, and science in the classroom. National Academies Press. https://doi.org/10.17226/10126

    Book  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 

  • Ottenbreit-Leftwich, A., Glazewski, K., Jeon, M., Jantaraweragul, K., Hmelo-Silver, C. E., Scribner, A., Lee, S., Mott, B., & Lester, J. (2022). Lessons Learned for AI Education with Elementary Students and Teachers. International Journal of Artificial Intelligence in Education, 1–23. https://doi.org/10.1007/s40593-022-00304-3

  • Pedro, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development. https://unesdoc.unesco.org/ark:/48223/pf0000366994

  • Porter, L., Bailey Lee, C., & Simon, B. (2013). Halving fail rates using peer instruction: a study of four computer science courses. In Proceedings of the 44th ACM technical symposium on Computer science education (pp. 177–182). Association for Computing Machinery. https://doi.org/10.1145/2445196.2445250

  • Qian, Y., & Lehman, J. (2017). Students’ misconceptions and other difficulties in introductory programming: A literature review. ACM Transactions on Computing Education (TOCE), 18(1), 1–24. https://doi.org/10.1145/3077618

    Article  Google Scholar 

  • Raffaghelli, J. E., & Stewart, B. (2020). Centering complexity in ‘educators’ data literacy to support future practices in faculty development: A systematic review of the literature. Teaching in Higher Education, 25(4), 435–455. https://doi.org/10.1080/13562517.2019.1696301

    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). Association for Computing Machinery. https://doi.org/10.1145/3341525.3387364

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

    Google Scholar 

  • Samsudin, A., Afif, N. F., Nugraha, M. G., Suhandi, A., Fratiwi, N. J., Aminudin, A. H., Adimayuda, R., Linuwih, S., & Costu, B. (2021). Reconstructing students’ misconceptions on work and energy through the PDEODE* E tasks with think-pair-share. Journal of Turkish Science Education, 18(1), 118–144. https://doi.org/10.36681/tused.2021.56

    Article  Google Scholar 

  • Sander, I. (2020). What is critical big data literacy and how can it be implemented? Internet Policy Review, 9(2), 1–22. https://doi.org/10.14763/2020.2.1479

    Article  Google Scholar 

  • Sanusi, I. T., Oyelere, S. S., & Omidiora, J. O. (2022). Exploring teachers’ preconceptions of teaching machine learning in high school: A preliminary insight from Africa. Computers and Education Open, 3, 100072. https://doi.org/10.1016/j.caeo.2021.100072

    Article  Google Scholar 

  • Schwanenflugel, P. J. (1991). Why are abstract concepts hard to understand? In P. J. Schwanenflugel (Ed.), The psychology of word meanings (pp. 235–262). Psychology Press.

    Google Scholar 

  • Simon, B., Kohanfars, M., Lee, J., Tamayo, K., & Cutts, Q. (2010). Experience report: peer instruction in introductory computing. In Proceedings of the 41st ACM technical symposium on Computer science education (pp. 341–345). Association of Computing and Machinery. https://doi.org/10.1145/1734263.1734381

  • Smith, J. P., III., DiSessa, A. A., & Roschelle, J. (1994). Misconceptions reconceived: A constructivist analysis of knowledge in transition. The Journal of the Learning Sciences, 3(2), 115–163. https://doi.org/10.1207/s15327809jls0302_1

    Article  Google Scholar 

  • Soeharto, S., Csapó, B., Sarimanah, E., Dewi, F. I., & Sabri, T. (2019). A review of students’ common misconceptions in science and their diagnostic assessment tools. Jurnal Pendidikan IPA Indonesia, 8(2), 247–266. https://doi.org/10.15294/jpii.v8i2.18649

    Article  Google Scholar 

  • Szczuka, J. M., Strathmann, C., Szymczyk, N., Mavrina, L., & Kramer, N. C. (2022). How do children acquire knowledge about voice assistants? A longitudinal field study on children’s knowledge about how voice assistants store and process data. International Journal of Child-Computer Interaction, 33, 100460. https://doi.org/10.1016/j.ijcci.2022.100460

    Article  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).

  • Taylor, A. K., & Kowalski, P. (2014). Student misconceptions: Where do they come from and what can we do? In V. A. Benassi, C. E. Overson, & C. M. Hakala (Eds.), Applying the science of learning in education: Infusing psychological science into the curriculum (pp. 259–273). Society for the Teaching of Psychology.

    Google Scholar 

  • Teague, D., & Lister, R. (2014). Programming: reading, writing, and reversing. In Proceedings of the 2014 conference on Innovation & technology in computer science education (pp. 285–290). Association for Computing Machinery. https://doi.org/10.1145/2591708.2591712

  • 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 

  • Touretzky, D., & Gardner-McCune, C. (2022). Artificial Intelligence Thinking in K-12. In S. C. Kong & H. Abelson (Eds.), (2022) 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(1), 9795–9799. https://doi.org/10.1609/aaai.v33i01.33019795

    Article  Google Scholar 

  • Treagust, D. F. (1988). Development and use of diagnostic tests to evaluate students’ misconceptions in science. International Journal of Science Education, 10(2), 159–169. https://doi.org/10.1080/0950069880100204

    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. Proceedings of the TEI ’16: Tenth International Conference on Tangible, Embedded, and Embodied Interaction, Eindhoven, Netherlands. https://doi.org/10.1145/2839462.2839491

  • Williams, R., Kaputsos, S. P., & Breazeal, C. (2021). Teacher perspectives on how to train your robot: A middle school AI and ethics curriculum. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17). 15678–15686. https://ojs.aaai.org/index.php/AAAI/article/view/17847

  • Williams, M., & Moser, T. (2019). The art of coding and thematic exploration in qualitative research. International Management Review, 15(1), 45–55.

    Google Scholar 

  • 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 

  • 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  MathSciNet  Google Scholar 

  • Yau, K. W., Chai, C. S., Chiu, T. K., Meng, H., King, I., & Yam, Y. (2022). A phenomenographic approach on teacher conceptions of teaching Artificial Intelligence (AI) in K-12 schools. Education and Information Technologies, 1–24. https://doi.org/10.1007/s10639-022-11161-x

  • Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). A systematic review of research on artificial intelligence applications in higher education–where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1–27. https://doi.org/10.1186/s41239-019-0171-0

    Article  Google Scholar 

  • Zhang, H., Lee, I., Ali, S., DiPaola, D., Cheng, Y., & Breazeal, C. (2022). Integrating ethics and career futures with technical learning to promote AI literacy for middle school students: An exploratory study. International Journal of Artificial Intelligence in Education, 1–35. https://doi.org/10.1007/s40593-022-00293-3

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Funding

This study was funded by U.S. DEPARTMENT OF DEFENSE (NATIONAL CENTER FOR THE ADVANCEMENT OF STEM EDUCATION), and the award number is 076967-00003C.

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Authors and Affiliations

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

    Keunjae Kim, Kyungbin Kwon, Anne Ottenbreit-Leftwich, Haesol Bae & Krista Glazewski

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  1. Keunjae Kim
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  2. Kyungbin Kwon
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  3. Anne Ottenbreit-Leftwich
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  4. Haesol Bae
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  5. Krista Glazewski
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Correspondence to Keunjae Kim.

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Kim, K., Kwon, K., Ottenbreit-Leftwich, A. et al. Exploring middle school students’ common naive conceptions of Artificial Intelligence concepts, and the evolution of these ideas. Educ Inf Technol 28, 9827–9854 (2023). https://doi.org/10.1007/s10639-023-11600-3

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  • DOI: https://doi.org/10.1007/s10639-023-11600-3

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