Exploring Elementary School Students' Image of Artificial Intelligence

인공지능에 대한 초등학생들의 이미지 탐색

  • Received : 2018.03.08
  • Accepted : 2018.05.07
  • Published : 2018.05.31


The current study explores students' views about artificial intelligence (AI) through analyses of their drawings and perceptions. The data were gathered from a total of 177 elementary school students. The constant comparative analysis was used as the data analysis method. Based on the result, the current study found that students' views about artificial intelligence were constructed into two distinct dimensions: form and relationship. The form dimension, students' views about artificial intelligence were categorized into human, household goods, machine, smart computer, electronic chip/algorithm, or the hybridized form related to the game of go such as AlphaGo. On the relationship dimension, students' views about artificial intelligence were categorized into servants, friends or enemy. Given the combination of two dimensions, the current study found two noted patterns. The first, students who viewed artificial intelligence as human form perceived artificial intelligence as a friend or an enemy. However, those who viewed artificial intelligence as non-human form perceived artificial intelligence as a servant or an enemy. Based on these results, educational implications related to the preparation of artificial intelligence era for elementary science education are discussed.


  1. Ahn, S. J. (2017). Artificial intelligence and criminal liability. Korean Journal of Legal Philosophy, 20(2), 77-122.
  2. Berg, P., Baltimore, D., Brenner, S., Roblin, R. O. & Singer, M. F. (1975). Summary statement of the Asilomar conference on recombinant DNA molecules. Proceeding of the National Academy of Sciences of the United States of America, 72(6), 1981-1984.
  3. Bernard, P. & Dudek, K. (2017). Revisiting students’ perceptions of research scientists-outcomes of an indirect draw a scientist test (INDAST). Journal of Baltic Science Education, 16(4), 562-575.
  4. Borland, J. & Coelli, M. (2017). Are robots taking our jobs? The Australian Economic Review, 50(4), 377-397.
  5. Borody, W. A. (2013). The Japanese roboticist Masahiro Mori's Buddhist inspired concept of "The uncanny valley" (Bukimi no Tani Genshō, 不気味の谷現象). Journal of Evolution & Technology, 23(1), 31-44.
  6. Boström, N. (2014). Superintelligence: Paths, dangers, strategies. New York: Oxford University Press.
  7. Bundesministerium fur Bildung und Forschung (2014). Perspektive MINT-Berufe: Forderung von Technik und Naturwissenschaft. Retrieved October 24, 2014, from
  8. Burk, C. L., Ground, C., Martin, J. & Wiese, B. (2016). Karrieren von Ingenieur-und Naturwissenschaftlern in Wissenschaft und Privatwirtschaft: Attraktoren und Durchlässigkeit aus psychologischer und personalokonomischer Perspektive. Beitrage zur Hochschulforschung, 38, 118-141.
  9. Chambers, D. W. (1983). Stereotypic images of the scientist: The draw-a-scientist test. Science Education, 67 (2), 255-265.
  10. Cheon, H. S. (2017). Urform des Posthumanen und literarische Imagination-Frankensteins Geschopf vs. Homunkulus. Zeitschrift fur Deutsche Sprache und Literatur, 78, 209-235.
  11. Choi, Y. S. (2016). Artificial Intelligence: Will it replace human medical doctors? Korean Medical Education Review, 18(2), 47-50.
  12. Dennett, D. (1997). Did HAL commit murder? In D. G. Stork, Ed. HAL's Legacy: 2001's Computer as dream and reality. Cambridge, MA: The MIT Press.
  13. Edwards, A., Edwards, C., Spence, P. R., Harris, C. & Gambino, A. (2016). Robots in the classroom: Differences in students' perceptions of credibility and learning between "teacher as robot" and "robot as teacher." Computers in Human Behavior, 65, 627-634.
  14. Ezzy, D. (2002). Qualitative analysis: Practice and innovation. London: Routledge.
  15. Fauconnier, G. (1997). Mapping in thought and language. Cambridge: Cambridge University Press.
  16. Fauconnier, G. & Turner, M. (1998). Conceptual integration networks. Cognitive Science, 22, 133-187.
  17. Fauconnier, G. & Turner, M. (2002). The way we think: Conceptual blending and the mind's hidden complexities. New York: Basic Books.
  18. Fenwick, T. J. & Edwards, R. (2010). Actor-network theory in education. New York: Routledge.
  19. Fernandez-Llamas, C., Conde, M. A., Rodriguez-Lera, F. J., Rodriguez-Sedano, F. J. & Garcia, F. (2018). May I teach you? Students' behavior when lectured by robotic vs. human teachers. Computers in Human Behavior, 80, 460-469.
  20. Fox, S. (2017). Beyond AI: Multi-Intelligence (MI) combining natural and artificial intelligences in hybrid beings and systems. Technologies, 5(38), 1-14.
  21. Glaser, B. G. & Strauss, A. L. (1967). The discovery of grounded theory: Strategies for qualitative research. Chicago: Aldine.
  22. Hyun, E. J., Park, H. K., Yeon, H. M. & Jang, J. Y. (2010). Young children’s emotion and role recognition of teacher assistive robot in a kindergarten. Journal of Early Childhood Education, 30(4), 171-186.
  23. Hyun, E. J., Yoon, H. M. & Kang, J. M. (2010). Relationships between young children's perceptions of and experience with education robot. Korean Journal of Children's Media, 9(1), 189-205.
  24. Jeong, Y., Kim, K., Jeong, I., Kim, H., Kim, C., Yu, J., Kim, C. & Hong, M. (2015). A development of the software education curriculum model for elementary students. Journal of the Korean Association of Information Education, 19(4), 467-480.
  25. Kim, C. (2016). A study of robot curriculum to consider conceptual understanding and learning activities for elementary school. Journal of the Korean Association of Information Education, 20(6), 645-654.
  26. Kim, D. & Kim, B. (2016). How AlphaGo does change people's perception of introduction of artificial intelligence into intellectual work? Journal of Cybercommunication Academic Society, 33(4), 107-158.
  27. Kim, H. (2016). Artificial intelligence and human intelligence-With emphasis on intelligence-concept in MacCarthy and Kant. Philosophical Investigation, 43, 161-190.
  28. Kim, J. (2017). Between homo faber and homo ethicus: With a focus on people’s fear of AlphaGo. Philosophical Investigation, 43, 161-190.
  29. Kim, J. H. (2014). How can we become posthuman subject? Philosophical Studies, 106, 215-242.
  30. Kim, Y. S. (2013). 'TransHuman', eine kulturwissenschaftliche Untersuchung uber hybrides Wesen von Maschinen-Mensch. The Journal of Humanities, 35, 279-298.
  31. Koodi2016 (2014). Koodi2016-ensiapua ohjelmoinnin opettamiseen peruskoulussa. Retrieved from
  32. Kwon, J. H. (2017). Eine Fallanalyse der Weiterbildung bei deutschen Unternehmen in Industrie 4.0 und die sich daraus ergebenden Implikationen für Koreanische Unternehmen. Koreanische Zeitschrift für Wirtschaftswissenschaften, 35(4), 1-19.
  33. Latour, B. (2005). Reassembling the social: An introduction to actor-network-theory. Oxford, UK: Oxford University Press.
  34. Lee, J. K., Lee, T. K., Shin, S., Chung, D. H. & Oh, S. W. (2013). Exploring the image types of secondary school students’ perception about the talented person in convergence. Journal of the Korean Association for Research in Science Education, 33(7), 1486-1509.
  35. Lee, S. (2008). The limits of artificial intelligence and the possibility of generalized intelligence: A posthuman context. The Korean Journal for the Philosophy of Science, 12, 49-69.
  36. Lincoln, Y. S. & Guba, E. G. (1989). Naturalistic inquiry. Beverley Hills, CA: Sage Publication.
  37. Mavridis, N., Katsaiti, M., Naef, S., Falasi, A., Nuaimi, A., Afifi, H. & Kitbi, A. (2012). Opinions and attitudes toward humanoid robots in the Middle East. AI & Society, 27, 517-534.
  38. Merriam, S. B. (2009). Qualitative research: A guide to design and implementation. Jossey-Bass Publishers.
  39. Mori, M. (1970). Bukimi no tani the uncanny valley. Energy, 7(4), 33-35.
  40. Mori, M. (2012). The uncanny valley, trans. by Karl F. MacDorman and Norri Kageki under authorization by Masahiro Mori. IEEE Robotics & Automation Magazine, p. 98-100.
  41. Park, J. H. & Shin, N. M. (2017). Students’ perceptions of artificial intelligence technology and artificial intelligence teachers. The Journal of Korean Teacher Education, 34(2), 169-192.
  42. Park, Y. H. (2012). Conceptual blending in imaginary expression. Korean Journal of Elementary Education, 23(1), 193-212.
  43. Rhee, C. (2010). Review of brain-machine interface technology. New Physics: Sae Mulli, 60(1), 1-22.
  44. Ryu, M. & Han, S. (2017). Image of artificial intelligence of elementary students by using semantic differential scale. Journal of the Korean Association of Information Education, 21(5), 527-535.
  45. Schwab, K. (2016). The fourth industrial revolution. Colony/Geneva: World Economic Forum.
  46. Shin, N. M. & Kim, S. A. (2007). What do robots have to do with students learning? The Journal of Educational Information and Media, 13(3), 79-99.
  47. Shin, N. & Kim, S. (2007). What do robot have to do with student learning? The Journal of Educational Information and Media, 13(3), 79-99.
  48. Shin, S. & Bae, Y. (2015). Review of software education based on the coding in Finland. Journal of the Korean Association of Information Education, 19(1), 127-138.
  49. Shin, S., Ha, M. & Lee, J. K. (2017). High school students’ perception of artificial intelligence: Focusing on conceptual understanding, emotion and risk perception. Journal of Learner-Centered Curriculum and Instruction, 17(21), 289-312.
  50. Shoham, Y., Perrault, R., Brynjolfsson, E. & Clark, J. (2017). Artificial intelligence index: 2017 Annual report. Retrieved from
  51. Song, S. C. & Shim, K. C. (2017). A study on the awareness of pre-service science teachers about secondary education in future intelligence information society. Biology Education, 45(3), 404-417.
  52. Szollosy, M. (2017). Freud, Frankenstein and our fear of robots: Projection in our cultural perception of technology. AI & Society, 32, 433-439.
  53. Thomas, J. A., Pedersen, J. E. & Finson, K. (2001). Validating the Draw-A-Science-Teacher-Test Checklist (DASTT-C): Exploring mental models and teacher beliefs. Journal of Science Teacher Education, 12(3), 295-310.
  54. Türkmen, H. (2008). Turkish primary students' perceptions about scientist and what factors affecting the image of the scientists. Eurasia Journal of Mathematics, Science & Technology Education, 4(1), 55-61.
  55. Wang, S., Lilienfeld, S. O. & Rochat, P. (2015). The uncanny valley: Existence and explanations. Review of General Psychology, 19(4), 393-407.
  56. Yoo, E. Y. & Cho, H. S. (2012). An analysis of preservice early childhood educators perceptions of scientists. Early Childhood Education Research & Review, 16(2), 399-420.
  57. Yoo, H. H., Lee, J. K. & Kim, A. (2015). Perceptual comparison of the 'good doctor' image between faculty and students in medical school. Korean Journal of Medical Education, 27(4), 257-266.