The Mathematical Education (한국수학교육학회지시리즈A:수학교육)

Korean Society of Mathematical Education (한국수학교육학회)
권호 표지
DOI QR코드

DOI QR Code

A study on the factors of elementary school teachers' intentions to use AI math learning system: Focusing on the case of TocToc-Math

초등교사들의 인공지능 활용 수학수업 지원시스템 사용 의도에 영향을 미치는 요인 연구: <똑똑! 수학탐험대> 사례를 중심으로

  • Kyeong-Hwa Lee (Seoul National University) ;
  • Sheunghyun Ye (Daegu National University of Education) ;
  • Byungjoo Tak (Jeonju National University of Education) ;
  • Jong Hyeon Choi (Gyeongin National University of Education) ;
  • Taekwon Son (Bongmyong Elementary School) ;
  • Jihyun Ock (Korea Education and Research Information Service)
  • 이경화 (서울대학교) ;
  • 여승현 (대구교육대학교) ;
  • 탁병주 (전주교육대학교) ;
  • 최종현 (경인교육대학교) ;
  • 손태권 (봉명초등학교) ;
  • 옥지현 (한국교육학술정보원)
  • Received : 2024.03.11
  • Accepted : 2024.05.01
  • Published : 2024.05.31
Download PDF
⟨ Previous Next ⟩

Abstract

This study explored the factors that influence elementary school teachers' intention to use an artificial intelligence (AI) math learning system and analyzed the interactions and relationships among these factors. Based on the technology acceptance model, perceived usefulness for math learning, perceived ease of use of AI, and attitude toward using AI were analyzed as the main variables. Data collected from a survey of 215 elementary school teachers was used to analyze the relationships between the variables using structural equation modeling. The results of the study showed that perceived usefulness for math learning and perceived ease of use of AI significantly influenced teachers' positive attitudes toward AI math learning systems, and positive attitudes significantly influenced their intention to use AI. These results suggest that it is important to positively change teachers' perceptions of the effectiveness of using AI technology in mathematics instruction and their attitudes toward AI technology in order to effectively adopt and utilize AI-based mathematics education tools in the future.

인공지능 활용 수학수업 지원시스템에 대한 교사의 사용 의도는, 인공지능을 활용하지 않은 전통적인 수학수업 환경에서 구현하기 어려웠던 다양한 수학학습 기회를 제공하는 데 핵심적인 역할을 한다. 본 연구는 초등교사의 인공지능 활용 수학수업 지원시스템 사용 의도에 영향을 미치는 요인을 탐색하고, 요인 간의 구조적 관계를 분석하는 데 목표를 두었다. 이를 위해 기술 수용 모델을 적용하여 인공지능 활용 수학수업 지원시스템의 하나인 <똑똑! 수학탐험대>에 대한 초등교사 215명의 태도와 사용 의도에의 영향 요인 간 관계를 분석하였다. 주요 변수는 수학 학습에 대한 지각된 유용성, 지각된 인공지능 사용 용이성, 그리고 인공지능 활용에 대한 태도였다. 연구 결과, 수학 학습에 대한 지각된 유용성과 지각된 인공지능 사용 용이성이 교사들의 <똑똑! 수학탐험대>에 대한 긍정적인 태도에 영향을 미치고, 긍정적인 태도가 <똑똑! 수학탐험대> 사용 의도에 유의미한 영향을 미치는 것으로 나타났다. 이러한 결과는 교사가 인공지능을 활용한 수학학습의 효과와 인공지능 사용 용이성에 대해 긍정적으로 인식하도록 돕는 것이 인공지능 활용 수학수업 지원시스템을 현장에 효과적으로 도입하여 수학수업과 수학학습을 지원하는 데 핵심임을 시사한다.

Keywords

Acknowledgement

This work was supported by the Korea Education and Research Information Service.

References

  1. Ball, L., Drijvers, P., Ladel, S., Siller, H. S., Tabach, M., & Vale, C. (2018). Uses of technology in primary and secondary mathematics education: Tools, topics and trends. Springer. https://doi.org/10.1007/978-3-319-76575-4
  2. Bray, A., & Tangney, B. (2017). Technology usage in mathematics education research-A systematic review of recent trends. Computers & Education, 114, 255-273. https://doi.org/10.1016/j.compedu.2017.07.004
  3. Chang, H., & Nam, J. (2021). The Use of Artificial Intelligence in Elementary Mathematics Education: Focusing on the math class support system "Knock-knock! Math Expedition". The Journal of Korea Elementary Education, 31, 105-123. https://doi.org/10.20972/kjee.31..202101.105
  4. Chocarro, R., Cortinas, M., & Marcos-Matas, G. (2023). Teachers' attitudes towards chatbots in education: a technology acceptance model approach considering the effect of social language, bot proactiveness, and users' characteristics. Educational Studies, 49(2), 295-313. https://doi.org/10.1080/03055698.2020.1850426
  5. Choi, S. Y. (2022). Development of an instructional design model for elementary mathematics classes based on an artificial intelligence education system [Master's thesis, Seoul National University].
  6. Choi, S., Jang, Y., & Kim, H. (2023). Influence of pedagogical beliefs and perceived trust on teachers' acceptance of educational artificial intelligence tools. International Journal of Human Computer Interaction, 39(4), 910-922. https://doi.org/10.1080/10447318.2022.2049145
  7. Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results [Doctoral dissertation, MIT Sloan School of Management].
  8. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003. https://doi.org/10.1287/mnsc.35.8.982
  9. Fornell, C., & Larcker, D. F. (1981). Evaluating Structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.2307/3151312
  10. Gefen, D., Rigdon, E. E., & Straub, D. (2011). Editor's comments: An update and extension to SEM guidelines for administrative and social science research. MIS Quarterly, 35(2), iii-xiv. https://doi.org/10.2307/23044042
  11. Gefen, D., Straub, D., & Boudreau, M. C. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4(7), 1-79. https://doi.org/10.17705/1CAIS.00407
  12. Granic, A., & Marangunic, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572-2593. https://doi.org/10.1111/bjet.12864
  13. Gurer, M. D., & Akkaya, R. (2022). The influence of pedagogical beliefs on technology acceptance: a structural equation modeling study of pre-service mathematics teachers. Journal of Mathematics Teacher Education, 25(4), 479-495. https://doi.org/10.1007/s10857-021-09504-5
  14. Higgins, K., Huscroft-D'Angelo, J., & Crawford, L. (2019). Effects of technology in mathematics on achievement, motivation, and attitude: A meta-analysis. Journal of Educational Computing Research, 57(2), 283-319. https://doi.org/10.1177/0735633117748416
  15. Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education. Center for Curriculum Redesign. https://doi.org/10.58863/20.500.12424/4276068
  16. Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
  17. Ibili, E., Resnyansky, D., & Billinghurst, M. (2019). Applying the technology acceptance model to understand maths teachers' perceptions towards an augmented reality tutoring system. Education and Information Technologies, 24, 2653-2675. https://doi.org/10.1007/s10639-019-09925-z
  18. Jeong, Y., Tak, B., Lim, M., & Kim, K. (2022). A study on advancement of elementary mathematics supporting system using artificial intelligence in teaching and learning. Ministry of Education.
  19. Joubert, J., Callaghan, R., & Engelbrecht, J. (2020). Lesson study in a blended approach to support isolated teachers in teaching with technology. ZDM Mathematics Education 52, 907-925. https://doi.org/10.1007/s11858-020-01161-x
  20. Kim, H. (2017). Theoretical perspectives on the age of artificial intelligence. Society and Theory, 31, 41-62. https://doi.org/10.17209/st.2017.11.31.41
  21. Kim, H., Gye, B., Lee, J., Lim, W., & Choi, I. (2018). Transforming mathematics education through information technology. Korea Foundation for the Advancement of Science & Creativity.
  22. Kim, J. W., Kwon, M., & Pang, J. S. (2023). Elementary school teachers' perceptions of using artificial intelligence in mathematics education. Education of Primary School Mathematics, 26(4), 299-316. https://doi.org/10.7468/JKSMEC.2023.26.4.299
  23. Kim, S., & Cho, M. (2022). AI-based educational platform analysis supporting personalized mathematics learning. Communications of Mathematical Education, 36(3), 417-438. http://doi.org/10.7468/jksmee.2022.36.3.417
  24. Kline, R. B. (2015). Principles and practice of structural equation modeling. Guilford publications. 
  25. Ko, E., & Han, K. (2023). The effect of the elementary school 'Smart Mathematics Exploration Team' support system on mathematical academic achievement and mathematical attitude. Journal of The Korean Association of Information Education, 27(3),235-243. http://dx.doi.org/10.14352/jkaie.2023.27.3.235
  26. Lee, K., Kim, D., Kim, S., Kim, H., Kim, H., Park, J., Lee, H., Lee, H., Lim, H., Jang, J., Jung, J., Jo, S., Choi, I., & Song, C. (2021). A study on how to organize future-oriented math curriculum in preparation for post-COVID-19. Ministry of Education.
  27. Lim, M., Kim, H., Nam, J., & Hong, O. (2021). Exploring the application of elementary mathematics supporting system suing artificial intelligence in teaching and learning. School Mathematics, 23(2), 251-270. https://doi.org/10.29275/sm.2021.06.23.2.251
  28. Mailizar, M., Burg, D., & Maulina, S. (2021). Examining university students' behavioural intention to use e-learning during the COVID-19 pandemic: An extended TAM model. Education and Information Technologies, 26(6), 7057-7077. https://doi.org/10.1007/s10639-021-10557-5
  29. Mardia, K. V. (1970). Measures of multivariate skewness and kurtosis with applications. Biometrika, 57(3), 519-530.
  30. Ministry of Education (2022). Mathematics Curriculum. 2022-33(Book 8).
  31. Moden, M., Tallvid, M., Lundin, J., & Lindstrom, B. (2021). Intelligent tutoring system: Why teachers abandoned a technology aimed at automating teaching processes. In B. Tung (Ed.), Proceedings of the 54th Hawaii international conference on system sciences (pp. 1538-1547). HICSS Conference Office. https://doi.org/10.24251/hicss.2021.186
  32. Montebello, M. (2018). AI injected e-learning: The future of online education. Springer. https://doi.org/10.1007/978-3-319-67928-0
  33. Mueller, J., Wood, E., Willoughby, T., Ross, C., & Specht, J. (2008). Identifying discriminating variables between teachers who fully integrate computers and teachers with limited integration. Computers & Education, 51(4), 1523-1537. https://doi.org/10.1016/j.compedu.2008.02.003
  34. Onal, N. (2017). Use of interactive whiteboard in the mathematics classroom: Students' perceptions within the framework of the technology acceptance model. International Journal of Instruction, 10(4), 67-86. https://doi.org/10.12973/iji.2017.1045a
  35. Park, M. (2020). Applications and possibilities of artificial intelligence in mathematics education. Communications of Mathematical Education, 34(4), 545-561. https://doi.org/10.7468/jksmee.2020.34.4.545
  36. Perienen, A. (2020). Frameworks for ICT integration in mathematics education-A teacher's perspective. Eurasia Journal of Mathematics, Science and Technology Education, 16(6), em1845. https://doi.org/10.29333/ejmste/7803
  37. Peters, M. A., Jackson, L., Papastephanou, M., Jandric, P., Lazaroiu, G., Evers, C. W., Cope, B., Kalantzis, M., Araya, D., Tesar, M., Mika, C., Chen, L., Wang, C., Sturm, S., Rider, S., & Fuller, S. (2023). AI and the future of humanity: ChatGPT-4, philosophy and education - Critical responses, Educational Philosophy and Theory, 1-35. https://doi.org/10.1080/00131857.2023.2213437
  38. Phillips, A., Pane, J. F., Reumann-Moore, R., & Shenbanjo, O. (2020). Implementing an adaptive intelligent tutoring system as an instructional supplement. Educational Technology Research and Development, 68(3), 1409-1437. https://doi.org/10.1007/s11423-020-09745-w
  39. Raykov, T., & Marcoulides, G. A. (2008). An introduction to applied multivariate analysis. Routledge.
  40. Rosseel, Y. (2012). Lavaan: An R package for structural equation. Journal of Statistical Software, 48(2), 1-36. https://doi.org/10.18637/jss.v048.i02
  41. Schumacker, R. E., & Lomax, R. G. (2004). A beginner's guide to structural equation modeling (2nd ed.). Lawrence Erlbaum Associates. https://doi.org/10.4324/9781410610904
  42. Seufert, S., Guggemos, J., & Sailer, M. (2021). Technology-related knowledge, skills, and attitudes of pre-and in-service teachers: The current situation and emerging trends. Computers in Human Behavior, 115, 106552. https://doi.org/10.1016/j.chb.2020.106552
  43. Shin, D. (2020). Artificial intelligence in primary and secondary education: A systematic review. Journal of Educational Research in Mathematics, 30(3), 531-552. https://doi.org/10.29275/jerm.2020.08.30.3.531
  44. Shin, D. (2021). Teaching mathematics integrating intelligent tutoring system: Investigating prospective teachers' concerns and TPACK. International Journal of Science and Mathematics Education, 20(8), 1659-1676. https://doi.org/10.1007/s10763-021-10221-x
  45. Shin, D. (2022). Mathematics teachers' professional development through artificial intelligence. Journal for Philosophy of Mathematics Education, 4(1), 33-50. http://dx.doi.org/10.23027/JPME.2022.4.1.3
  46. Son, T. (2023). Preservice teacher's understanding of the intention to use the artificial intelligence program 'Knock-Knock! Mathematics Expedition' in mathematics lesson: Focusing on self-efficacy, artificial intelligence anxiety, and technology acceptance model. The Mathematical Education, 62(3), 401-416. https://doi.org/10.7468/mathedu.2023.62.3.401
  47. Taylor, S., & Todd, P. (1995). Assessing IT usage: The role of prior experience. MIS Quarterly, 19(4), 561-570. https://doi.org/10.2307/249633
  48. Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education, 52(2), 302-312. https://doi.org/10.1016/j.compedu.2008.08.006
  49. Teo, T., & Milutinovic, V. (2015). Modelling the intention to use technology for teaching mathematics among pre-service teachers in Serbia. Australasian Journal of Educational Technology, 31(4), 363-380. https://doi.org/10.14742/ajet.1668
  50. Teo, T., Ursavas, O. F., & Bahcekapili, E. (2012). An assessment of pre-service teachers' technology acceptance in Turkey: A structural equation modeling approach. Asia-Pacific Education Researcher, 21(1), 191-202.
  51. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
  52. Yeo, S., Rutherford, T., & Campbell, T. (2022). Understanding elementary mathematics teachers' intention to use a digital game through the technology acceptance model. Education and Information Technologies, 27(8), 11515-11536. https://doi.org/10.1007/s10639-022-11073-w
  53. Yim, Y., Ahn, S., Kim, K. M., Kim, J. H., & Hong, O. (2021). The effects of an AI-based class support system on student learning: focusing on the case of toctoc math expedition in Korea. The Journal of Korea Elementary Education, 32(4), 61-73. http://dx.doi.org/10.20972/kjee.32.4.202112.61
  54. Yoo, Y. K., & Chung, J. Y. (2024). An analysis on secondary mathematics teachers' perceptions of ai-convergence education. The Journal of Learner-Centered Curriculum and Instruction, 24(2), 139-156. https://doi.org/10.22251/jlcci.2024.24.2.139
  55. Young, J. R. (2016). Unpacking TPACK in mathematics education research: A systematic review of meta-analyses. International Journal of Educational Methodology, 2(1), 19-29. https://doi.org/10.12973/ijem.2.1.19
  56. Zawacki-Richter, O., Marin, V. I., Bond, M., & Gouverneur, F. (2019). 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