DOI QR코드

DOI QR Code

Analysis of Chemistry Teachers' Perceptions of AI Utilization in Education: Focusing on Participants in First-Grade Teacher Qualification Level Training

AI 활용 교육에 대한 화학 교사의 인식 분석 -1급 정교사 자격 연수 참여자를 중심으로-

  • Sungki Kim (Korea Institute for Curriculum and Evaluation)
  • 김성기 (한국교육과정평가원)
  • Received : 2024.08.27
  • Accepted : 2024.10.07
  • Published : 2024.10.31

Abstract

This study investigated the perceptions of chemistry teachers regarding the use of AI in education, focusing on their stages of concern, expected effects, and factors impeding implementation. Data were collected through a survey of 79 chemistry teachers who participated in first-grade teacher qualification training in 2024. The stages of concern were analyzed both overall and individually, and differences in stages of concern based on background variables were examined using the Kruskal-Wallis H test. The expected effects were measured across seven aspects, with differences were analyzed using repeated measures ANOVA and the Bonferroni method. Factors impeding implementation were analyzed through keyword analysis, focusing on internal and external factors. The results showed that overall concern was relatively low, with informational concern (Stage 1) and unconcerned (Stage 0) being high at 35.4% and 34.2%, respectively. Among active teachers, significant differences in stages of concern were observed depending on whether they had training experience (p<.05). The expected effects of AI in education showed significant statistical differences across the seven aspects (p<.05). Teachers rated 'providing diverse learning experiences' as the highest effect, while 'enhancing understanding of scientific concepts', 'improving scientific inquiry skills', and 'cultivating scientific literacy' were rated relatively low (p<.05). Internal factors were found to impede implementation more than external factors, with key internal factors including 'resistance to change', 'lack of capability', and 'teachers' negative perceptions of AI in education'. Based on these findings, recommendations were made to enhance the implementation of AI in educational settings.

연구는 화학 교사를 대상으로 AI 활용 교육에 대한 인식을 관심도, 효과 기대, 실행을 방해하는 요인을 중심으로 알아보았다. 2024년 화학 1급 정교사 자격 연수에 참여한 79명의 화학 교사를 대상으로 설문 조사를 통해 데이터를 수집하였다. 관심도는 전반적인 관심도와 개별적 관심도를 분석하고, 배경 변인에 따른 관심도 차이를 Kruskal-Wallis H 검정으로 분석하였다. 효과 기대는 7가지 항목에서 측정하고, 이에 따른 차이를 반복측정 ANOVA와 Bonferroni 방법으로 분석하였다. 실행을 방해하는 요인은 내적 요인과 외적 요인을 중심으로 키워드 분석하였다. 연구 결과, 전반적인 관심도는 상대적으로 낮았으며, 특히 정보적 관심(1단계)과 무관심(0단계)이 각각 35.4%와 34.2%로 높게 나타났다. 현직 교사일 때 관련 연수 경험 유무에 따라 관심도에서 유의미한 차이를 보였다(p<.05). AI 활용 교육에 대한 효과 기대에서 7가지 항목의 효과 기대가 통계적으로 유의미한 차이가 나타났다(p<.05). 교사들은 '다양한 학습 경험 제공'을 가장 높은 효과로 평가했으며, 반면에 '과학 개념 이해 증진', '과학적 탐구 능력 증진', '과학적 소양 함양'은 상대적으로 낮게 평가하였다(p<.05). 실행을 방해하는 요인으로는 내적 요인의 비율이 외적 요인에 비해 높았으며, 내적 요인에 해당하는 키워드로 '변화에 대한 부담', '역량 부족', 'AI 활용 교육에 대한 교사의 부정적 인식'이 있었다. 이 결과를 바탕으로 AI 활용 교육의 실행을 위한 방안을 제안하였다.

Keywords

References

  1. Chang, J. H., Kim, S. W., & Lee, S. B. (2015). Analysis on stages of concern and levels of use for achievement standards-based assessment in specialized high schools. The Journal of Curriculum and Evaluation, 18(2), 105-129.
  2. Choi, W. (2022). A theoretical inquiry on the meaning of 'digital literacy' in the 2022 revised English curriculum. Journal of the Korea English Education Society, 21(4), 115-132.
  3. George, A. A., Hall, G. E., & Stiegelbauer, S. M. (2006). Measuring implementation in schools: The stages of concern questionnaire. Austin, TX: Southwest Educational Development Laboratory.
  4. Hall, G. E., & Hord, S. M. (2006). Implementing change: Patterns, principles, and potholes. New Jersey: Pearson Education.
  5. Han, H. J., Kim, K. J., & Kwon, H. (2020). The analysis of elementary school teachers' perception of using artificial intelligence in education. Journal of Digital Convergence, 18(7), 47-56.
  6. Im, Y. J., & Woo, Y. S. (2020). Relationship between the practical practice of teachers for the innovation of elementary schools, the satisfaction of classes, and the implications of class innovation. The Journal of Learner-Centered Curriculum and Instruction, 20(23), 953-974.
  7. Jia, F., Sun, D., & Looi, C. K. (2024). Artificial intelligence in science education (2013-2023): Research trends in ten years. Journal of Science Education and Technology, 33(1), 94-117.
  8. Jones, R. H., & Hafner, C. A. (2021). Understanding digital literacy: A practical introduction (2nd ed.). Routledge.
  9. Kim, E. J. (2020). An analysis of self-reflection journals of pre-service teachers in PBL focusing on teaching innovation components for pre-service teachers' creativity-convergence. The Journal of the Korea Contents Association, 20(3), 481-490.
  10. Kim, H., & Kim, S. (2023). Perceptions and demands of pre-service chemistry teachers following the introduction of the teaching practicum semester system. Korean Journal of Teacher Education, 39(2), 23-42.
  11. Kim, H., Hong, S., Park, Y., Kim, E. Y., Choi, J., & Kim, Y. (2020). Teachers' perceptions of AI in school education. Journal of Educational Technology, 36(3), 905-930.
  12. Kim, J. S., & Lee, J. M. (2020). An investigation of teachers' stages of concern and levels of use about SW education based on concerns-based adoption model. The Journal of the Korea Contents Association, 20(8), 75-87.
  13. Kim, S., & Kim, H. (2023a). An analysis of chemistry teachers' stages of concern and level of use on competency assessment based on CBAM. Journal of Science Education, 47(1), 24-36.
  14. Kim, S., & Kim, H. (2023b). The analysis actual use and perception of class using coding of chemistry teacher. Brain, Digital, & Learning, 13(3), 295-307.
  15. Kim, S., & Kim, H. (2024). Exploring pre-service chemistry teachers' beliefs related to traditionalist and constructivist approaches to teaching and learning. Journal of Field-based Lesson Studies, 5(2), 1-21.
  16. Kim, S., & Paik, S. H. (2016). An analysis of science teachers' stages of concern and levels of use on descriptive assessment. Journal of Korean Chemical Society, 60(5), 353-361.
  17. Kim, S., & Paik, S. H. (2020). Teachers' perceptions of explanatory method based-on process viewpoint for floating and sinking phenomena. Journal of the Korean Association for Science Education, 40(6), 583-594.
  18. Kim, S., Choi, H., & Paik, S. H. (2019). Using a systems thinking approach and a scratch computer program to improve students' understanding of the Bronsted-Lowry acid-base model. Journal of Chemical Education, 96(12), 2926-2936.
  19. Koo, K. H., & Kim, S. W. (2018). An analysis on the stages of teachers' concern and levels of use for a free learning semester in middle schools. Teacher Education Research, 57(2), 169-181.
  20. Kwak, E. R., & Lee, S. Y. (2019). The stages of concerns about maker education of elementary school teacher according to the concerns-based adoption model. The Journal of Elementary Education, 32(4), 133-157.
  21. Lee, D., Shim, H. P., & Baik, J. (2024). Exploration on the feasibility of utilization and teacher perceptions of using ChatGPT for student assessment in science. Journal of the Korean Association for Science Education, 44(1), 119-130.
  22. Lee, J., Noh, E., & Shin, H. J. (2019). Analysis on perceptions of teachers in subject matter of technology and home economics (practical arts) about digital literacy education. Journal of Korean Practical Arts Education Research, 25(3), 107-127.
  23. Lee, J., Park, H. K., & Choi, H. (2018). Effects of SW education using robots on computational thinking, creativity, academic interest and collaborative skill. Journal of the Korean Association of information Education, 22(1), 9-21.
  24. Lee, S. Y. (2020). Elementary school teachers 'understanding and awareness of AI education. Korean Journal of Elementary Education, 31, 15-31.
  25. Lee, S., Kim, S., & Paik, S. H. (2023). The effect of classes using the scratch for quasi-microscopic representation approaches in dynamic equilibrium learning. Journal of the Korean Chemical Society, 67(4), 241-252.
  26. Lukyanova, M., Danilov, S., & Glebova, Z. (2018). Novice teachers' readiness for innovative activities in education. European Proceedings of Social and Behavioural Sciences, 45, 705-711.
  27. MOE. (2015). 2015 Revised National Curriculum.
  28. MOE. (2022a). 2022 Revised National Curriculum.
  29. MOE. (2022b). 2022 Revised National Science Curriculum.
  30. OECD. (2005). The definition and selection of key competencies: Executive summary. OECD Publishing.
  31. OECD. (2016). Innovating education and educating for innovation: The power of digital technologies and skills. OECD Publishing.
  32. Park, H. (2024). Study on the factors which inhibit the teaching practices as perceived by elementary school teachers. Study on the Factors Which Inhibit the Teaching Practices as Perceived by Elementary School Teachers. The Journal of Learner-Centered Curriculum and Instruction, 24(8). 529-549.
  33. Sim, J. H., Park, H. J., & Jeong, J. S. (2018). An investigation of teachers' STEAM education implementation using the concerns based adoption model. Teacher Education Research, 57(3), 325-340.
  34. Suh, M. (2017). The meta-analysis of the relationships among burnout, personal factors, job factors and social factors in elementary and secondary teachers. Korean Journal of Educational Psychology, 31(4), 615-637.
  35. Sydnor, J., Davis, T. R., & Daley, S. (2024). Learning from the unexpected journeys of novice teachers' professional identity development. Education Sciences, 14(8), 895.
  36. Xu, W., & Ouyang, F. (2022). The application of AI technologies in STEM education: A systematic review from 2011 to 2021. International Journal of STEM Education, 9(1), 59.
  37. Yang, H., Ahn, S., Kim, S. H., & Kang, S. J. (2024). An Investigation Into the Effects of AI-Based Chemistry I Class Using Classification Models. Journal of the Korean Chemical Society, 68(3), 160-175.
  38. Yi, J. E., & Shin, J. H. (2012). An analysis of teachers' stage of concerns and implementation on the 2007 revised curriculum based on CBAM. Teacher Education Research, 51(1), 137-151.
  39. Yoon, J. K., & Kim, Y. (2018). Influence of programming education utilizing arduino on creative problem solving ability of high school students. The SNU Journal of Education Research, 27(3), 53-73.