한국지구과학회지 (Journal of the Korean earth science society)

  • 제44권6호
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  • Pages.675-692
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  • 2023
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  • 1225-6692(pISSN)
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  • 2287-4518(eISSN)
한국지구과학회 (The Korean Earth Science Society)
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챗봇 활용 핵심광물 탐구에서 나타난 학생과 생성형 인공지능의 상호작용

Interaction Between Students and Generative Artificial Intelligence in Critical Mineral Inquiry Using Chatbots

  • 투고 : 2023.11.20
  • 심사 : 2023.12.27
  • 발행 : 2023.12.31
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초록

This study used a Chatbot, a generative artificial intelligence (AI), to analyze the interaction between the Chatbot and students when exploring critical minerals from an epistemological aspect. The results, issues to be kept in mind in the teaching and learning process using AI were discussed in terms of the role of the teacher, the goals of education, and the characteristics of knowledge. For this study, we conducted a three-session science education program using a Chatbot for 19 high school students and analyzed the reports written by the students. As a result, in terms of form, the students' questions included search-type questions and non-search-type questions, and in terms of content, in addition to various questions asking about the characteristics of the target, there were also questions requiring a judgment by combining various data. In general, students had a questioning strategy that distinguished what they should aim for and what they should avoid. The Chatbot's answer had a certain form and consisted of three parts: an introduction, a body, and a conclusion. In particular, the conclusion included commentary or opinions with opinions on the content, and in this, value judgments and the nature of science were revealed. The interaction between the Chatbot and the student was clearly evident in the process in which the student organized questions in response to the Chatbot's answers. Depending on whether they were based on the answer, independent or derived questions appeared, and depending on the direction of comprehensiveness and specificity, superordinate, subordinate, or parallel questions appeared. Students also responded to the chatbot's answers with questions that included critical thinking skills. Based on these results, we discovered that there are inherent limitations between Chatbots and students, unlike general classes where teachers and students interact. In other words, there is 'limited interaction' and the teacher's role to complement this was discussed, and the goals of learning using AI and the characteristics of the knowledge they provide were also discussed.

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과제정보

이 연구는 2023년도 한국지질자원연구원의 지질자원 표본·기초학술연구와 선도형 R&D 정책/성과 확산 연구 사업 지원을 받아 수행되었음.

참고문헌

  1. Ausubel, D. P., 2000, The acquisition and retention of knowledge: a cognitive view. Boston: Kluwer Academic Publishers. 212 p.
  2. Busan Metropolitan City office of Education, 2023, Busan Academic Support System. Retrieved September 27, 2023, from https://bass.pen.go.kr/
  3. Chang, J., Park, J. H., and Park, J. S., 2021, An analysis on the trends of education research related to 'Artificial Intelligence chatbot' in Korea: Focusing on implications for use in science education. The Journal of Learner-Centered Curriculum and Instruction, 21(13), 729-743. (in Korean) https://doi.org/10.22251/jlcci.2021.21.13.729
  4. Cho, H., 2020, Discussion for how to apply artificial intelligence to physics education. New Physics: Sae Mulli, 70(11), 974-984. (in Korean) https://doi.org/10.3938/NPSM.70.974
  5. Cho, H., 2023, Understanding of generative artificial intelligence based on textual data and discussion for its application in science education. Journal of the Korean Association for Science Education, 43(3), 307-319. (in Korean)
  6. Choi, Y., 2023, A meta-analysis on research trends in science teaching and learning using artificial intelligence. Biology Education, 51(2), 162-186. (in Korean)
  7. Christensen, D., and Lombardi, D., 2020, Understanding biological evolution through computational thinking a K-12 learning progression. Science & Education, 29(4), 1035-1077. https://doi.org/10.1007/s11191-020-00141-7
  8. Cooper, G., 2023, Examining science education in ChatGPT: An exploratory study of generative artificial intelligence. Journal of Science Education and Technology, 32, 444-452. https://doi.org/10.1007/s10956-023-10039-y
  9. Donga Science, 2023, Is ChatGPT a threat to mathematicians? Retrieved September 27, 2023, from https://www.dongascience.com/news.php?idx=60085%C2%A0
  10. Fergus, S., Botha, M., and Ostovar, M., 2023, Evaluating academic answers generated using chatGPT. Journal of Chemical Education, 100, 1672-1675. https://doi.org/10.1021/acs.jchemed.3c00087
  11. Gibson, P., and Mourad, T., 2018, The growing importance of data literacy in life science education. American journal of botany, 105(12), 1953-1956. https://doi.org/10.1002/ajb2.1195
  12. Gyeonggi Provincial Office of Education, 2023, Gyeonggi Provincial Office of Education High Learning. Retrieved September 27, 2023, from https://hi.goe.go.kr/
  13. Gyeongsangnamdo Office of Education, 2023, ITalkTalk. Retrieved September 27, 2023, from https://support.itt.link/index.do
  14. Ha, M., Lee, G., Shin, S., Lee, J., Choi, S., Choo, J., Kim, N., Lee, H., Lee, J., Cho, Y., Kang, G., and Park, J., 2019, Assessment as a learning-support tool and utilization of artificial intelligence: WA3I project case. School Science Journal, 13(3), 271-282. (in Korean ) https://doi.org/10.15737/SSJ.13.3.201908.271
  15. How, M., and Hung, W., 2019, Educing AI-thinking in science, technology, engineering, arts, and mathematics (STEAM) education. Education Sciences, 9(3), 184.
  16. Han, J., 2021, Creating an open science ecosystem in the field of geological resources. issue! geological resources. Retrieved June 27, 2023, from https://www.kigam.re.kr/gallery.es?mid=a10703020000&bid=0004&act=view&list_no=2962
  17. Heo, K., Kim, H., Lim, S., Kim, M., and Yang, M., 1991, A story of thinking ability development program (V), Regular research report: RR 91-18, Korean Educational Development Institute: Seoul.
  18. Humphry, T., and Fuller, A. L., 2023, Potential ChatGPT use in undergraduate chemistry laboratories. Journal of chemical Education, 100, 1434-1436. https://doi.org/10.1021/acs.jchemed.3c00006
  19. Heo, H., and Chun, J., 2022, Development and application of the 'Advanced Science Research' section class program in the science inquiry experiment on the subject of artificial intelligence. The Journal of Learner-Centered Curriculum and Instruction, 22(8), 533-545. (in Korean) https://doi.org/10.22251/jlcci.2022.22.8.173
  20. Kaldaras, L., Yoshida, N., and Haudek, K., 2022, Rubric development for AI-enabled scoring of three-dimensional constructed-response assessment aligned to NGSS learning progression. Frontiers in Education, 7, 983055.
  21. Kasneci, E., Sessler, K., Kuchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Gunnemann, S. Hullermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., Stadler, M., Weller, J., Kuhn, J., and Kasneci, G., 2023, ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 1-9.
  22. Kastens, K., Krumhansl, R., and Baker, I., 2015, Thinking big. The Science Teacher, 82(5), 25.
  23. Kim, T., 2023, The era of generative AI triggered by ChatGPT, future outlook and counter measures, Future Horizon, 1(55), 2-9. (in Korean)
  24. Kim, W. J., 2022, AI-integrated science teaching through facilitating epistemic discourse in the classroom. Asia-Pacific Science Education, 8, 9-42. https://doi.org/10.1163/23641177-bja10041
  25. Kjelvik, M. K., and Schultheis, E. H., 2019, Getting messy with authentic data: Exploring the potential of using data from scientific research to support student data literacy. CBE-Life Sciences Education, 18(2), 1-8. https://doi.org/10.1187/cbe.18-02-0023
  26. Korea Institute of Geoscience and Mineral Resources, 2021, Critical Mineral: Analysis of 6 critical mineral issues at a glance. Daejeon: KIGAM. (in Korean)
  27. Landis, J. R., and Koch, G. G., 1977, The measurement of observer agreement for categorical data. Biometrics, 33(1), 159-174. https://doi.org/10.2307/2529310
  28. Lederman, N., and Lederman, J., 2014, Research on teaching and learning of Nature of Science. In Lederman, N., & Abell, S. (eds.), Handbook of research on science education, volume II. New York: Routledge.
  29. Lee, J., Kim, G., and Kang, S., 2022, Content system and teaching/learning case study for systematic convergence of artificial intelligence and science subjects. The Journal of Learner-Centered Curriculum and Instruction, 22(13), 623-640. (in Korean) https://doi.org/10.22251/jlcci.2022.22.13.623
  30. Lee, G., Ha, H., Hong, H., and Kim, H., 2018, Exploratory research on automating the analysis of scientific argumentation using machine learning. Journal of the Korean Association for Science Education, 38(2), 219-234. (in Korean)
  31. Lee, S., 2022, Construction of Geology/Topography Center of Environmental Big Data Platform. The Geological Society of Korea Conference Proceedings, 149. (in Korean)
  32. Maddigan, P., and Susnjak, T., 2023, Chat2VIS: generating data visualisations via natural language using ChatGPT, codex and GPT-3 large language models. Retrieved September 28, 2023, from https://www.researchgate.net/publication/368304795_Chat2VIS_Generating_Data_Visualisations_via_Natural_Language_using_ChatGPT_Codex_and_GPT-3_Large_Language_Models
  33. Ministry of Education, 2022a, General overview of elementary and secondary school curriculum. Notification No. 2022-33 of MOE. Seoul: MOE.
  34. Ministry of Education, 2022b, Science Curriculum. Notification No. 2022-33 of MOE Separate volume 9. Seoul: MOE.
  35. Ministry of Education, 2023, AI digital textbook promotion plan. Retrieved September 27, 2023, from https://www.korea.kr/briefing/policyBriefingView.do?newsId=156574196
  36. Novak, J. D., and Gowin, D. B., 1984, Learning how to learn. Cambridge: Cambridge University Press. 194 p.
  37. Oh, P., and Kang, S., 2021, Integrating artificial intelligence to chemistry experiment: Carbon dioxide fountain. Journal of Chemical Education, 98(7), 2376-2380. (in Korean) https://doi.org/10.1021/acs.jchemed.1c00004
  38. Park, S., Lee, B., Ham, E., Lee, Y., and Lee, S., 2023, Exploring the possibility of science-inquiry competence assessment by ChatGPT-4: Comparisons with human evaluators. Korean Journal of Educational Research, 61(4), 299-332. (in Korean) https://doi.org/10.30916/KERA.61.4.299
  39. Russell, S. J., Norvig, P., and Davis, E., 2010, Artificial intelligence: a modern approach. (3rd Ed). Upper Saddle River, NJ: Prentice Hall.
  40. Shin, W., and Shin, D., 2020, A case study on application of artificial intelligence convergence education in elementary biological classification learning. Journal of Korean Elementary Science Education, 39(2), 284-295. (in Korean)
  41. Wu, C., Liu, C., and Huang, Y., 2022, The exploration of continuous learning intention in STEAM education through attitude, motivation, and cognitive load. International Journal of STEM Education, 9(35), 1-22. https://doi.org/10.1186/s40594-022-00346-y
  42. Wu, J., and Tsai, C., 2022, Harnessing the power of promising technologies to transform science education: Prospects and challenges to promote adaptive epistemic beliefs in science learning. International Journal of Science Education, 44(2), 346-353. https://doi.org/10.1080/09500693.2022.2028927
  43. Yang, S., Kim, S., and Kim, J., 2020, Developing relational database for big data platform of isotopes in Korea. 2020 The Geological Society of Korea Conference Proceedings, 149. (in Korean)
  44. Yoon, E., and Park, Y., 2019, Qualitative performance evaluation of the word-embeddin model through learning science textbook corpus (K-STeC). New Physics: Sae Mulli, 69(10), 1038-1052. (in Korean) https://doi.org/10.3938/NPSM.69.1038
  45. Zhai, X., 2021, Practices and theories: How can machine learning assist in innovative assessment practices in science education. Journal of Science Education and Technology, 30, 139-149. https://doi.org/10.1007/s10956-021-09901-8
  46. Zhai, X., He, P., and Krajcik, J., 2022, Applying machine learning to automatically assess scientific models. Journal of Research in Science Teaching, 59(10), 1765-1794. https://doi.org/10.1002/tea.21773
  47. Zhai, X., Shi, L., and Nehm, R., 2021, A meta-analysis of machine learning-based science assessments: Factors impacting machine-human score agreements. Journal of Science Education and Technology, 30(3), 361-379. https://doi.org/10.1007/s10956-020-09875-z