生成式人工智慧對於線上科學探究之影響

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朱翊瑄(Yi-Syuan Chu) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

生成式人工智慧對於線上科學探究之影響
(The Impact of Generative Artificial Intelligence on Online Scientific Inquiry)

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至系統瀏覽論文 (2026-8-1以後開放)

摘要(中)

科學探究是一種以問題為導向的學習方法，但教師的指導對於科學探究學習的有效性至關重要，因此本研究將結合線上科學探究平台與基於生成式人工智慧的對話機器人，建立可以引導學生高層次思維的教育對話機器人──智慧助教，擔任教師的角色，引導學生進行科學探究，探討智慧助教的協助下對學生答題表現、學習成效、探究行為與自我效能的影響。
本研究招募台灣北部的高中生共62人，在CoSci線上科學探究平台中進行探究活動，主題為高中物理的質心與動量單元，活動中包含物理模擬與多個探究問題，學生需完成活動中的所有探究問題。本研究將所有學生分為兩組，實驗組採用本研究提出之引導探究智慧助教輔助進行探究學習活動(33人)，控制組學生則搭配相關物理教材自主進行探究活動(29人)。活動前後蒐集學生物理概念測驗、自我效能問卷，並記錄學生在探究學習活動中的回答和操作行為。此外，學生與智慧助教的對話、互動紀錄也將一併蒐集。
研究結果顯示，在經過活動後，兩組學生的物理概念測驗後測成績和自我效能皆有顯著提升。然而，接受智慧助教協助的學生在探究學習活動中的答題表現顯著高於控制組，且在物理概念後測成績中的進步幅度也有顯著提升，這表明智慧助教的引導有助於學生建構科學概念。但在自我效能方面，可能由於活動時間不長，因此智慧助教的協助對其並無顯著影響。在探究行為方面，智慧助教引導的學生在進行活動時參與度較高，回答探究問題的次數顯著高於控制組，在進行探究活動時更有策略性；而自主進行探究的學生則是在進行活動時較無頭緒與方向性，在切換不同探究問題的次數顯著高於實驗組。此外，在對話互動分析中，發現智慧助教的回饋可以在學生需要支援時提供幫助，其提問也可引發學生思考。綜上所述，在線上學習環境中，智慧助教的協助可以有效幫助學生進行科學探究，能夠提供學生即時的教學輔助，以提升學生的學習表現與對物理概念的掌握程度。

摘要(英)

Scientific inquiry is a problem-oriented learning method, but teacher guidance is crucial for its effectiveness. Therefore, this study combines an online scientific inquiry platform with a Generative AI-based chatbot to establish an pedagogical agent called the "AI-Tutor," which can guide students′ higher-order thinking. Acting in the role of a teacher, the AI-Tutor guides students through scientific inquiry, investigating the effects of its assistance on students′ answer performance, learning effectiveness, inquiry behavior, and self-efficacy.
This study recruited 62 high school students from northern Taiwan to participate in an inquiry activity on the CoSci online scientific inquiry platform. The activity focused on the "center of mass and momentum" unit in high school physics and included computer simulations and multiple inquiry questions that students needed to complete. The students were divided into two groups: the experimental group, which was guided by the AI-Tutor (33 students) , and the control group, which conducted the inquiry activities independently using related physics materials (29 students). This study collected pre- and post-test data on both physics conceptual test and self-efficacy questionnaire. Students′ answers on inquiry questions and operational behaviors during the inquiry activities were recorded. Additionally, the interactions and dialogues between students and the AI-Tutor were collected.
The results showed that both groups significantly improved their post-test scores in the physics conceptual test and their self-efficacy after the activity. However, students assisted by the AI-Tutor performed significantly better on inquiry activity questions than the control group and showed greater improvement in their post-test physics concept scores, indicating that the guidance of the AI-Tutor helped students construct scientific concepts. However, due to the short duration of the activity, the assistance of the AI-Tutor did not have a significant impact on self-efficacy. In terms of inquiry behaviors, students guided by the AI-Tutor participated more actively and answered inquiry questions significantly more frequently than the control group. They were also more strategic during the inquiry activities. Students in the control group were more directionless and switched between different inquiry questions significantly more frequently than the experimental group. Moreover, analysis of the dialogue interactions revealed that the feedback from the AI-Tutor provided support when students needed it, and its questions prompted students to think critically. In summary, in an online learning environment, the assistance of the AI-Tutor can effectively help students engage in scientific inquiry, providing timely instructional support to enhance their learning performance and understanding of physics concepts.

關鍵字(中)

★ 生成式人工智慧
★ 智慧助教
★ 科學探究
★ 電腦模擬

關鍵字(英)

★ Generative AI
★ AI-tutor
★ Scientific Inquiry
★ Computer Simulation

論文目次

摘要 i
Abstract ii
致謝 iv
目錄 vi
圖目錄 ix
表目錄 x
第一章緒論 1
1-1 研究背景與動機 1
1-2 研究目的與問題 3
1-3 名詞解釋 4
1-3-1 科學探究(Scientific Inquiry) 4
1-3-2 電腦模擬(Computer Simulation) 4
1-3-3 教學對話機器人(Pedagogical Tutor) 4
1-4 論文架構 4
第二章文獻探討 6
2-1 科學探究 6
2-2 智慧型教學系統 7
2-3 生成式人工智慧 8
第三章系統設計 10
3-1 系統架構 10
3-2 系統介紹 11
3-2-1 CoSci電腦科學模擬平台 11
3-2-2 探究智慧助教系統 13
3-2-3 智慧助教引導對話設計 17
第四章研究方法 20
4-1 研究流程 20
4-2 研究對象 21
4-3 實驗設計 21
4-4 研究工具 25
4-4-1 探究學習活動 25
4-4-2 物理概念測驗 28
4-4-3 自我效能問卷 28
4-5 資料蒐集與分析 29
4-5-1 探究學習活動表現 29
4-5-2 學習成效 32
4-5-3 自我效能 33
4-5-4 探究學習活動操作行為 33
4-5-5 智慧助教與學生之對話互動模式 35
第五章實驗結果與討論 37
5-1 探究學習活動表現 37
5-2 學習成效 39
5-3 自我效能 41
5-4 探究學習活動操作行為 41
5-5 與智慧助教互動情形探討 51
5-5-1 互動情形發生人數比例 51
5-5-2 互動情形對話案例 53
5-5-3 不符合預期回應分類與案例 58
第六章結論與建議 64
6-1 結論 64
6-1-1 在探究學習活動中，智慧助教是否影響學生在探究問題上的答題表現? 64
6-1-2 智慧助教對學生學習成效之影響? 65
6-1-3 智慧助教對學生科學學習之自我效能是否有影響? 65
6-1-4 智慧助教的支持是否影響學生在探究活動中的行為? 66
6-1-5 智慧助教與學生之對話互動模式為何?智慧助教如何引導學生進行探究? 67
6-2 未來建議 68
參考文獻 69
附錄 A 質心與動量教材 75
附錄 B 自我效能問卷 77
附錄 C 探究活動評分標準與範例 78

參考文獻

Ahmad, N., Murugesan, S., & Kshetri, N. (2023). Generative artificial intelligence and the education sector. Computer, 56(6), 72-76.
Blonder, R., Mamlock-Naaman, R., & Hofstein, A. (2008). Analyzing inquiry questions of high-school students in a gas chromatography open-ended laboratory experiment [10.1039/B812414K]. Chemistry Education Research and Practice, 9(3), 250-258. https://doi.org/10.1039/B812414K
Chan, C. K. Y., & Tsi, L. H. (2023). The AI revolution in education: Will AI replace or assist teachers in higher education? arXiv preprint arXiv:2305.01185.
Chang, C. J., Liu, C. C., Wu, Y. T., Chang, M. H., Chiang, S. F., Chiu, B. C., Wen, C. T., Hwang, F. K., Chao, P. Y., Lai, C. H., Wu, S. W., & Chang, C. K. (2016). Students′ perceptions on problem solving with collaborative computer simulation. In 24th International Conference on Computers in Education, ICCE 2016 (pp. 166-168). Asia-Pacific Society for Computers in Education.
Council, N. R., Center for Science, M., Education, E., & Inquiry, C. o. D. o. a. A. t. t. N. S. E. S. o. S. (2000). Inquiry and the national science education standards: A guide for teaching and learning. National Academies Press. https://books.google.com.tw/books?id=YNf0HXz7DzkC
de Jong, T., Linn, M. C., & Zacharia, Z. C. (2013). Physical and virtual laboratories in science and engineering education. Science, 340(6130), 305-308. https://doi.org/10.1126/science.1230579
de Jong, T., Sotiriou, S., & Gillet, D. (2014). Innovations in STEM education: the Go-Lab federation of online labs. Smart Learning Environments, 1(1), 3. https://doi.org/10.1186/s40561-014-0003-6
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., . . . Wright, R. (2023). Opinion paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71. https://doi.org/10.1016/j.ijinfomgt.2023.102642
Feng, S., Magana, A. J., & Kao, D. (2021). A systematic review of literature on the effectiveness of intelligent tutoring systems in STEM.In 2021 IEEE frontiers in education conference (fie) (pp. 1-9). IEEE. https://doi.org/10.1109/fie49875.2021.9637240
Freire, S. K., Wang, C., & Niforatos, E. (2024). Chatbots in knowledge-intensive contexts: Comparing intent and LLM-based systems. ArXiv, abs/2402.04955.
Gan, W., Qi, Z., Wu, J., & Lin, C.-W. (2023). Large language models in education: Vision and opportunities. 2023 IEEE International Conference on Big Data (BigData), 4776-4785.
Gertner, A. S., & VanLehn, K. (2000, 2000, June). Andes: A coached problem solving environment for physics. In International conference on intelligent tutoring systems (pp. 133-142). Berlin, Heidelberg: Springer Berlin Heidelberg.
Gillies, R. M., Nichols, K., Burgh, G., & Haynes, M. (2014). Primary students’ scientific reasoning and discourse during cooperative inquiry-based science activities. International Journal of Educational Research, 63, 127-140. https://doi.org/https://doi.org/10.1016/j.ijer.2013.01.001
Girault, I., Peffer, M., Chiocarriello, A., Renken, M., & Otrel-Cass, K. (2016). Computer simulations on a multidimensional continuum: A definition and examples. In M. Renken, M. Peffer, K. Otrel-Cass, I. Girault, & A. Chiocarriello (Eds.), Simulations as Scaffolds in Science Education (pp. 5-14). Springer International Publishing. https://doi.org/10.1007/978-3-319-24615-4_2
Graesser, A. C., Lu, S., Jackson, G. T., Mitchell, H. H., Ventura, M., Olney, A., & Louwerse, M. M. (2004). AutoTutor: A tutor with dialogue in natural language. Behavior Research Methods, Instruments, & Computers, 36(2), 180-192. https://doi.org/10.3758/BF03195563
Graesser, A. C., VanLehn, K., Rose, C. P., Jordan, P. W., & Harter, D. (2001). Intelligent tutoring systems with conversational dialogue. AI Magazine, 22(4), 39. https://doi.org/10.1609/aimag.v22i4.1591
Harris, C. J., Phillips, R. S., & Penuel, W. R. (2012). Examining teachers’ instructional moves aimed at developing students’ Ideas and questions in learner-centered science classrooms. Journal of Science Teacher Education, 23(7), 769-788. https://doi.org/10.1007/s10972-011-9237-0
Hmelo-Silver, C. E., Duncan, R. G., & Chinn, C. A. (2007). Scaffolding and achievement in problem-based and inquiry learning: A response to Kirschner, Sweller, and Clark (2006). Educational Psychologist, 42(2), 99-107. https://doi.org/10.1080/00461520701263368
Hwang, G.-J., & Chang, C.-Y. (2021). A review of opportunities and challenges of chatbots in education. Interactive Learning Environments, 31(7), 4099-4112. https://doi.org/10.1080/10494820.2021.1952615
Jin, H., Lee, S., Shin, H. J., & Kim, J. (2023). Teach AI how to code: Using large language models as teachable agents for programming education. Proceedings of the CHI Conference on Human Factors in Computing Systems.
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., . . . Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103. https://doi.org/10.1016/j.lindif.2023.102274
Kawalkar, A., & Vijapurkar, J. (2013). Scaffolding Science Talk: The role of teachers′ questions in the inquiry classroom. International Journal of Science Education, 35(12), 2004-2027. https://doi.org/10.1080/09500693.2011.604684
Keselman, A. (2003). Supporting inquiry learning by promoting normative understanding of multivariable causality. Journal of Research in Science Teaching, 40(9), 898-921. https://doi.org/10.1002/tea.10115
Kline, R. B. (2023). Principles and practice of structural equation modeling. Guilford publications.
Kolb, D. A. (2014). Experiential learning: Experience as the source of learning and development. FT press.
Kuhn, D., & Pease, M. (2008). What needs to develop in the development of inquiry skills? Cognition and Instruction, 26(4), 512-559. https://doi.org/10.1080/07370000802391745
Lai, C.-L., Hwang, G.-J., & Tu, Y.-H. (2018). The effects of computer-supported self-regulation in science inquiry on learning outcomes, learning processes, and self-efficacy. Educational Technology Research and Development, 66(4), 863-892. https://doi.org/10.1007/s11423-018-9585-y
Latham, A., Crockett, K., McLean, D., & Edmonds, B. (2012). A conversational intelligent tutoring system to automatically predict learning styles. Computers & Education, 59(1), 95-109. https://doi.org/https://doi.org/10.1016/j.compedu.2011.11.001
Latham, A. M., Crockett, K. A., McLean, D. A., Edmonds, B., & O′shea, K. (2010, July). Oscar: An intelligent conversational agent tutor to estimate learning styles. In International conference on fuzzy systems (pp. 1-8). IEEE.
Lazonder, A. W., & Harmsen, R. (2016). Meta-analysis of inquiry-based learning: Effects of guidance. Review of Educational Research, 86, 681 - 718.
Limna, P., Kraiwanit, T., Jangjarat, K., Klayklung, P., & Chocksathaporn, P. (2023). The use of ChatGPT in the digital era: Perspectives on chatbot implementation., 6. https://doi.org/10.37074/jalt.2023.6.1.32
Linn, M. C., Clark, D., & Slotta, J. D. (2003). WISE design for knowledge integration. Science Education, 87(4), 517-538. https://doi.org/https://doi.org/10.1002/sce.10086
Madhuri, G. V., Kantamreddi, V. S. S. N., & Prakash Goteti, L. N. S. (2012). Promoting higher order thinking skills using inquiry-based learning. European Journal of Engineering Education, 37, 117 - 123.
Maeots, M., Pedaste, M., & Sarapuu, T. (2011, July). Interactions between inquiry processes in a web-based learning environment. In 2011 IEEE 11th International Conference on Advanced Learning Technologies (pp. 331-335). IEEE.
Mayer, R. E. (2004). Should there be a three-strikes rule against pure discovery learning? The case for guided methods of instruction. Am Psychol, 59(1), 14-19. https://doi.org/10.1037/0003-066x.59.1.14
McElhaney, K. W., & Linn, M. C. (2011). Investigations of a complex, realistic task: Intentional, unsystematic, and exhaustive experimenters. Journal of Research in Science Teaching, 48(7), 745-770. https://doi.org/https://doi.org/10.1002/tea.20423
Ng, D. T. K., Tan, C. W., & Leung, J. K. L. (2024). Empowering student self‐regulated learning and science education through ChatGPT: A pioneering pilot study. British Journal of Educational Technology. https://doi.org/10.1111/bjet.13454
Nwana, H. S. (1990). Intelligent tutoring systems: an overview. Artificial Intelligence Review, 4(4), 251-277. https://doi.org/10.1007/BF00168958
OpenAI. Prompt engineering. https://platform.openai.com/docs/guides/prompt-engineering
OpenAI. (2022). Introducing ChatGPT. https://openai.com/index/chatgpt/
Pardos, Z. A., & Bhandari, S. (2023). Learning gain differences between ChatGPT and human tutor generated algebra hints. ArXiv, abs/2302.06871.
Pedaste, M., Mäeots, M., Siiman, L. A., de Jong, T., van Riesen, S. A. N., Kamp, E. T., Manoli, C. C., Zacharia, Z. C., & Tsourlidaki, E. (2015). Phases of inquiry-based learning: Definitions and the inquiry cycle. Educational Research Review, 14, 47-61. https://doi.org/10.1016/j.edurev.2015.02.003
Pyatt, K., & Sims, R. (2012). Virtual and physical experimentation in inquiry-based science labs: Attitudes, performance and access. Journal of Science Education and Technology, 21(1), 133-147. https://doi.org/10.1007/s10956-011-9291-6
Qadir, J. (2023, 1-4 May 2023). Engineering education in the era of ChatGPT: Promise and pitfalls of generative AI for education. In: 2023 IEEE Global Engineering Education Conference (EDUCON). IEEE, 2023. p. 1-9.
Richards, J., Barowy, W., & Levin, D. (1992). Computer simulations in the science classroom. Journal of Science Education and Technology, 1(1), 67-79. https://doi.org/10.1007/BF00700244
Rus, V., D′Mello, S., Hu, X., & Graesser, A. C. (2013). Recent advances in conversational intelligent tutoring systems. AI Magazine, 34(3), 42-54. https://doi.org/https://doi.org/10.1609/aimag.v34i3.2485
Safitri, R. E., & Widjajanti, D. B. (2019). The effect of inquiry in scientific learning on students’ self-confidence. Journal of Physics: Conference Series, 1157.
Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., & Scialom, T. (2023). Toolformer: Language models can teach themselves to use tools. ArXiv, abs/2302.04761.
Tuan, H. L., Chin, C. C., & Shieh, S. H. (2005). The development of a questionnaire to measure students′ motivation towards science learning. International Journal of Science Education, 27(6), 639-654. https://doi.org/10.1080/0950069042000323737
Urban, M., Děchtěrenko, F., Lukavský, J., Hrabalová, V., Svacha, F., Brom, C., & Urban, K. (2024). ChatGPT improves creative problem-solving performance in university students: An experimental study. Computers & Education, 215, 105031. https://doi.org/https://doi.org/10.1016/j.compedu.2024.105031
Veletsianos, G., & Russell, G. S. (2014). Pedagogical agents. In J. M. Spector, M. D. Merrill, J. Elen, & M. J. Bishop (Eds.), Handbook of Research on Educational Communications and Technology (pp. 759-769). Springer New York. https://doi.org/10.1007/978-1-4614-3185-5_61
Volkmann, M. J., Abell, S. K., & Zgagacz, M. (2005). The challenges of teaching physics to preservice elementary teachers: Orientations of the professor, teaching assistant, and students. Science Education, 89, 847-869.
Wieman, C. E., Adams, W. K., & Perkins, K. K. (2008). PhET: Simulations that enhance learning. Science, 322(5902), 682-683. https://doi.org/10.1126/science.1161948
Xiao, J., & Bai, Q. (2022, 19-22 July 2022). iTutor: Promoting AI guided knowledge interaction in online learning. In 2022 International Symposium on Educational Technology (ISET) (pp. 253-257). IEEE.
Zhao, P., Zhang, H., Yu, Q., Wang, Z., Geng, Y., Fu, F., Yang, L., Zhang, W., & Cui, B. (2024). Retrieval-augmented generation for ai-generated content: A survey. ArXiv, abs/2402.19473.
Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., Du, Y., Yang, C., Chen, Y., Chen, Z., Jiang, J., Ren, R., Li, Y., Tang, X., Liu, Z., . . . Wen, J.-r. (2023). A survey of large language models. ArXiv, abs/2303.18223.

指導教授

劉晨鐘(Chen-Chung Liu)

審核日期

2024-7-26

推文