隨著擴增實境與人工智慧技術的快速發展，教育領域開始探索其在實驗教學中的應用潛力，特別是在提升學生探究能力與概念理解方面展現出顯著成效。本研究旨在建置一套融合生成式人工智慧與擴增實境技術的科學實驗學習系統，並探討其對中學生科學學習成效之影響。系統以微軟HoloLens 2頭戴式裝置為基礎，搭配實體光學儀器操作，讓學生得以在真實空間中觀察隨儀器移動的虛擬光線路徑變化，並透過生成式 AI 教學機器人即時獲得學習引導與回饋。系統設計亦融合 Kolb 經驗學習理論，依據「具體經驗」、「反思觀察」、「抽象概念化」與「應用驗證」四個階段，引導學生進行完整的學習歷程。
為驗證本系統之有效性，本研究採準實驗設計，邀請某國中七年級學生共 57 人參與，分為AI 引導學習組（28 人）與單人自主學習組（29 人）。兩組皆接受相同課程與教學內容，單人自主學習組在不具人工智慧輔助的擴增實境環境中進行光學實驗學習；AI 引導學習組則使用本系統輔助進行光學實驗與概念學習。資料收集包含學習成效前後測、學習動機與自我效能問卷、以及學生行為紀錄分析，以瞭解系統在不同層面的影響。
研究結果顯示，AI 引導學習組學生在整體學習成效上有顯著提升，顯著優於自我學習組。然而，在內在動機與自我效能方面，雖有提升趨勢，但未達統計顯著水準。進一步分析發現，AI 引導學習組學生在抽象概念理解與探究操作策略方面的進步幅度明顯。此外，行為分析亦發現，AI 引導學習組學生展現出更多主動操作與反思性思考行為，顯示 AI 引導能有效促進學生由具體經驗轉化為高階概念理解，並應用於問題解決情境中，顯現出本系統在教學互動性與學習成效提升上的應用潛力。

With the rapid advancement of augmented reality (AR) and artificial intelligence (AI) technologies, the education sector has begun exploring their potential applications in experimental teaching, particularly in enhancing students' inquiry skills and conceptual understanding. This study aims to develop a science experiment learning system that integrates generative AI and AR technologies, and to investigate its impact on middle school students' science learning outcomes. The system is based on the Microsoft HoloLens 2 head-mounted device, combined with hands-on manipulation of physical optical instruments, enabling students to observe and manipulate virtual light paths in a real-world environment. Through interactions with a generative AI chatbot, students receive real-time guidance and feedback. The system is also designed in accordance with Kolb's experiential learning theory, guiding students through the four stages of "concrete experience," "reflective observation," "abstract conceptualization," and "active experimentation."
To evaluate the effectiveness of the system, a quasi-experimental design was adopted. A total of 57 seventh-grade students from a junior high school participated in the study, divided into an AI-guided learning group (28 students) and an individual self-learning group (29 students). Both groups received the same curriculum and instructional content. The individual self-learning group conducted optical experiments using an AR environment without AI support, while the AI-guided group used the proposed system to engage in optical experiments and conceptual learning. Data collection included pre- and post-tests of learning performance, questionnaires on learning motivation and self-efficacy, and behavioral log analyses to examine the system's impact on multiple dimensions.
The results indicated that the AI-guided learning group demonstrated a statistically significant improvement in overall learning performance, outperforming the self-learning group. However, while there was an upward trend in intrinsic motivation and self-efficacy, these differences did not reach statistical significance. Further analysis revealed that the AI-guided group showed notable gains in abstract conceptual understanding and inquiry-based operational strategies. Additionally, behavioral analysis showed that students in the AI-guided group exhibited more proactive manipulation and reflective thinking behaviors. These findings suggest that AI guidance effectively facilitates the transformation from concrete experiences to higher-order conceptual understanding and application in problem-solving contexts, highlighting the system's potential in enhancing instructional interactivity and learning effectiveness.

Akçayır, M., & Akçayır, G. (2017). Advantages and challenges associated with augmented reality for education: A systematic review of the literature. Educational research review, 20, 1-11.
Anuar, S., Nizar, N., & Ismail, M. A. (2021). The impact of using augmented reality as teaching material on students' motivation. Asian Journal of Vocational Education And Humanities, 2(1), 1-8.
Azuma, R., Baillot, Y., Behringer, R., Feiner, S., Julier, S., & MacIntyre, B. (2001). Recent advances in augmented reality. IEEE computer graphics and applications, 21(6), 34-47.
Bakeman, R., & Gottman, J. M. (1997). Observing interaction: An introduction to sequential analysis. Cambridge university press.
Bandura, A., & Wessels, S. (1997). Self-efficacy (pp. 4-6). Cambridge: Cambridge University Press.
Billinghurst, M., & Duenser, A. (2012). Augmented reality in the classroom. Computer, 45(7), 56-63.
Billinghurst, M., Clark, A., & Lee, G. (2015). A survey of augmented reality. Foundations and Trends® in Human–Computer Interaction, 8(2-3), 73-272.
Buchner, J., Buntins, K., & Kerres, M. (2022). The impact of augmented reality on cognitive load and performance: A systematic review. Journal of Computer Assisted Learning, 38(1), 285-303.
Cai, S., Wang, X., & Chiang, F. K. (2014). A case study of Augmented Reality simulation system application in a chemistry course. Computers in human behavior, 37, 31-40.
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. Ieee Access, 8, 75264-75278.
Chiang, T. H., Yang, S. J., & Hwang, G. J. (2014). An augmented reality-based mobile learning system to improve students’ learning achievements and motivations in natural science inquiry activities. Journal of Educational Technology & Society, 17(4), 352-365.
Chiu, T. K. (2024). The impact of Generative AI (GenAI) on practices, policies and research direction in education: A case of ChatGPT and Midjourney. Interactive Learning Environments, 32(10), 6187-6203.
Cohen, J. (2013). Statistical power analysis for the behavioral sciences. routledge.
Dai, C. P., Ke, F., Pan, Y., Moon, J., & Liu, Z. (2024). Effects of artificial intelligence-powered virtual agents on learning outcomes in computer-based simulations: A meta-analysis. Educational Psychology Review, 36(1), 31.
Deng, R., Jiang, M., Yu, X., Lu, Y., & Liu, S. (2025). Does ChatGPT enhance student learning? A systematic review and meta-analysis of experimental studies. Computers & Education, 227, 105224.
Duncan, T. G., & McKeachie, W. J. (2005). The making of the motivated strategies for learning questionnaire. Educational psychologist, 40(2), 117-128.
Duncan, T., Pintrich, P., Smith, D., & Mckeachie, W. (2015). Motivated strategies for learning questionnaire (MSLQ) manual. University of Michigan, National Center for Research to Improve Postsecondary Teaching and Learning. https://doi. org/10.13140/RG. 2.1.2547.6968.
Dunleavy, M., & Dede, C. (2013). Augmented reality teaching and learning. Handbook of research on educational communications and technology, 735-745.
Dunleavy, M., Dede, C., & Mitchell, R. (2009). Affordances and limitations of immersive participatory augmented reality simulations for teaching and learning. Journal of science Education and Technology, 18, 7-22.
Gelana, F., & Campbell, A. (2024, January). The Possibilities of AI and Augmented Reality in Education. In 2024 IEEE International Conference on Consumer Electronics (ICCE) (pp. 1-4). IEEE.
George, D. and Mallery, M. (2010) SPSS for Windows Step by Step: A Simple Guide and Reference, 17.0 Update, 10th Edition, Pearson, Boston
Gunturu, A., Jadon, S., Zhang, N., Faraji, M., Thundathil, J., Ahmad, T., ... & Suzuki, R. (2024). RealitySummary: Exploring On-Demand Mixed Reality Text Summarization and Question Answering using Large Language Models. arXiv preprint arXiv:2405.18620.
Hadie, S. N., & Yusoff, M. S. (2016). Assessing the validity of the cognitive load scale in a problem-based learning setting. Journal of Taibah University Medical Sciences, 11(3), 194-202.
Hair, J. F. (2009). Multivariate data analysis.
Hashemyolia, S., Asmuni, A., Ayub, A. F. M., Daud, S. M., & Shah, J. A. (2015). Motivation to use self-regulated learning strategies in learning management system amongst science and social science undergraduates. Asian Social Science, 11(3), 49.
Holmes, W. (2019). Artificial intelligence in education. In Encyclopedia of education and information technologies (pp. 1-16). Springer, Cham.
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education promises and implications for teaching and learning. Center for Curriculum Redesign.
Kamalov, F., Santandreu Calonge, D., & Gurrib, I. (2023). New era of artificial intelligence in education: Towards a sustainable multifaceted revolution. Sustainability, 15(16), 12451.
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., ... & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and individual differences, 103, 102274.
Khan, T., Johnston, K., & Ophoff, J. (2019). The impact of an augmented reality application on learning motivation of students. Advances in Human‐Computer Interaction, 2019(1), 7208494.
Kolb, David. (1984). Experiential Learning: Experience As The Source Of Learning And Development.
Law, E. L. C., & Heintz, M. (2021). Augmented reality applications for K-12 education: A systematic review from the usability and user experience perspective. International Journal of Child-Computer Interaction, 30, 100321.
Lee, D., Arnold, M., Srivastava, A., Plastow, K., Strelan, P., Ploeckl, F., ... & Palmer, E. (2024). The impact of generative AI on higher education learning and teaching: A study of educators’ perspectives. Computers and Education: Artificial Intelligence, 6, 100221.
Leppink, J., & Van den Heuvel, A. (2015). The evolution of cognitive load theory and its application to medical education. Perspectives on medical education, 4, 119-127.
Loewenthal, K.M., & Lewis, C.A. (2001). An Introduction to Psychological Tests and Scales (2nd ed.). Psychology Press.
Nunnally, J. C. (1978). Psychometric Theory: 2d Ed. McGraw-Hill.
Pajares, F. (1997). Current directions in self-efficacy research. Advances in motivation and achievement, 10(149), 1-49.
Pintrich, P. R. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ).
Pintrich, P. R., & De Groot, E. V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of educational psychology, 82(1), 33.
Salinas-Navarro, D. E., Vilalta-Perdomo, E., Michel-Villarreal, R., & Montesinos, L. (2024). Designing experiential learning activities with generative artificial intelligence tools for authentic assessment. Interactive Technology and Smart Education, 21(4), 708-734.
Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380-1400.
Stadler, M., Bannert, M., & Sailer, M. (2024). Cognitive ease at a cost: LLMs reduce mental effort but compromise depth in student scientific inquiry. Computers in Human Behavior, 160, 108386.
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive science, 12(2), 257-285.
Sweller, J., Ayres, P., Kalyuga, S., Sweller, J., Ayres, P., & Kalyuga, S. (2011). Altering element interactivity and intrinsic cognitive load. Cognitive load theory, 203-218.
Thees, M., Kapp, S., Strzys, M. P., Beil, F., Lukowicz, P., & Kuhn, J. (2020). Effects of augmented reality on learning and cognitive load in university physics laboratory courses. Computers in Human Behavior, 108, 106316.
VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational psychologist, 46(4), 197-221.
Villegas-Ch, W., García-Ortiz, J., & Sánchez-Viteri, S. (2024). Educational advances in the metaverse: Boosting learning through virtual and augmented reality and artificial intelligence. IEEE Access.
Wang, J., & Fan, W. (2025). The effect of ChatGPT on students’ learning performance, learning perception, and higher-order thinking: insights from a meta-analysis. Humanities and Social Sciences Communications, 12(1), 1-21.
Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 252, 124167.
Wei, Z., Hassan, N. C., Hassan, S. A., Ismail, N., Gu, X., & Dong, J. Psychometric Properties of the Self-Efficacy Subscale of the Motivated Strategies for Learning Questionnaire (MSLQ) among Chinese Undergraduates.
Wu, H. K., Lee, S. W. Y., Chang, H. Y., & Liang, J. C. (2013). Current status, opportunities and challenges of augmented reality in education. Computers & education, 62, 41-49.
Yan, L., Sha, L., Zhao, L., Li, Y., Martinez‐Maldonado, R., Chen, G., ... & Gašević, D. (2024). Practical and ethical challenges of large language models in education: A systematic scoping review. British Journal of Educational Technology, 55(1), 90-112.
Yardley, S., Teunissen, P. W., & Dornan, T. (2012). Experiential learning: transforming theory into practice. Medical teacher, 34(2), 161-164.
Yilmaz, O. (2021). Augmented Reality in Science Education: An Application in Higher Education. Shanlax International Journal of Education, 9(3), 136-148.
Zainudin, A. (2012). Structural equation modeling using AMOS graphic. Shah Alam: Universiti Teknologi MARA Publication Centre (UPENA).
Zhai, X. (2023). ChatGPT for next generation science learning. XRDS: Crossroads, The ACM Magazine for Students, 29(3), 42-46.
Zhai, X., Nyaaba, M., & Ma, W. (2024). Can generative AI and ChatGPT outperform humans on cognitive-demanding problem-solving tasks in science?. Science & Education, 1-22.