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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 王偉仲 | zh_TW |
| dc.contributor.advisor | Weichung Wang | en |
| dc.contributor.author | 蘇文楷 | zh_TW |
| dc.contributor.author | Wenkai Su | en |
| dc.date.accessioned | 2025-08-18T00:51:42Z | - |
| dc.date.available | 2025-08-18 | - |
| dc.date.copyright | 2025-08-15 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-08-05 | - |
| dc.identifier.citation | [1] J. S. Bruner. The process of education. Harvard university press, 2009.
[2] G. Chartrand, P. M. Cheng, E. Vorontsov, M. Drozdzal, S. Turcotte, C. J. Pal, S. Kadoury, and A. Tang. Deep learning: a primer for radiologists. Radiographics, 37(7):2113–2131, 2017. [3] P. M. Cheng, E. Montagnon, R. Yamashita, I. Pan, A. Cadrin-Chênevert, F. Perdigón Romero, G. Chartrand, S. Kadoury, and A. Tang. Deep learning: an update for radiologists. Radiographics, 41(5):1427–1445, 2021. [4] R. Deng, M. Jiang, X. Yu, Y. Lu, and S. Liu. Does chatgpt enhance student learning? a systematic review and meta-analysis of experimental studies. Computers & Education, 227:105224, 2025. [5] R. M. Harden. What is a spiral curriculum? Medical teacher, 21(2):141–143, 1999. [6] F. Isensee, P. F. Jaeger, S. A. Kohl, J. Petersen, and K. H. Maier-Hein. nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2):203–211, 2021. [7] F. Jiang, Y. Jiang, H. Zhi, Y. Dong, H. Li, S. Ma, Y. Wang, Q. Dong, H. Shen, and Y. Wang. Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology, 2(4), 2017. [8] J. Kim. Leading teachers’ perspective on teacher-ai collaboration in education. Education and information technologies, 29(7):8693–8724, 2024. [9] G. Kourounis, A. A. Elmahmudi, B. Thomson, J. Hunter, H. Ugail, and C. Wilson. Computer image analysis with artificial intelligence: a practical introduction to convolutional neural networks for medical professionals. Postgraduate medical journal, 99(1178):1287–1294, 2023. [10] Y. LeCun, Y. Bengio, and G. Hinton. Deep learning. nature, 521(7553):436–444, 2015. [11] W. Li, A. Yuille, and Z. Zhou. How well do supervised 3d models transfer to medical imaging tasks? arXiv preprint arXiv:2501.11253, 2025. [12] M. Rana and M. Bhushan. Machine learning and deep learning approach for medical image analysis: diagnosis to detection. Multimedia Tools and Applications, 82(17):26731–26769, 2023. [13] R. Tolentino, A. Baradaran, G. Gore, P. Pluye, and S. Abbasgholizadeh-Rahimi. Curriculum frameworks and educational programs in ai for medical students, residents, and practicing physicians: scoping review. JMIR medical education, 10(1):e54793, 2024. [14] R. Tozuka, H. Johno, A. Amakawa, J. Sato, M. Muto, S. Seki, A. Komaba, and H. Onishi. Application of notebooklm, a large language model with retrieval-augmented generation, for lung cancer staging. Japanese Journal of Radiology, 43(4):706–712, 2025. [15] E. Tufino. Notebooklm: An llm with rag for active learning and collaborative tutoring. arXiv preprint arXiv:2504.09720, 2025. [16] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. [17] C. Wang, C. Xiao, X. Zhang, Y. Zhu, X. Chen, Y. Li, and H. Qi. Exploring medical students’ intention to use of chatgpt from a programming course: a grounded theory study in china. BMC Medical Education, 25(1):209, 2025. [18] J. Wang and W. Fan. 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, 2025. [19] J. Wang, S. Wang, and Y. Zhang. Deep learning on medical image analysis. CAAI Transactions on Intelligence Technology, 10(1):1–35, 2025. [20] H. Yu. The application and challenges of chatgpt in educational transformation: New demands for teachers’ roles. Heliyon, 10(2), 2024. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/98555 | - |
| dc.description.abstract | 隨著人工智慧技術在醫療領域快速發展，欠缺架構完整的智慧醫療教育課程已成為一大問題，同時教育界對於如何有效整合 AI 輔助教學工具（如 ChatGPT）仍缺乏明確策略。本研究設計一套涵蓋醫學影像與人工智慧核心概念的智慧醫療課程，採用螺旋式架構並提供多種彈性學習路徑，以適應不同學習者需求。課程開發與教學過程廣泛應用生成式 AI 工具協助教材製作與教學策略規劃。本研究以混合研究方法，在為期六週的課程中進行驗證，並以課前課後問卷評估學習成效。結果顯示，學員自認在課程結束後於人工智慧、Python 程式設計與醫學知識方面皆有明顯進步；同時，AI 工具的應用有效提升教材開發效率並驗證了 AI 輔助教學策略的可行性。本研究亦提出 AI 輔助學習策略，以促進學習者更高效且深入地參與智慧醫療課程，研究成果可作為未來智慧醫療教育課程設計與 AI 工具整合的參考。 | zh_TW |
| dc.description.abstract | As artificial intelligence (AI) rapidly advances in healthcare, the lack of a comprehensive and structured medical AI educational curriculum has become a significant concern. Additionally, clear strategies for effectively integrating AI tools, such as ChatGPT, into educational practices remain scarce. To address these challenges, this study proposes a structured medical AI curriculum that encompasses core concepts in medical imaging and artificial intelligence. Utilizing a spiral curriculum framework, the design provides multiple flexible learning pathways to accommodate diverse learner needs. Throughout the course development and teaching process, generative AI tools were extensively employed to support the creation of instructional materials and the formulation of teaching strategies. A mixed methods approach was adopted, implementing a six-week pilot course, with pre-course and post-course questionnaires administered to evaluate learning effectiveness. The results indicated that learners perceived significant improvements in their knowledge of AI, Python programming, and medical concepts after completing the course. Furthermore, the use of AI tools significantly improved the efficiency of curriculum development, validating the feasibility of AI-assisted teaching strategies. This study also proposes specific AI-assisted learning strategies to facilitate deeper and more efficient engagement among learners in medical AI courses. The findings provide valuable insights and practical guidance for future medical AI curriculum design and the integration of AI tools in educational contexts. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-08-18T00:51:42Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-08-18T00:51:42Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 致謝i
摘要ii Abstract iii Contents v List of Figures viii List of Tables ix Chapter 1 Introduction 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Research Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Organization of the Thesis . . . . . . . . . . . . . . . . . . . . . . . 2 Chapter 2 Literature Review 4 2.1 Medical AI Curriculum . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2 AI-assisted Teaching . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.3 AI-assisted Learning . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Chapter 3 Methods 8 3.1 Research Framework . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.2 Curriculum Design . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.2.1 Spiral Curriculum . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.2.2 Curriculum Framework . . . . . . . . . . . . . . . . . . . . . . . . 11 3.2.2.1 Fundamentals . . . . . . . . . . . . . . . . . . . . . . 13 3.2.2.2 AI for Medical Imaging . . . . . . . . . . . . . . . . . 16 3.2.3 Learner-Centered Flexible Curriculum Pathways . . . . . . . . . . 23 3.2.3.1 One-year Curriculum Pathway . . . . . . . . . . . . . 23 3.2.3.2 Six-month Curriculum Pathway . . . . . . . . . . . . . 28 3.2.3.3 Six-week Short-term Pathway . . . . . . . . . . . . . . 29 3.3 Selection and Integration of AI Tools . . . . . . . . . . . . . . . . . 30 3.3.1 Selection of AI Tools . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.3.2 Integration of AI Tools in Teaching . . . . . . . . . . . . . . . . . . 31 3.3.3 Integration of AI Tools in Learning . . . . . . . . . . . . . . . . . . 31 3.4 Curriculum Implementation Details . . . . . . . . . . . . . . . . . . 32 Chapter 4 Results and Discussion 34 4.1 Curriculum Implementation Results . . . . . . . . . . . . . . . . . . 34 4.2 AI-Assisted Teaching Strategies . . . . . . . . . . . . . . . . . . . . 35 4.3 AI-Assisted Learning Strategies . . . . . . . . . . . . . . . . . . . . 39 Chapter 5 Conclusion 41 5.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 5.2 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 5.3 Future Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Reference 44 Appendix A - Course Materials 47 Appendix B - Pre-course Survey Results for the Medical AI Curriculum 49 Appendix C - Post-course Survey Results for the Medical AI Curriculum 52 | - |
| dc.language.iso | en | - |
| dc.subject | 醫學影像 | zh_TW |
| dc.subject | 智慧醫療課程 | zh_TW |
| dc.subject | 課程設計 | zh_TW |
| dc.subject | AI 輔助學習 | zh_TW |
| dc.subject | AI 輔助教學 | zh_TW |
| dc.subject | AI-assisted Teaching | en |
| dc.subject | AI-assisted Learning | en |
| dc.subject | Curriculum Design | en |
| dc.subject | Medical Imaging | en |
| dc.subject | Medical AI Curriculum | en |
| dc.title | 智慧醫療課程之設計與 AI 輔助教學及學習策略 | zh_TW |
| dc.title | Designing a Medical AI Curriculum With AI-assisted Teaching and Learning Strategies | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 林吉晉;許育萍 | zh_TW |
| dc.contributor.oralexamcommittee | Gigin Lin;Yu-Ping Hsu | en |
| dc.subject.keyword | 智慧醫療課程,醫學影像,AI 輔助教學,AI 輔助學習,課程設計, | zh_TW |
| dc.subject.keyword | Medical AI Curriculum,Medical Imaging,AI-assisted Teaching,AI-assisted Learning,Curriculum Design, | en |
| dc.relation.page | 53 | - |
| dc.identifier.doi | 10.6342/NTU202503045 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2025-08-08 | - |
| dc.contributor.author-college | 理學院 | - |
| dc.contributor.author-dept | 應用數學科學研究所 | - |
| dc.date.embargo-lift | N/A | - |
| 顯示於系所單位： | 應用數學科學研究所 | |
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