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題名 整合生成式人工智慧與新媒體內容創作教育：混合法質性研究分析
Integrating GenAI into new media content creation education: A mixed-method qualitative research作者 阮德明
Nguyen, Duc Minh貢獻者 林翠絹
Lin, Trisha
阮德明
Nguyen, Duc Minh關鍵詞 生成式人工智慧
生成式人工智慧於媒體教育
生成式人工智慧於內容創作
生成式人工智慧的限制
體驗式學習
創造力
批判性思維
協作學習
GenAI
GenAI in media education
GenAI in content creation
GenAI limitations
experiential learning
creativity
critical thinking
collaborative learning日期 2024 上傳時間 4-十月-2024 10:51:49 (UTC+8) 摘要 在數位媒體快速演變的背景下，本研究探討將生成式人工智慧（GenAI）工具引入教育環境對學生適應產業變革的影響。本研究聚焦於媒體產業深刻轉型的背景下，如何將生成式人工智慧技術融入新媒體內容創作課程，並對學生的體驗式學習及創作過程產生的影響。基於David Kolb的體驗式學習理論，本研究強調通過具體經驗、反思觀察、抽象概念化及主動實驗這四個階段來學習生成式人工智慧技術。此外，研究還探討了生成式人工智慧技術如何塑造學生的製作流程，並影響其創造力、批判性思維、效率及協作學習能力。 近期文獻強調了生成式人工智慧在教育及內容創作領域具有革新潛力，能夠促進主動學習、創造力及批判性思維的提升。然而，關於生成式人工智慧融入媒體教育課程及其對學生學習經驗與結果的影響，實證數據仍然有限。本案例研究採用了混合方法，探討台灣一所頂尖大學的兩門強化生成式人工智慧媒體課程。一門課程側重於利用生成式人工智慧進行線上影片製作及社群媒體行銷，另一門則聚焦於生成式人工智慧在創新概念發想與視覺化中的應用。資料收集包括學期長度的明顯觀察以及對17位學生的深入訪談。觀察於2023年9月至2024年1月進行，深入訪談則於2024年初進行。這些豐富的質性資料通過主題分析進行處理。 混合方法的數據顯示，在具體經驗階段，學生通過自我導向學習和實際專案掌握了生成式人工智慧技術並發展了實用技能。反思觀察階段揭示了生成式人工智慧雖然提升了效率與創造力，但同時也帶來了準確性、可靠性及倫理方面的問題。學生認識到付費工具的優勢，並強調清晰提示對輸出品質的重要性。在抽象概念化階段，學生通過人機協作進一步提升了技能，通過交叉驗證來確保準確性，同時應對倫理問題。最終，在主動實驗階段，學生將生成式人工智慧工具應用於實際情境中，專注於迭代改進與問題解決，從而鞏固了他們對AI協作與倫理的理解，提升了專案的品質。 關於生成式人工智慧工具對內容創作過程的影響，這些工具顯著改變了前期製作和後期製作階段，特別是在後期製作階段的影響最大。在前期製作中，ChatGPT與MidJourney等工具有助於研究、想法生成及概念視覺化。然而，製作階段則受到的影響較小，仍需要大量的人工投入，尤其是在創意決策和執行方面。相比之下，後期製作廣泛使用了生成式人工智慧工具來自動化重複性工作，如字幕處理、視覺效果及視覺和音頻元素生成，這使學生能更專注於創意上的精進。 生成式人工智慧工具對學生的創造力、批判性思維、效率及協作學習產生了顯著影響。這些工具通過節省時間，使學生能更專注於創造性任務，同時激發新的想法，並完善內容以輔助人類的創造力。生成式人工智慧工具強化了批判性思維，因為學生必須對AI生成的內容進行驗證與改進。在協作學習中，AI改善了溝通和工作流程，但有時也會抑制創造力，減少協作深度，並在工作分配上造成不平衡，因此需要謹慎管理以確保協作的有效性。
In the swiftly evolving landscape of digital media, this research examines the transformative impact of incorporating Generative AI (GenAI) tools in educational settings to prepare students for industry changes. Set against a backdrop of a media sector undergoing profound transformation, the exploratory study focuses on how GenAI technologies are integrated into new media content creation curricula, influencing students' experiential learning and creative processes. Based on David Kolb's experiential learning theory, this research emphasizes the learning of GenAI through a cycle of concrete experience, reflective observation, abstract conceptualization, and active experimentation. It also investigates how GenAI technologies shape students’ production processes and influence their creativity, critical thinking, efficiency, and collaborative learning. Recent literature underscores the potential of GenAI in revolutionizing education and content creation, enhancing active learning, creativity, and critical thinking. However, empirical data on the integration of GenAI into media educational curricula and its impacts on student learning experiences and outcomes is scarce. This case study employs a mixed-method approach to investigate two GenAI-enhanced media courses at a leading Taiwanese university. One course focuses on using GenAI for online video production and social media marketing, while the other involves leveraging GenAI for innovative idea concept development and visualization. Data collection included semester-long overt observations and in-depth interviews with 17 students. The observations took place during September 2023 to January 2022, with the in-depth interviews conducted in early 2024. The rich qualitative data were analyzed thematically The mixed method data reveals that in the concrete experience stage, students gained a strong understanding of GenAI technologies and developed practical skills through self-directed learning and hands-on projects. Reflective observation revealed that while GenAI enhanced efficiency and creativity, it also posed issues with accuracy, reliability, and ethics. Students recognized the benefits of paid tools and the importance of clear prompts for quality outputs. In the abstract conceptualization stage, they deepened their skills through Human-AI collaboration, refining ideas, and adopting cross-verification methods to ensure accuracy while addressing ethical concerns. Finally, in the active experimentation stage, students applied GenAI tools in real-world scenarios, focusing on iterative improvement and problem-solving, which solidified their understanding of AI collaboration and ethics, ultimately enhancing project quality. Regarding the influence of GenAI tools on the content creation process, these tools have significantly transformed the pre-production and post-production phases, with the post-production phase being the most impacted. In pre-production, tools like ChatGPT and MidJourney facilitated research, idea generation, and concept visualization. However, the production phase remained less influenced, as it still required considerable human input, particularly in creative decision-making and execution. Post-production, in contrast, saw extensive use of GenAI tools for automating repetitive tasks such as subtitling, visual effects and generating visual and audio elements, allowing students to focus more on creative refinement. GenAI tools significantly impact students' creativity, critical thinking, efficiency, and collaborative learning. They enhance efficiency by saving time and allowing focus on creative tasks, while also sparking new ideas and refining content to complement human creativity. 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(2021). Deepfakes: Detecting forged and synthetic media content using machine learning. Artificial 描述 碩士
國立政治大學
全球傳播與創新科技碩士學位學程
111ZM1016資料來源 http://thesis.lib.nccu.edu.tw/record/#G0111ZM1016 資料類型 thesis dc.contributor.advisor 林翠絹 zh_TW dc.contributor.advisor Lin, Trisha en_US dc.contributor.author (作者) 阮德明 zh_TW dc.contributor.author (作者) Nguyen, Duc Minh en_US dc.creator (作者) 阮德明 zh_TW dc.creator (作者) Nguyen, Duc Minh en_US dc.date (日期) 2024 en_US dc.date.accessioned 4-十月-2024 10:51:49 (UTC+8) - dc.date.available 4-十月-2024 10:51:49 (UTC+8) - dc.date.issued (上傳時間) 4-十月-2024 10:51:49 (UTC+8) - dc.identifier (其他 識別碼) G0111ZM1016 en_US dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/153917 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 全球傳播與創新科技碩士學位學程 zh_TW dc.description (描述) 111ZM1016 zh_TW dc.description.abstract (摘要) 在數位媒體快速演變的背景下，本研究探討將生成式人工智慧（GenAI）工具引入教育環境對學生適應產業變革的影響。本研究聚焦於媒體產業深刻轉型的背景下，如何將生成式人工智慧技術融入新媒體內容創作課程，並對學生的體驗式學習及創作過程產生的影響。基於David Kolb的體驗式學習理論，本研究強調通過具體經驗、反思觀察、抽象概念化及主動實驗這四個階段來學習生成式人工智慧技術。此外，研究還探討了生成式人工智慧技術如何塑造學生的製作流程，並影響其創造力、批判性思維、效率及協作學習能力。 近期文獻強調了生成式人工智慧在教育及內容創作領域具有革新潛力，能夠促進主動學習、創造力及批判性思維的提升。然而，關於生成式人工智慧融入媒體教育課程及其對學生學習經驗與結果的影響，實證數據仍然有限。本案例研究採用了混合方法，探討台灣一所頂尖大學的兩門強化生成式人工智慧媒體課程。一門課程側重於利用生成式人工智慧進行線上影片製作及社群媒體行銷，另一門則聚焦於生成式人工智慧在創新概念發想與視覺化中的應用。資料收集包括學期長度的明顯觀察以及對17位學生的深入訪談。觀察於2023年9月至2024年1月進行，深入訪談則於2024年初進行。這些豐富的質性資料通過主題分析進行處理。 混合方法的數據顯示，在具體經驗階段，學生通過自我導向學習和實際專案掌握了生成式人工智慧技術並發展了實用技能。反思觀察階段揭示了生成式人工智慧雖然提升了效率與創造力，但同時也帶來了準確性、可靠性及倫理方面的問題。學生認識到付費工具的優勢，並強調清晰提示對輸出品質的重要性。在抽象概念化階段，學生通過人機協作進一步提升了技能，通過交叉驗證來確保準確性，同時應對倫理問題。最終，在主動實驗階段，學生將生成式人工智慧工具應用於實際情境中，專注於迭代改進與問題解決，從而鞏固了他們對AI協作與倫理的理解，提升了專案的品質。 關於生成式人工智慧工具對內容創作過程的影響，這些工具顯著改變了前期製作和後期製作階段，特別是在後期製作階段的影響最大。在前期製作中，ChatGPT與MidJourney等工具有助於研究、想法生成及概念視覺化。然而，製作階段則受到的影響較小，仍需要大量的人工投入，尤其是在創意決策和執行方面。相比之下，後期製作廣泛使用了生成式人工智慧工具來自動化重複性工作，如字幕處理、視覺效果及視覺和音頻元素生成，這使學生能更專注於創意上的精進。 生成式人工智慧工具對學生的創造力、批判性思維、效率及協作學習產生了顯著影響。這些工具通過節省時間，使學生能更專注於創造性任務，同時激發新的想法，並完善內容以輔助人類的創造力。生成式人工智慧工具強化了批判性思維，因為學生必須對AI生成的內容進行驗證與改進。在協作學習中，AI改善了溝通和工作流程，但有時也會抑制創造力，減少協作深度，並在工作分配上造成不平衡，因此需要謹慎管理以確保協作的有效性。 zh_TW dc.description.abstract (摘要) In the swiftly evolving landscape of digital media, this research examines the transformative impact of incorporating Generative AI (GenAI) tools in educational settings to prepare students for industry changes. Set against a backdrop of a media sector undergoing profound transformation, the exploratory study focuses on how GenAI technologies are integrated into new media content creation curricula, influencing students' experiential learning and creative processes. Based on David Kolb's experiential learning theory, this research emphasizes the learning of GenAI through a cycle of concrete experience, reflective observation, abstract conceptualization, and active experimentation. It also investigates how GenAI technologies shape students’ production processes and influence their creativity, critical thinking, efficiency, and collaborative learning. Recent literature underscores the potential of GenAI in revolutionizing education and content creation, enhancing active learning, creativity, and critical thinking. However, empirical data on the integration of GenAI into media educational curricula and its impacts on student learning experiences and outcomes is scarce. This case study employs a mixed-method approach to investigate two GenAI-enhanced media courses at a leading Taiwanese university. One course focuses on using GenAI for online video production and social media marketing, while the other involves leveraging GenAI for innovative idea concept development and visualization. Data collection included semester-long overt observations and in-depth interviews with 17 students. The observations took place during September 2023 to January 2022, with the in-depth interviews conducted in early 2024. The rich qualitative data were analyzed thematically The mixed method data reveals that in the concrete experience stage, students gained a strong understanding of GenAI technologies and developed practical skills through self-directed learning and hands-on projects. Reflective observation revealed that while GenAI enhanced efficiency and creativity, it also posed issues with accuracy, reliability, and ethics. Students recognized the benefits of paid tools and the importance of clear prompts for quality outputs. In the abstract conceptualization stage, they deepened their skills through Human-AI collaboration, refining ideas, and adopting cross-verification methods to ensure accuracy while addressing ethical concerns. Finally, in the active experimentation stage, students applied GenAI tools in real-world scenarios, focusing on iterative improvement and problem-solving, which solidified their understanding of AI collaboration and ethics, ultimately enhancing project quality. Regarding the influence of GenAI tools on the content creation process, these tools have significantly transformed the pre-production and post-production phases, with the post-production phase being the most impacted. In pre-production, tools like ChatGPT and MidJourney facilitated research, idea generation, and concept visualization. However, the production phase remained less influenced, as it still required considerable human input, particularly in creative decision-making and execution. Post-production, in contrast, saw extensive use of GenAI tools for automating repetitive tasks such as subtitling, visual effects and generating visual and audio elements, allowing students to focus more on creative refinement. GenAI tools significantly impact students' creativity, critical thinking, efficiency, and collaborative learning. They enhance efficiency by saving time and allowing focus on creative tasks, while also sparking new ideas and refining content to complement human creativity. GenAI strengthens critical thinking as students must verify and refine AI-generated content. In collaborative learning, AI improves communication and workflow but can sometimes stifle creativity, reduce collaborative depth and create imbalances in teamwork, requiring careful management to ensure effective collaboration. en_US dc.description.tableofcontents Table of Contents 1. Introduction 1 2. Literature Review 3 2.1 GenAI in Education 3 2.2 Experiential Learning in GenAI Education 5 2.2.1 Stage 1: Concrete Experience in GenAI Learning 6 2.2.2 Stage 2: Reflective Observation in GenAI Learning 7 2.2.3 Stage 3: Abstract Conceptualization in GenAI Learning 8 2.2.4 Stage 4: Active Experimentation in GenAI Learning 9 2.3 GenAI in Content Creation 10 2.3.1 GenAI in Content Production 12 2.4 The proposed conceptual framework 14 3. Methodology 18 3.1 Overt Observation 20 3.1.1 Data Analysis 21 3.2 Semi-structure Interview 21 3.2.1 List of Interview Participants 22 3.2.2 Code scheme & Analysis 24 4. Results 27 4.1 Development of GenAI Knowledge and Skills Through Experiential Learning Stages 28 4.1.1 Stage 1: Concrete Experience 29 4.1.2 Stage 2: Reflective Observation 32 4.1.3 Stage 3: Abstract Conceptualization 42 4.1.4 Stage 4: Active Experimentation 51 4.2 Influence of GenAI Tools on Content Creation Processes 55 4.2.1 Pre-Production 56 4.2.2 Production 64 4.2.3 Post-production 70 4.3 Utilization of GenAI tools and influences on creativity, critical thinking, efficiency, and collaborative learning 77 4.3.1 Creativity 78 4.3.2 Critical Thinking 80 4.3.3 Efficiency 82 4.3.4 Collaborative Learninng 84 5. Discussion and Conclusion 88 5.1 Discussion 89 5.2 Implications for Media Education 96 5.2.1 Students 96 5.2.2 Media Educator 98 5.2 Limitation and Future Study 100 6. Reference 104 7. Appendix 113 zh_TW dc.format.extent 2842360 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0111ZM1016 en_US dc.subject (關鍵詞) 生成式人工智慧 zh_TW dc.subject (關鍵詞) 生成式人工智慧於媒體教育 zh_TW dc.subject (關鍵詞) 生成式人工智慧於內容創作 zh_TW dc.subject (關鍵詞) 生成式人工智慧的限制 zh_TW dc.subject (關鍵詞) 體驗式學習 zh_TW dc.subject (關鍵詞) 創造力 zh_TW dc.subject (關鍵詞) 批判性思維 zh_TW dc.subject (關鍵詞) 協作學習 zh_TW dc.subject (關鍵詞) GenAI en_US dc.subject (關鍵詞) GenAI in media education en_US dc.subject (關鍵詞) GenAI in content creation en_US dc.subject (關鍵詞) GenAI limitations en_US dc.subject (關鍵詞) experiential learning en_US dc.subject (關鍵詞) creativity en_US dc.subject (關鍵詞) critical thinking en_US dc.subject (關鍵詞) collaborative learning en_US dc.title (題名) 整合生成式人工智慧與新媒體內容創作教育：混合法質性研究分析 zh_TW dc.title (題名) Integrating GenAI into new media content creation education: A mixed-method qualitative research en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Abdelghani, R., Sauzéon, H., & Oudeyer, P.-Y. 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