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| Title: | 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 |
| Authors: | 阮德明
Nguyen, Duc Minh |
| Contributors: | 林翠絹
Lin, Trisha
阮德明
Nguyen, Duc Minh |
| Keywords: | 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 |
| Date: | 2024 |
| Issue Date: | 2024-10-04 10:51:49 (UTC+8) |
| Abstract: | 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. 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. |
| Reference: | Abdelghani, R., Sauzéon, H., & Oudeyer, P.-Y. (2023). GenAI in the Classroom: Can Students Remain Active Learners? (Version 2). arXiv. https://doi.org/10.48550/ARXIV.2310.03192
Akella, D. (2010). Learning together: Kolb's experiential theory and its application. Journal of Management & Organization, 16(1), 100-112.
AlAli, R., Wardat, Y., Al-Saud, K., & Alhayek, K. A. (2024). GenAI in Education: Best Practices for Successful Implementation. In International Journal of Religion (Vol. 5, Issue 9, pp. 1016–1025). Wise Academia Research Solutions.
Analytica, O. (2023). GenAI will transform workplace tasks across industries. Expert Briefings.
Anscomb, C. (2024). Creating Images with GenAI. In Artificial Intelligence, Co-Creation and Creativity (pp. 77–88). Routledge. https://doi.org/10.4324/9781003453901-9
Arputha, Sahaya, Raj, J. (2024). (3) GenAI and the Future of Education and Research. Social Science Research Network, doi: 10.2139/ssrn.4686338
Ayob, A., Hussain, A., Mustafa, M. M., & Shaarani, M. F. A. S. (2011). Nurturing creativity and innovative thinking through experiential learning. Procedia-Social and Behavioral Sciences, 18, 247-254.
Babcock, J., & Bali, R. (2021). GenAI with Python and TensorFlow 2: Create images, text, and music with VAEs, GANs, LSTMs, Transformer models. Packt Publishing Ltd.
Bahroun, Z., Anane, C., Ahmed, V., & Zacca, A. (2023). Transforming education: A Comprehensive review of generative artificial intelligence in educational settings through bibliometric and content analysis. Sustainability, 15(17), 12983.
Bailey, D., Southam, A., & Costley, J. (2021). Digital storytelling with chatbots: Mapping L2 participation and perception patterns. Interactive Technology and Smart Education, 18(1), 85-103. https://doi.org/10.1108/ITSE-08-2020-0170 .
Behboudi, A., Adefisayo, A. O., & Arastoopour Irgens, G. (2024). Promoting Critical Consciousness in Students through Artifacts Co-creation with GAI. In Proceedings of the Symposium on Learning, Design and Technology. LDT ’24: Symposium on Learning, Design and Technology. ACM. https://doi.org/10.1145/3663433.3663435
Bell, S. (2010). Project-based learning for the 21st century: Skills for the future. The Clearing House: A Journal of Educational Strategies, Issues and Ideas, 83(2), 39-43.
Boden, M. A. (2016). AI: Its nature and future. Oxford University Press.
Cartwright, S. (2012). Pre-production planning for video, film, and multimedia. Routledge.
Chandrasekera, T., Hosseini, Z., & Perera, U. (2024). Can artificial intelligence support creativity in early design processes? In International Journal of Architectural Computing. SAGE Publications. https://doi.org/10.1177/14780771241254637
Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20(1), 22. https://doi.org/10.1186/s41239-023-00392-8
Datta, A., & Goswami, R. (2021). The film industry leaps into artificial intelligence: Scope and challenges by the filmmakers. In Rising Threats in Expert Applications and Solutions: Proceedings of FICR-TEAS 2020 (pp. 665-670). Springer Singapore.
Dehouche, N., & Dehouche, K. (2023). What’s in a text-to-image prompt: The potential of Stable Diffusion in visual arts education. https://doi.org/10.48550/arXiv.2301.01902
Dellarocas, C. (2023, December 8). How GenAI Could Accelerate Employee Learning and Development. Harvard Business Review. https://hbr.org/2023/12/how-genai-could-accelerate- employee-learning-and-development
Dickey, E., & Bejarano, A. (2023). A model for integrating GenAI into course content development. arXiv preprint arXiv:2308.12276.
Diefenbach, D., & Slatton, A. (2019). Video production techniques: Theory and practice from concept to screen. Routledge.
Elhoseny, H., Riad, A. M., & Hassanien, A. E. (2018). A framework for big data analysis in smart cities. Advances in Intelligent Systems and Computing, 723, 405–414. https://doi.org/10.1007/978-3-319-74690-6_40
Ertmer, P. A., & Newby, T. J. (2013). Behaviorism, cognitivism, constructivism: Comparing critical features from an instructional design perspective. Performance Improvement Quarterly, 26(2), 43-71.
Essel, H. B., Vlachopoulos, D., Tachie-Menson, A., Johnson, E. E., & Baah, P. K. (2022). The impact of a virtual teaching assistant (chatbot) on students’ learning in Ghanaian higher education. International Journal of Educational Technology in Higher Education, 19, 57. https://doi.org/10.1186/s41239-022-00362-6
Eunwoo, Yoo., Louella, Ursua., Romart, Clark., Jeongmin, Seok., Jaehyun, Jeon., Hong, Bin, Kim. (2019). The effect of incorporating covert observation into established overt observation- based hand hygiene promotion programs. American Journal of Infection Control, doi: 10.1016/J.AJIC.2018.11.002
Farid, S. (2022). Observation. In Principles of Social Research Methodology (pp. 365–375). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-5441-2_25
Fook, J., & Gardner, F. (2007). Practising Critical Reflection: A Resource Handbook. Open University Press.
G.Pushpalatha. (2022). Experiential Learning in English Language Teaching: A study. International journal of engineering technology and management sciences, doi: 10.46647/ijetms.2022.v06i06.069
Garcia, M. B. (2024). The Paradox of Artificial Creativity: Challenges and Opportunities of GenAI Artistry. Creativity Research Journal, 1-14.
Gimpel, H., Hall, K., Decker, S., Eymann, T., Lämmermann, L., Mädche, A., ... & Vandrik, S. (2023). Unlocking the power of GenAI models and systems such as GPT-4 and ChatGPT for higher education: A guide for students and lecturers (No. 02-2023). Hohenheim Discussion Papers in Business, Economics and Social Sciences.
Grassini, S. (2023, July 7). Shaping the Future of Education: Exploring the Potential and Consequences of AI and ChatGPT in Educational Settings. Education Sciences, 13(7), 692. https://doi.org/10.3390/educsci13070692
Greckhamer, T., & Cilesiz, S. (2022). Qualitative Research: Foundations, Approaches, and Practices. In Oxford Research Encyclopedia of Business and Management.
Greenhalgh, C. (2018). Cinematography: Practice as Research, Research into Practice. Screen Production Research: Creative Practice as a Mode of Enquiry, 143-159.
Harrer, S. (2023). Attention is not all you need: The complicated case of ethically using large language models in healthcare and medicine. eBioMedicine, 90, 104512. 17
Hattie, J., & Timperley, H. (2007). "The power of feedback." Review of Educational Research, 77(1), 81-112.
He, J., Houde, S., Gonzalez, G. E., Silva Moran, D. A., Ross, S. I., Muller, M., & Weisz, J. D. (2024, June). AI and the Future of Collaborative Work: Group Ideation with an LLM in a Virtual Canvas. In Proceedings of the 3rd Annual Meeting of the Symposium on Human-Computer Interaction for Work (pp. 1-14).
Hoda, J. A. (2023). Creativity. In The Art of Investigation Revisited (pp. 71–82). CRC Press. https://doi.org/10.4324/9781003252283-8
Horbatso, M., & Gutsuliak, A. (2024). MANAGEMENT CHALLENGES AND FUTURE DIRECTIONS OF USING GENERATIVE ARTIFICIAL INTELLIGENCE (GEN AI) IN TEAMS. Publishing House “Baltija Publishing”.
Häufglöckner, L. (2023). Self-supervised language models in journalism: quality perception of GPT-3-written articles (Doctoral dissertation, Hochschule Darmstadt).
Im, D., Pyo, J., Lee, H., Jung, H., & Ock, M. (2023). Qualitative Research in Healthcare: Data Analysis. Journal of Preventive Medicine and Public Health, 56(2), 100.
Jeffrey, T. (2020). Understanding college student perceptions of artificial intelligence. Systemics, Cybernetics and Informatics, 18(2), 8-13.
Jiang-Wei Wan, J.-W. W. (2024). The Application Strategy of Intelligent Computer Multimedia Technology in Film and Television Post Production. In 電腦學刊 (Vol. 35, Issue 2, pp. 183–197). Angle Publishing Co., Ltd. https://doi.org/10.53106/199115992024043502012
Jonassen, D. H. (1999). Designing constructivist learning environments. In C. M. Reigeluth (Ed.), Instructional-design theories and models: A new paradigm of instructional theory, Volume II (pp. 215-239). Lawrence Erlbaum Associates.
Kerrigan, S. (2016). The spectator in the film-maker: re-framing filmology through creative film-making practices. Journal of Media Practice, 17(2-3), 186-198.
Kerrigan, S. (2019). Innovation in the Arts. The Oxford handbook of group creativity and innovation, 335.
Ko, D., Hassan, A., Suk, J., & Choi, J. (2020). Korean font synthesis with GANs. International Journal of Computer Theory and Engineering, 12(4), 92-96.
Kolb, A. Y., & Kolb, D. A. (2005). Learning styles and learning spaces: Enhancing experiential learning in higher education. Academy of Management Learning & Education, 4(2), 193-212.
Kolb, D. A. (1984). Experiential Learning: Experience as the Source of Learning and Development. Prentice-Hall.
Kolb, D. A. (2014). Experiential learning: Experience as the source of learning and development. FT press.
Kubik, V., Frey, I. G., & Gaschler, R. (2021). PLAT 20 (3) 2021: Promoting self-regulated learning: training, feedback, and addressing teachers’ misconceptions. Psychology Learning & Teaching, 20(3), 306-323.
Kumar, A. H. S. (2023). Analysis of ChatGPT tool to assess the potential of its utility for academic writing in biomedical domain. BEMS Reports, 9(1), 24-30. https://doi.org/10.5530/bems.9.1.5
Kumar, K., Ford, T., Mao, Y., Zhu, L., & Przysucha, M. (2019). U.S. Patent No. 10,237,227. Washington, DC: U.S. Patent and Trademark Office.
Kumar, L., Singh, D. K., & Ansari, M. A. (2024). Role of Video Content Generation in Education Systems Using GenAI. In Integrating GenAI in Education to Achieve Sustainable Development Goals (pp. 341–355). IGI Global. https://doi.org/10.4018/979-8-3693-2440-0.ch019
Kurt, S. (2020). Kolb’s experiential learning theory & learning styles. Educational Technology, December, 28, 2020.
Li, N., Zhou, H., & Mikel-Hong, K. (2024). GenAI Enhances Team Performance and Reduces Need for Traditional Teams. arXiv preprint arXiv:2405.17924.
Lu, H. S., & Smiles, R. (2022). The role of collaborative learning in online education. International Journal of Economics, Business and Management Research, 6(06), 125-137.
Lubowitz, J. H. (2023). ChatGPT, an artificial intelligence chatbot, is impacting medical literature. Arthroscopy, 39(5), 1121-1122. https://doi.org/10.1016/j.arthro.2023.01.015
Lyu, Y., Zhang, H., Niu, S., & Cai, J. (2024). A Preliminary Exploration of YouTubers’ Use of Generative-AI in Content Creation. In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems. CHI ’24: CHI Conference on Human Factors in Computing Systems. ACM. https://doi.org/10.1145/3613905.3651057
Maddock, D. (2021). What Is Cinematography in the Age of Virtual Film Production? Posing a New Definition for the Practice of Cinematography. Journal of Film and Video, 73(4), 44-58.
Meskys, E., Kalpokiene, J., Jurcys, P., & Liaudanskas, A. (2020). Regulating deep fakes: legal and ethical considerations. Journal of Intellectual Property Law & Practice, 15(1), 24-31.
Ming, X. M. (2022). Post-production of digital film and television with development of virtual reality image technology-advance research analysis. Int. J. Mech. Eng, 7.
Mizumoto, A., & Eguchi, M. (2023). Exploring the potential of using an AI language model for automoated essay scoring. https://dx.doi.org/10.2139/ssrn.4373111
Morales-Chan, M., Amado-Salvatierra, H. R., & Hernandez-Rizzardini, R. (2024). Workshop: Educational Innovation Through Generative Artificial Intelligence: Tools, Opportunities, and Challenges. In 2024 IEEE World Engineering Education Conference (EDUNINE). 2024 IEEE World Engineering Education Conference (EDUNINE). IEEE.
Nixon, N., Lin, Y., & Snow, L. (2024). Catalyzing Equity in STEM Teams: Harnessing GenAI for Inclusion and Diversity. Policy Insights from the Behavioral and Brain Sciences, 11(1), 85-92.
Peres, R., Shreier, M., Schweidel, D., & Sorescu, A. (2023). On ChatGPT and beyond: How generative artificial intelligence may affect research, teaching, and practice. International Journal of Research in Marketing. Advance online publication. https://doi.org/10.1016/j.ijresmar.2023.03.001
Pesovski, I., Santos, R., Henriques, R., & Trajkovik, V. (2024). GenAI for Customizable Learning Experiences. In Sustainability (Vol. 16, Issue 7, p. 3034). MDPI AG.
Prince, M. (2004). Does active learning work? A review of the research. Journal of Engineering Education, 93(3), 223-231.
Rahaman, M. S., Ahsan, M. T., Anjum, N., Terano, H. J. R., & Rahman, M. M. (2023). From ChatGPT-3 to GPT-4: a significant advancement in ai-driven NLP tools. Journal of Engineering and Emerging Technologies, 2(1), 1-11.
Rathod, R. (2024). Renaissance on GenAI. In International Journal for Research in Applied Science and Engineering Technology (Vol. 12, Issue 6, pp. 1354–1359). International Journal for Research in Applied Science and Engineering Technology (IJRASET). https://doi.org/10.22214/ijraset.2024.63324
Rivero, D., Ramírez-Morales, I., Fernandez-Blanco, E., Ezquerra, N., & Pazos, A. (2020). Classical music prediction and composition by means of variational autoencoders. Applied Sciences, 10(9), 3053.
Ruiz-Rojas, L. I., Salvador-Ullauri, L., & Acosta-Vargas, P. (2024). Collaborative Working and Critical Thinking: Adoption of Generative Artificial Intelligence Tools in Higher Education. In Sustainability (Vol. 16, Issue 13, p. 5367). MDPI AG.
Sandhaus, H., Parreira, M. T., & Ju, W. (2024). Student Reflections on Self-Initiated GenAI Use in HCI Education. arXiv preprint arXiv:2405.01467.
Salinas-Navarro, D. E., Vilalta-Perdomo, E., Michel-Villarreal, R., & Montesinos, L. (2024). Using Generative Artificial Intelligence Tools to Explain and Enhance Experiential Learning for Authentic Assessment. Education Sciences, 14(1), 83. https://doi.org/10.3390/educsci14010083
Sandhu, R., Channi, H. K., Ghai, D., Cheema, G. S., & Kaur, M. (2024). An Introduction to GenAI Tools for Education 2030. In Integrating GenAI in Education to Achieve Sustainable Development Goals (pp. 1–28). IGI Global.
Sarkar, A., & Cooper, S. (2021, August). Generating and blending game levels via quality- diversity in the latent space of a variational autoencoder. In Proceedings of the 16th International Conference on the Foundations of Digital Games (pp. 1-11).
Satapathy, S. K., & Parmar, D. (2023). Video Generation by Summarizing the Generated Transcript. In 2023 3rd Asian Conference on Innovation in Technology (ASIANCON). 2023 3rd Asian Conference on Innovation in Technology (ASIANCON). IEEE. https://doi.org/10.1109/asiancon58793.2023.10270304
Savery, J. R. (2006). Overview of problem-based learning: Definitions and distinctions. Interdisciplinary Journal of Problem-based Learning, 1(1), 9-20.
Schön, D. A. (1983). The Reflective Practitioner: How Professionals Think in Action. Basic Books.
Seli, P., Ph. D. (2024). Beyond the Brush: Human Versus AI Creativity in the Realm of Generative Art. Center for Open Science. https://doi.org/10.31234/osf.io/vgzhj
Sencer, Y., & Laura, A. L. (2023). GENAI: CHALLENGES TO ACADEMIC QUALITY. In Herald of the Kazakh-British technical university (Vol. 20, Issue 3, pp. 17–25). Kazakh-British Technical University.
Shahriar, S. (2022). GAN computers generate arts? a survey on visual arts, music, and literary text generation using generative adversarial network. Displays, 73, 102237.
Shuck, J. P. (2020). U.S. Patent No. 10,861,495. Washington, DC: U.S. Patent and Trademark Office.
Slight, Sarah & Cresswell, Kathrin & Robertson, Ann & Huby, Guro & Avery, Tony & Sheikh, Sir Aziz. (2011). The Case Study Approach. BMC medical research methodology. 11. 100. 10.1186/1471-2288-11-100.
Soe, T. H. (2021). AI video editing tools. What editors want and how far is AI from delivering?. arXiv preprint arXiv:2109.07809.
Strudwick, R. (2019). Tensions in ethnographic observation: overt or covert?. Journal of Organizational Ethnography, 8(2), 185-195.
Stuebs, M., Miller, W., & Mintz, S. (2021). Advancing practical wisdom in ethics as an essential soft skill developed through experiential learning. In Research on professional responsibility and ethics in accounting (pp. 37-53). Emerald Publishing Limited.
Sui, C. J., Yen, M. H., & Chang, C. Y. (2023). Investigating Effects of Perceived Technology- enhanced Environment on Self-regulated Learning: Beyond P-values. arXiv preprint arXiv:2306.02392.
Tatar, K., Bisig, D., & Pasquier, P. (2021). Latent timbre synthesis: Audio-based variational auto-encoders for music composition and sound design applications. Neural Computing and Applications, 33, 67-84.
Tatar, K., Ericson, P., Cotton, K., del Prado, P. T. N., Batlle-Roca, R., Cabrero-Daniel, B., ... & Hussain, J. (2023). A Shift In Artistic Practices through Artificial Intelligence. arXiv preprint arXiv:2306.10054.
Thomas, J. W. (2000). A review of research on project-based learning. The Autodesk Foundation.
Totlani, K. (2023). The Evolution of GenAI: Implications for the Media and Film Industry. In International Journal for Research in Applied Science and Engineering Technology (Vol. 11, Issue 10, pp. 973–980). International Journal for Research in Applied Science and Engineering Technology (IJRASET). https://doi.org/10.22214/ijraset.2023.56140
Umarji, N. (2024, May 15). GenAI Studio: Reinventing Content Creation in Media & Entertainment. https://mactores.com/blog/genai-studio-reinventing-content-creation-in-media-entertainment
Uzayr, S. bin. (2022). Prototyping. In Mastering Sketch (pp. 129–154). CRC Press. https://doi.org/10.1201/9781003261575-6
Vincent-Lancrin, S. (2024). Critical thinking. In Elgar Encyclopedia of Interdisciplinarity and Transdisciplinarity (pp. 124–128). Edward Elgar Publishing.
Wang, J., Huang, X., Yang, Z., & Zhao, W. (2023, November). Construction of Artificial Intelligence Generated Content in Digital Film Production. In 2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) (pp. 2621-2628). IEEE.
Yan, W., Zhang, Y., Abbeel, P., & Srinivas, A. (2021). Videogpt: Video generation using vq- vae and transformers. arXiv preprint arXiv:2104.10157.
Yanazume, T. (2006). U.S. Patent Application No. 11/330,216.
Yenduri, G., Srivastava, G., Maddikunta, P. K. R., Jhaveri, R. H., Wang, W., Vasilakos, A. V., & Gadekallu, T. R. (2023). Generative Pre-trained Transformer: A Comprehensive Review on Enabling Technologies, Potential Applications, Emerging Challenges, and Future Directions. arXiv preprint arXiv:2305.10435.
Zimmerman, B. J. (2002). "Becoming a self-regulated learner: An overview." Theory into Practice, 41(2), 64-70.
Zobaed, S., Rabby, F., Hossain, I., Hossain, E., Hasan, S., Karim, A., & Md Hasib, K. (2021). Deepfakes: Detecting forged and synthetic media content using machine learning. Artificial |
| Description: | 碩士
國立政治大學
全球傳播與創新科技碩士學位學程
111ZM1016 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0111ZM1016 |
| Data Type: | thesis |
| Appears in Collections: | [全球傳播與創新科技碩士學位學程] 學位論文
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| 101601.pdf | | 2775Kb | Adobe PDF | 5 | View/Open |
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