| dc.contributor.advisor | 陳嬿如<br>翁嘉祥 | zh_TW |
| dc.contributor.advisor | Chen, Yenn-Ru<br>Weng, Chia-Hsiang | en_US |
| dc.contributor.author (Authors) | 王韋翔 | zh_TW |
| dc.contributor.author (Authors) | Wang, Wei-Hsiang | en_US |
| dc.creator (作者) | 王韋翔 | zh_TW |
| dc.creator (作者) | Wang, Wei-Hsiang | en_US |
| dc.date (日期) | 2025 | en_US |
| dc.date.accessioned | 4-Aug-2025 13:03:20 (UTC+8) | - |
| dc.date.available | 4-Aug-2025 13:03:20 (UTC+8) | - |
| dc.date.issued (上傳時間) | 4-Aug-2025 13:03:20 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0112932102 | en_US |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/158339 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 經營管理碩士學程(EMBA) | zh_TW |
| dc.description (描述) | 112932102 | zh_TW |
| dc.description.abstract (摘要) | 本研究旨在探討生成式人工智慧（AI）工具對學術研究與高等教育的深遠影響。為此，本研究採用雙重研究方法：首先，運用AI搜尋引擎（Perplexity）生成一份關於蘇格蘭威士忌產業ESG（環境、社會與治理）現況的學術報告，並透過AI寫作輔助工具（Thesify）評估其產出品質；其次，深度訪談三位國立大學商管領域的教授，以了解其在研究與教學實踐中應用AI的真實經驗、挑戰與觀點。研究發現，AI工具在整合事實性資料與建立報告架構上表現高效，但其生成內容缺乏可供辯論的深入分析與原創性觀點，且部分論述未能提供充分證據支持，凸顯了當前AI在高品質學術寫作中的內在限制。訪談分析則顯示，教授們普遍肯定AI在提升程式撰寫與資料處理效率上的價值，但對其在文獻回顧中的可靠性持保留態度，並指出AI引發了教學模式的根本性變革，教學重點從知識傳遞轉向培養學生的批判性思維與判讀AI產出的能力。綜合而言，本研究結論指出，AI正成為學術研究不可或缺的輔助工具，但其角色應是增強而非取代人類的深度分析與原創思考。同時，高等教育體系與學術社群必須積極應對AI帶來的挑戰，重新定義學術誠信規範，並調整教學策略以培養適應人機協作時代的核心素養。 | zh_TW |
| dc.description.abstract (摘要) | This study explores the profound impact of generative artificial intelligence (AI) tools on academic research and higher education. A dual-methodology approach was employed: first, an AI search engine (Perplexity) was utilized to generate a scholarly report on the Environmental, Social, and Governance (ESG) status of the Scotch whisky industry, the quality of which was then assessed using an AI writing assistant (Thesify). Second, in-depth interviews were conducted with three university professors in the business and management fields to understand their real-world experiences, challenges, and perspectives on applying AI in their research and teaching practices. The findings reveal that while AI tools are highly efficient in integrating factual data and structuring reports, the generated content lacks in-depth, debatable analysis and original perspectives, with some claims unsupported by sufficient evidence. This highlights the intrinsic limitations of current AI in high-quality scholarly writing. The interview analysis shows that professors widely acknowledge AI's value in boosting efficiency for tasks like programming and data processing but express reservations about its reliability for literature reviews. They also note that AI is catalyzing a fundamental shift in pedagogical models, moving the focus from knowledge transmission to cultivating students' critical thinking and their ability to evaluate AI-generated content. In conclusion, this study suggests that while AI is becoming an indispensable auxiliary tool for academic research, its role is to augment, not replace, human researchers' deep analysis and original thought. Concurrently, higher education systems and the academic community must proactively address these challenges by redefining academic integrity standards and adapting teaching strategies to foster the core competencies required for an era of human-machine collaboration. | en_US |
| dc.description.tableofcontents | 第一章 緒論 7
第一節 研究背景 7
第二節 研究動機 9
第三節 研究問題 11
第二章 文獻探討 12
第一節 人工智慧技術發展 12
第二節 大型語言模型（LLM）介紹 16
第三節 人工智慧工具介紹 20
第三章 AI 研究 23
第一節 研究方法 23
第二節 研究過程 25
第四章 訪談分析與討論 35
第一節 訪談分析 35
第二節 訪談討論 39
第五章 結論與建議 41
第一節 研究摘要 41
第二節 研究限制與建議 42
參考文獻及附錄 44 | zh_TW |
| dc.format.extent | 3345621 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0112932102 | en_US |
| dc.subject (關鍵詞) | 生成式人工智慧 | zh_TW |
| dc.subject (關鍵詞) | 學術研究 | zh_TW |
| dc.subject (關鍵詞) | 高等教育 | zh_TW |
| dc.subject (關鍵詞) | 人機協作 | zh_TW |
| dc.subject (關鍵詞) | ESG | zh_TW |
| dc.subject (關鍵詞) | Generative AI | en_US |
| dc.subject (關鍵詞) | Academic Research | en_US |
| dc.subject (關鍵詞) | Higher Education | en_US |
| dc.subject (關鍵詞) | Human-Machine Collaboration | en_US |
| dc.subject (關鍵詞) | ESG | en_US |
| dc.title (題名) | AI工具如何改變教學與學術研究 - 以研究威士忌產業的ESG現狀為例 | zh_TW |
| dc.title (題名) | How AI Tools Are Changing Teaching and Academic Research - Using the ESG Status of the Whisky Industry as an Example | en_US |
| dc.type (資料類型) | thesis | en_US |
| dc.relation.reference (參考文獻) | 1 https://educationaltechnologyjournal.springeropen.com/articles/10.1186/s41239-023-00408-3
2 https://education.illinois.edu/about/news-events/news/article/2024/10/24/ai-in-schools--pros-and-cons
3 https://www.whitehouse.gov/presidential-actions/2025/04/advancing-artificial-intelligence-education-for-american-youth
4 https://www.whitehouse.gov/fact-sheets/2025/04/fact-sheet-president-donald-j-trump-advances-ai-education-for-american-youth
5 https://teaching.cornell.edu/generative-artificial-intelligence/ethical-ai-teaching-and-learning
6 https://www.cmu.edu/news/stories/archives/2025/april/cmu-examines-how-ai-tools-are-reshaping-learning-for-both-teachers-and-students
7 Wikipedia: History of Artificial Intelligence ( https://en.wikipedia.org/wiki/History_of_artificial_intelligence )
8 Tableau: What is the history of artificial intelligence ( https://www.tableau.com/data-insights/ai/history )
9 Grammarly: AI History Key Milestones That Shaped Artificial Intelligence ( https://www.grammarly.com/blog/ai/ai-history/ )
10 Google Cloud: What is Machine Learning? ( https://cloud.google.com/learn/artificial-intelligence-vs-machine-learning )
11 IBM: What is Machine Learning (ML)? ( https://www.ibm.com/design/ai/basics/ml/ )
12 SAS: Types of machine learning algorithms ( https://www.sas.com/en_us/insights/analytics/machine-learning.html )
13 Google Cloud: What is Deep Learning? ( https://cloud.google.com/discover/what-is-deep-learning )
14 Qualcomm: The rise of generative AI ( https://www.qualcomm.com/news/onq/2024/02/the-rise-of-generative-ai-timeline-of-breakthrough-innovations )
15 Deep Dive into LLM like ChatGPT by Andrej Karpathy ( https://www.youtube.com/watch?v=7xTGNNLPyMI )
16 https://openai.com/index/learning-to-reason-with-llms/
17 https://developers.google.com/machine-learning/resources/intro-llms
18 https://aws.amazon.com/what-is/large-language-model/
19 https://learn.microsoft.com/en-us/training/modules/introduction-large-language-models/
20 https://arxiv.org/html/2403.04642v1
21 https://arxiv.org/abs/2503.23674
22 https://arxiv.org/pdf/2503.23674
23 https://theconversation.com/chatgpt-just-passed-the-turing-test-but-that-doesnt-mean-ai-is-now-as-smart-as-humans-253946
24 https://www.nobelprize.org/all-nobel-prizes-2024/ | zh_TW |
