生成式AI研究應用之學術倫理挑戰

Challenges of Academic Ethics Arising from Research Applications of Generative AI

生成式人工智慧的普及，為學術研究與教育體系帶來前所未有的倫理挑戰。本文首先介紹其應用樣態及所涉潛在風險，包括專業權威內容的農場化、缺乏內化與批判性思考的產出，以及去脈絡化所造成的知識誤導問題。在學術倫理分析上，區分狹義與廣義兩個層次探討。狹義層面聚焦於造假、變造、抄襲樣態的重構、敏感資料治理與評量機制的公正性；廣義層面則關注研究者認知能力退化、語言平權與文化同質化的雙面效應，及學術實踐去人性化的風險。最後，本文提出結合規範、制度與價值之三層因應策略：在規範面確立「人類最終責任」與使用揭露原則、在制度面由成果檢核轉向研究歷程評量、在價值面強調以人為本。

The popularity of Generative Artificial Intelligence has brought unprecedented ethical challenges to academic research and the educational system. This paper first introduces its application patterns and the potential risks involved, including the“content farming”of professional authority, outputs lacking internalization and critical thinking, and the problem of misleading knowledge caused by decontextualization. In analyzing academic ethics, this study examines two levels: the narrow and the broad senses. The narrow sense focuses on the reconstruction of fabrication, falsification, and plagiarism; the governance of sensitive data; and the fairness of assessment mechanisms. The broad sense concerns the degradation of researchers’cognitive abilities, the double-edged effect of linguistic equity and cultural homogenization, and the risk of dehumanizing academic practice. Finally, this paper proposes a three-tier response strategy combining norms, institutions, and values: Normative level: Establish the principle of“ultimate human responsibility”and disclosure of use. Institutional level: Shift from outcome verification to research process assessment. Value level: Emphasize a human-centered approach.