看到现在的毕业生被AIGC查重折磨，我有话想说。

Core Viewpoint - The article expresses concern over the implementation of AIGC detection tools in universities to combat academic dishonesty, highlighting the potential negative impact on students' graduation prospects due to misjudgments by these AI systems [1][7][40]. Group 1: AIGC Detection Implementation - Several universities have started using AIGC detection tools, with specific thresholds set for AI-generated content in student theses, such as 20% and 15% [1]. - Students have expressed frustration and confusion regarding the accuracy of these AIGC detection tools, with many reporting high AI detection rates for their original work [4][6]. Group 2: Limitations of AIGC Detection Tools - The underlying principle of AIGC detection is flawed, as it relies on AI to judge whether a text is AI-generated, leading to absurd outcomes where human-written content is misclassified as AI-generated [10][38]. - The detection methods, including perplexity and entropy analysis, machine learning classifiers, and syntactic and stylistic feature modeling, have inherent issues that can result in false positives for students' work [14][20][24]. Group 3: Implications for Education - The reliance on AIGC detection tools reflects a misunderstanding of AI's capabilities and a lack of trust in students' efforts, reducing their work to mere numerical scores [36][40]. - The article argues that the current approach to AIGC detection could lead to a chilling effect on students' willingness to write, as they may fear being misjudged by AI systems [41].