看到大学生被AI检测折磨，我有话想说

Group 1 - The article discusses the implementation of AIGC detection tools in universities to combat academic misconduct, particularly in student thesis submissions [4][9][10] - Several universities have set specific thresholds for AIGC detection, such as 20% and 15%, which can impact students' graduation eligibility [10][6] - The author expresses concern over the effectiveness and reliability of AIGC detection methods, arguing that they may misjudge human-written content as AI-generated [20][22][62] Group 2 - The article critiques the underlying algorithms of AIGC detection tools, categorizing them into three main types: perplexity and entropy analysis, machine learning classifiers, and syntactic and stylistic feature modeling [26][35][39] - The author highlights the absurdity of using AI to judge AI-generated content, emphasizing that the detection systems often fail to account for the nuances of human writing [21][34][66] - There is a significant concern that educational institutions may not fully understand the limitations of these detection tools, leading to unfair consequences for students [55][56][62] Group 3 - The article argues that the reliance on AIGC detection tools reflects a broader trust crisis in the educational system regarding the use of AI [64][67] - The author believes that the current approach to AIGC detection prioritizes algorithmic judgment over human effort and creativity, which could stifle genuine academic expression [70][73] - The piece concludes with a warning about the potential future where individuals may feel compelled to prove their originality through surveillance rather than trust [71][72]