《滕王阁序》AI生成率竟达100%，高校AI检测逼疯师生

Core Viewpoint - The article discusses the transformation of AI detection systems from academic aids to new forms of academic taxation, highlighting the absurdity of the current situation where students, businesses, and platforms engage in a "cat-and-mouse" game over AI detection rates, compromising academic integrity and the essence of education [1]. Group 1: AI Detection and Academic Integrity - Several universities in China have implemented strict regulations on AI-generated content detection rates for theses, with undergraduate papers not exceeding 15%, master's theses 10%, and doctoral dissertations 5% [1]. - Classic literary works have been misidentified as AI-generated content by detection tools, with examples like "Tengwang Ge Xu" being flagged with a 100% AI generation probability, raising concerns about the reliability of AI detection technology [1][2]. Group 2: Misjudgment and Systemic Issues - AI detection systems primarily rely on public databases and online search mechanisms, leading to misjudgments of classic literature due to their widespread citation [2]. - Students have faced undue pressure to prove the originality of their work, often resorting to extreme measures to counteract AI detection, which can lead to significant psychological stress [5][10]. Group 3: Commercial Exploitation and Black Market - The increasing focus on AI detection has given rise to a "technical black market," where students feel compelled to pay for multiple detection services due to inconsistent results across platforms [14]. - Some detection platforms exploit students' anxiety over AI rates, creating a profit-driven cycle that burdens students financially, with average annual expenses for detection reaching 3000 yuan [15]. Group 4: Flaws in Detection Technology - The algorithms used in AI detection systems often exhibit "mode bias," mislabeling well-structured academic writing as AI-generated due to their reliance on specific linguistic features [6][9]. - The rapid advancement of AI generation technologies has outpaced the optimization of detection systems, leading to high misjudgment rates and exacerbating the academic evaluation crisis [10][12]. Group 5: Recommendations for Improvement - To address the challenges posed by AI detection, a multi-faceted approach is recommended, focusing on enhancing the controllability of detection technologies, adapting academic evaluation systems, and strengthening legal regulations to ensure fair practices in the detection market [26][27][28].