Is ai writing detection really accurate?

The accuracy of ai writing detection has certain limitations and there is a possibility of misjudgment. ‌
In recent years, with the rapid development of artificial intelligence technology, the ability of AI to generate academic papers has become increasingly strong, capable of generating logically clear and structurally complete academic content. In order to prevent AI from ghostwriting graduation theses, some universities have added AIGC (AI Generated Content) detection on the basis of the original plagiarism detection. However, this detection method is not entirely reliable and there are cases of misjudgment.
Misjudgment phenomenon and reasons
Original content misjudged: Some students have reported that their papers written through field research have been judged by the detection system as having a "high AI rate". For example, Yang, a senior graduate from a certain university, had his AI detection rate for the same paper increase from 10.37% to 27.54% on a certain paper detection platform within one day, resulting in the need for re editing and plagiarism checking.
Technical limitations: Currently, ai writing detection technology mainly determines whether content is generated by AI by analyzing the semantic coherence, thematic consistency, and writing style of the text. However, these methods have limitations in handling semantic associations, topic consistency, and citation systems across paragraphs, which may lead to misjudgments.
Algorithm and Data Processing: The core of AIGC technology lies in algorithms, including machine learning, deep learning, and generative adversarial networks (GANs). Although these technologies can generate high-quality content, the system may not be able to effectively identify semantic associations across paragraphs and intrinsic academic correlations in citation systems during detection, leading to misjudgments.
Suggestions for avoiding misjudgments
Full text detection: It is recommended to use full text detection instead of segmented detection to avoid misjudgment caused by local analysis. Full text detection can better evaluate the semantic coherence and thematic consistency of text.
Optimization algorithm: Develop more advanced algorithms and technologies to improve the accuracy and reliability of detection systems. For example, by improving methods such as semantic analysis, topic recognition, and citation system evaluation, the occurrence of misjudgments can be reduced.