Research on the Generation and Automatic Detection of Chinese Academic Writing

With the advancement of text-generation technology, misuse has increasingly challenged academic research sustainability. The Chinese academic community, vast and active with millions of researchers and extensive literature, faces an inevitable generator misuse. However, research on the automatic detection of Chinese academic texts remains scarce, necessitating the thorough exploration of detection methods to support ongoing academic development. This study explored automatic detection technology for Chinese academic texts, focusing on TK2A dataset construction, generation models, and detection methods to assess their practical impact. TK2A covers papers across disciplines, such as computer science, engineering, and medicine, ensuring broad applicability and forming a solid foundation for model training and evaluation. Using advanced natural language processing, models trained on TK2A showed strong performance across disciplines. Rigorous manual evaluation verified their reliability in terms of grammar, semantics, and logic. The study employed the widely adopted BERT model for detection, achieving high accuracy in distinguishing human-written content from AI-generated content on TK2A. This research underscores TK2A’s practical value by offering crucial support to journals with an accuracy exceeding 84%, institutions, and education in swiftly detecting AI-generated content, preventing misconduct, and enhancing academic publication quality.