A Review of AIGC Text Detection in Academic Papers
TL;DR
The review shows that current detection approaches face several challenges, including a shortage of suitable datasets, ambiguous labels for human–AI collaborative text, insufficient coverage of generative models, limited generalization, and inadequate interpretability.
Abstract
With the widespread adoption of large language models in academic writing, the detection of artificial intelligence-generated content has become an important research topic. This paper reviews the detection of AI-generated text in academic papers. It defines the relevant concepts, surveys detection methods and commercial detection tools, and introduces experimental datasets and evaluation metrics. The review shows that current detection approaches face several challenges, including a shortage of suitable datasets, ambiguous labels for human–AI collaborative text, insufficient coverage of generative models, limited generalization, and inadequate interpretability. Future research should develop datasets covering multiple languages, disciplines, models, and generation methods; strengthen evaluation on unseen models and adversarial samples; and improve explainable detection and human-review mechanisms, thereby supporting the governance of academic integrity.