  Large language models (LLMs) have exhibited remarkable capabilities in text
generation tasks. However, the utilization of these models carries inherent
risks, including but not limited to plagiarism, the dissemination of fake news,
and issues in educational exercises. Although several detectors have been
proposed to address these concerns, their effectiveness against adversarial
perturbations, specifically in the context of student essay writing, remains
largely unexplored. This paper aims to bridge this gap by constructing
AIG-ASAP, an AI-generated student essay dataset, employing a range of text
perturbation methods that are expected to generate high-quality essays while
evading detection. Through empirical experiments, we assess the performance of
current AIGC detectors on the AIG-ASAP dataset. The results reveal that the
existing detectors can be easily circumvented using straightforward automatic
adversarial attacks. Specifically, we explore word substitution and sentence
substitution perturbation methods that effectively evade detection while
maintaining the quality of the generated essays. This highlights the urgent
need for more accurate and robust methods to detect AI-generated student essays
in the education domain.
