Hidding the Ghostwriters: An Adversarial Evaluation of AI-Generated Student Essay Detection

This paper evaluates the robustness of AI-generated content detectors specifically in the context of student essays, constructing the AIG-ASAP dataset with various adversarial perturbation methods to test whether current detectors can identify AI-written student essays that have been modified to evade detection.

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 br

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