Educators are increasingly concerned about the usage of Large Language Models (LLMs) such as ChatGPT in programming education, particularly regarding the potential exploitation of imperfections in Artificial Intelligence Generated Content (AIGC) Detectors for academic misconduct. In this paper, we present an empirical study where the LLM is examined for its attempts to bypass detection by AIGC Detectors. This is achieved by generating code in response to a given question using different variants. We collected a dataset comprising 5,069 samples, with each sample consisting of a textual description of a coding problem and its corresponding human-written Python solution codes. These samples were obtained from various sources, including 80 from Quescol, 3,264 from Kaggle, and 1,725 from LeetCode. From the dataset, we created 13 sets of code problem variant prompts, which were used to instruct ChatGPT to generate the outputs. Subsequently, we assessed the performance of five AIGC detectors. Our results demonstrate that existing AIGC Detectors perform poorly in distinguishing between human-written code and AI-generated code.

翻译：教育工作者日益关注大型语言模型（如ChatGPT）在编程教育中的应用，特别是担忧学生可能利用人工智能生成内容（AIGC）检测器的缺陷实施学术不端行为。本文通过实证研究，考察大型语言模型如何尝试规避AIGC检测器的识别。我们通过生成针对特定问题的不同变体代码展开实验：首先收集包含5069个样本的数据集，每个样本包含编程问题的文本描述及其对应的人类编写的Python解决方案代码（其中80个来自Quescol，3264个来自Kaggle，1725个来自LeetCode）。基于该数据集，我们创建了13组代码问题变体提示，引导ChatGPT生成输出结果；随后评估了五款AIGC检测器的性能。研究结果表明，现有AIGC检测器在区分人类编写代码与AI生成代码方面表现欠佳。