Human-AI collaborative writing has been greatly facilitated with the help of modern large language models (LLM), e.g., ChatGPT. While admitting the convenience brought by technology advancement, educators also have concerns that students might leverage LLM to partially complete their writing assignment and pass off the human-AI hybrid text as their original work. Driven by such concerns, in this study, we investigated the automatic detection of Human-AI hybrid text in education, where we formalized the hybrid text detection as a boundary detection problem, i.e., identifying the transition points between human-written content and AI-generated content. We constructed a hybrid essay dataset by partially removing sentences from the original student-written essays and then instructing ChatGPT to fill in for the incomplete essays. Then we proposed a two-step detection approach where we (1) Separated AI-generated content from human-written content during the embedding learning process; and (2) Calculated the distances between every two adjacent prototypes (a prototype is the mean of a set of consecutive sentences from the hybrid text in the embedding space) and assumed that the boundaries exist between the two prototypes that have the furthest distance from each other. Through extensive experiments, we summarized the following main findings: (1) The proposed approach consistently outperformed the baseline methods across different experiment settings; (2) The embedding learning process (i.e., step 1) can significantly boost the performance of the proposed approach; (3) When detecting boundaries for single-boundary hybrid essays, the performance of the proposed approach could be enhanced by adopting a relatively large prototype size, leading to a $22$\% improvement (against the second-best baseline method) in the in-domain setting and an $18$\% improvement in the out-of-domain setting.

翻译：人机协作写作在现代大语言模型（如ChatGPT）的助力下已得到极大便利。在承认技术进步带来便捷的同时，教育工作者也担忧学生可能利用大语言模型部分完成写作作业，并将人机混合文本冒充为其原创作品。基于此担忧，本研究探索了教育领域中的人机混合文本自动检测问题，我们将混合文本检测形式化为边界检测任务，即识别人类撰写内容与AI生成内容之间的转换点。我们通过从原始学生作文中部分移除句子，并指导ChatGPT补全不完整作文的方式，构建了混合作文数据集。随后提出一种两步检测方法：（1）在嵌入学习过程中分离AI生成内容与人类撰写内容；（2）计算每两个相邻原型（原型指嵌入空间中混合文本连续句子序列的均值向量）之间的距离，并假设边界存在于相距最远的两个原型之间。通过大量实验，我们总结出以下主要发现：（1）所提方法在不同实验设置下均持续优于基线方法；（2）嵌入学习过程（即步骤1）可显著提升所提方法的性能；（3）在检测单边界混合作文时，采用较大的原型尺寸可增强所提方法性能，在域内设置下较次优基线方法提升22%，在域外设置下提升18%。