  The recent large language models (LLMs), e.g., ChatGPT, have been able to
generate human-like and fluent responses when provided with specific
instructions. While admitting the convenience brought by technological
advancement, educators also have concerns that students might leverage LLMs to
complete their writing assignments and pass them off as their original work.
Although many AI content detection studies have been conducted as a result of
such concerns, most of these prior studies modeled AI content detection as a
classification problem, assuming that a text is either entirely human-written
or entirely AI-generated. In this study, we investigated AI content detection
in a rarely explored yet realistic setting where the text to be detected is
collaboratively written by human and generative LLMs (i.e., hybrid text). We
first formalized the detection task as identifying the transition points
between human-written content and AI-generated content from a given hybrid text
(boundary detection). Then we proposed a two-step approach where we (1)
separated AI-generated content from human-written content during the encoder
training process; and (2) calculated the distances between every two adjacent
prototypes and assumed that the boundaries exist between the two adjacent
prototypes that have the furthest distance from each other. Through extensive
experiments, we observed the following main findings: (1) the proposed approach
consistently outperformed the baseline methods across different experiment
settings; (2) the encoder training process can significantly boost the
performance of the proposed approach; (3) when detecting boundaries for
single-boundary hybrid essays, the proposed approach could be enhanced by
adopting a relatively large prototype size, leading to a 22% improvement in the
In-Domain evaluation and an 18% improvement in the Out-of-Domain evaluation.
