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Due to the recent improvements and wide availability of Large Language Models(LLMs), they have posed a serious threat to academic integrity in education.Modern LLM-generated text detectors attempt to combat the problem by offeringeducators with services to assess whether some text is LLM-generated. In thiswork, we have collected 124 submissions from computer science students beforethe creation of ChatGPT. We then generated 40 ChatGPT submissions. We used thisdata to evaluate eight publicly-available LLM-generated text detectors throughthe measures of accuracy, false positives, and resilience. The purpose of thiswork is to inform the community of what LLM-generated text detectors work andwhich do not, but also to provide insights for educators to better maintainacademic integrity in their courses. Our results find that CopyLeaks is themost accurate LLM-generated text detector, GPTKit is the best LLM-generatedtext detector to reduce false positives, and GLTR is the most resilientLLM-generated text detector. We also express concerns over 52 false positives(of 114 human written submissions) generated by GPTZero. Finally, we note thatall LLM-generated text detectors are less accurate with code, other languages(aside from English), and after the use of paraphrasing tools (like QuillBot).Modern detectors are still in need of improvements so that they can offer afull-proof solution to help maintain academic integrity. Further, theirusability can be improved by facilitating a smooth API integration, providingclear documentation of their features and the understandability of theirmodel(s), and supporting more commonly used languages.

Detecting LLM-Generated Text in Computing Education: A Comparative Study for ChatGPT Cases