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

Abstract

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 offering educators with services to assess whether some text is LLM-generated. In this work, we have collected 124 submissions from computer science students before the creation of ChatGPT. We then generated 40 ChatGPT submissions. We used this data to evaluate eight publicly-available LLM-generated text detectors through the measures of accuracy, false positives, and resilience. Our results find that Copy Leaks is the most accurate LLM-generated text detector, G PTKit is the best LLM-generated text detector to reduce false positives, and GLTR is the most resilient LLM-generated text detector. We note that all LLM-generated text detectors are less accurate with code, other languages (aside from English), and after the use of paraphrasing tools.

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