The rapid advancement of Large Language Models (LLMs) presents a significant challenge to academic integrity within computing education. As educators seek reliable detection methods, this paper evaluates the capacity of three prominent LLMs (GPT-4, Claude, and Gemini) to identify AI-generated text in computing-specific contexts. We test their performance under both standard and 'deceptive' prompt conditions, where the models were instructed to evade detection. Our findings reveal a significant instability: while default AI-generated text was easily identified, all models struggled to correctly classify human-written work (with error rates up to 32%). Furthermore, the models were highly susceptible to deceptive prompts, with Gemini's output completely fooling GPT-4. Given that simple prompt alterations significantly degrade detection efficacy, our results demonstrate that these LLMs are currently too unreliable for making high-stakes academic misconduct judgments.

翻译：大型语言模型（LLMs）的快速发展对计算机教育领域的学术诚信构成了重大挑战。随着教育工作者寻求可靠的检测方法，本文评估了三种主流LLMs（GPT-4、Claude和Gemini）在计算机特定语境中识别AI生成文本的能力。我们在标准提示和"欺骗性"提示条件下测试了它们的性能，其中模型被指示规避检测。我们的研究结果揭示了显著的不稳定性：虽然默认的AI生成文本容易被识别，但所有模型都难以正确分类人类撰写的作业（错误率高达32%）。此外，这些模型对欺骗性提示高度敏感，Gemini的输出甚至完全骗过了GPT-4。鉴于简单的提示修改就能显著降低检测效能，我们的结果表明，这些LLMs目前对于做出高风险学术不端判断而言仍具有不可靠性。