Can AI Recognize Its Own Reflection? Self-Detection Performance of LLMs in Computing Education

• URL: http://arxiv.org/abs/2512.23587v1
• Date: Mon, 29 Dec 2025 16:35:52 GMT
• Title: Can AI Recognize Its Own Reflection? Self-Detection Performance of LLMs in Computing Education
• Authors: Christopher Burger, Karmece Talley, Christina Trotter,
• Abstract summary: This paper evaluates the capacity of three prominent Large Language Models to identify AI-generated text in computing-specific contexts.<n>We test their performance under both standard and 'deceptive' prompt conditions, where the models were instructed to evade detection.<n>Our findings reveal a significant instability: while default AI-generated text was easily identified, all models struggled to correctly classify human-written work.
• Score: 0.10195618602298682
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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