用AI识别并阻断考试中的大模型聊天服务，保障学术诚信。
Fighting AI with AI: AI-Agent Augmented DNS Blocking of LLM Services during Student Evaluations
- 通过AI代理动态发现并分类新兴LLM聊天服务
- 在监考考试中实现全网临时阻断，准确率超83%
- 支持多语言、可解释性高，适合教育安全研究者使用
大型语言模型（LLMs）在教育领域具有提升可及性和个性化学习的潜力，但其可能削弱学术评估，导致认知卸载和批判性思维缺失。为应对这一挑战，我们提出AI-Sinkhole——一种基于DNS的AI代理增强框架，可动态发现、语义分类并临时全网阻断考试期间的新兴LLM聊天服务。该框架利用量化LLM（LLama 3、DeepSeek-R1、Qwen-3）实现可解释分类，在跨语言场景下表现稳健（F1分数 > 0.83）。通过Pi-Hole实现动态域名阻断。项目代码已开源，便于后续研究与部署。
原文摘要 · Abstract (English)
The transformative potential of large language models (LLMs) in education, such as improving accessibility and personalized learning, is being eclipsed by significant challenges. These challenges stem from concerns that LLMs undermine academic assessment by enabling bypassing of critical thinking, leading to increased cognitive offloading. This emerging trend stresses the dual imperative of harnessing AI's educational benefits while safeguarding critical thinking and academic rigor in the evolving AI ecosystem. To this end, we introduce AI-Sinkhole, an AI-agent augmented DNS-based framework that dynamically discovers, semantically classifies, and temporarily network-wide blocks emerging LLM chatbot services during proctored exams. AI-Sinkhole offers explainable classification via quantized LLMs (LLama 3, DeepSeek-R1, Qwen-3) and dynamic DNS blocking with Pi-Hole. We also share our observations in using LLMs as explainable classifiers which achieved robust cross-lingual performance (F1-score > 0.83). To support future research and development in this domain initial codes with a readily deployable 'AI-Sinkhole' blockist is available on https://github.com/AIMLEdu/ai-sinkhole.
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