Argus：生成式人工智能时代的学术诚信
Argus: Academic Integrity in the Era of Generative AI
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中文总结 AI 辅助
Argus系统通过行为与风格指标检测编程作业中的LLM辅助使用，发现45%学生存在此类模式，且与考试成绩显著负相关，强调需结合人工审查流程以维护学术诚信。
中文摘要 AI 辅助
大型语言模型（LLM）在教育领域的迅速普及，给学术诚信的维护带来了重大挑战，尤其是在编程课程中。我们提出了Argus，一个用于检测本科生C语言编程作业中LLM辅助学生工作的自动化系统。Argus整合了行为与风格指标，以全面描绘学生在作业中的进展，并揭示可能指向滥用LLM辅助的异常情况。我们使用Argus对普渡大学一门大班CS2课程连续六个春季学期的数据进行了量化与分析，发现在2026年春季学期，45%的注册学生表现出与LLM辅助代码开发一致的模式。为了对这些结果进行背景分析，我们考察了被标记的LLM使用与学生书面、现场监考考试成绩之间的关系，并发现显著的负相关。我们还探讨了减少误报的问题，认识到错误的学术诚信指控对学生和教师都会产生重大后果，尤其是在大班课程中。我们认为，任何自动化检测系统都必须辅以结构化的人工审查流程。我们讨论了这些工具日益普及对未来课程设计、学术政策调整的影响，以及基于LLM的工具在计算机科学教育中的教学意义。
英文摘要
The rapid proliferation of large language models (LLMs) in the context of education has introduced significant challenges in enforcement of academic integrity, especially in programming courses. We present Argus, an automated detection system for LLM-assisted student work in undergraduate C programming assignments. Argus integrates behavioral and stylistic indicators to create a holistic picture of the student's progress through an assignment and surfaces anomalies that point to potential misuse of LLM assistance. We quantify and analyze data over six years of Spring semester offerings in a large-enrollment CS2 course at Purdue University using Argus, finding that 45% of enrolled students exhibited patterns consistent with LLM-assisted code development in Spring 2026. To contextualize these results, we analyze the relationship between flagged LLM use and student performance on written, in-person proctored examinations, and find a significant negative correlation. We also explore the problem of mitigating false positives, recognizing that erroneous accusations of academic integrity carry significant consequences for students and instructors alike, particularly in the context of large enrollment courses. We argue that any automated detection system must be accompanied by a structured process for human review. We discuss the consequences for future course design, changing academic policy as these tools become more ubiquitous, and the pedagogical implications of LLM-based tools in computer science education.
发表机构
- Purdue University（普渡大学）
机构由 AI 辅助整理，请以论文原文为准。
