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Detecting AI-Generated Code in Introductory Programming Courses
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DOI：10.1145/3770762.3772522.png)
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Abstract
En 中文
With the rapid surge of generative AI, many tools have been introduced, such as Google's Gemini and OpenAI's GPT-4, with the well-intentioned goal of supporting programmers [5, 8]. These tools can be used by professional programmers to help write code efficiently as well as support debugging and testing; however, we recently began to notice an increase in the number of novice programmers who have become highly dependent on Large Language Models (LLMs) to code for them rather than using LLMs as a learning tool [2, 10]. In our CS1 course, approximately 10-15% of the students (out of similar to 350) were cited for academic misconduct due to direct plagiarism from LLMs, many of which performed poorly due to an over-reliance on generative AI. To address this, we developed a machine learning-based tool to detect AI-generated code. The tool utilized datasets consisting of thousands of student submissions (in C++) from introductory programming courses and we created an equal number of AI-generated solutions using carefully curated prompts. We trained traditional ML models (Random Forest, XGBoost, etc.) on a labeled datasets, and our best-performing model achieved high (>98%) precision and recall. Our work is unique as we are able to demonstrate that the models remained robust even when trained with noisy data (including AI-generated submissions in student-written dataset). Our goal is to provide the community with a model that can be customized to any course program to encourage early detection and intervention of plagiarized code generated by LLMs.
Keywords:
Generative AI
Large Language Models
Machine Learning
Plagiarism
CS1
Programming Assignments
Journal
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