ChatGPT Code Detection: Techniques for Uncovering the Source of Code
Project Overview
The document explores the integration of generative AI, particularly large language models (LLMs) like ChatGPT, in the educational landscape, underscoring both its potential benefits and challenges. It discusses how these AI tools can enhance learning experiences by providing personalized support, assisting educators in content creation and assessment, and transforming traditional educational practices. However, the rise of AI in education also raises ethical dilemmas, such as the risk of academic dishonesty, as AI-generated code can be indistinguishable from human-written code. The research addresses these concerns by developing classification techniques to differentiate between human and AI-generated outputs using various machine learning models. This highlights the need to maintain the integrity of academic work while leveraging AI's capabilities. Overall, the findings indicate that while generative AI can offer innovative solutions to pedagogical challenges, it is crucial to navigate the associated ethical and practical implications to ensure a balanced and effective integration into educational systems.
Key Applications
AI Code Generation and Understanding Tools
Context: Higher education settings, including software development courses, competitive programming, and classroom teaching environments focused on programming and code comprehension.
Implementation: Utilizes large language models (LLMs) such as ChatGPT and CodeT5+ for generating code and providing code examples. These tools are implemented as educational resources to assist students in learning programming and understanding coding concepts, including developing models to distinguish between human and AI-generated code.
Outcomes: Students display improved coding skills and a better understanding of programming concepts. Additionally, models developed can achieve high accuracy (up to 98%) in distinguishing between AI-generated code and human-written code.
Challenges: Challenges include the difficulty of obtaining a sufficiently large and diverse dataset of AI-generated code for effective model training, the potential biases in generated code examples, and the need for careful oversight during implementation.
Implementation Barriers
Data availability
Lack of a sufficient number of publicly available GPT-generated code samples that meet the study's criteria for training classifiers.
Proposed Solutions: Generating diverse and high-quality datasets by using multiple AI models or prompts to enhance the variety of code examples.
Ethical
Concerns regarding data privacy and the potential for bias in AI-generated content.
Proposed Solutions: Implementing strict data governance policies and ensuring transparency in AI algorithms.
Practical
Challenges in integrating AI tools into existing curricula and training educators to use them effectively.
Proposed Solutions: Providing comprehensive training programs for educators and gradually introducing AI tools into the classroom.
Project Team
Marc Oedingen
Researcher
Raphael C. Engelhardt
Researcher
Robin Denz
Researcher
Maximilian Hammer
Researcher
Wolfgang Konen
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Marc Oedingen, Raphael C. Engelhardt, Robin Denz, Maximilian Hammer, Wolfgang Konen
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai