Damilola John Babalola

College of Engineering

Works (1)

Updated: April 20th, 2024 05:00

2024 article

Detecting ChatGPT-Generated Code Submissions in a CS1 Course Using Machine Learning Models

PROCEEDINGS OF THE 55TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, SIGCSE 2024, VOL. 1, pp. 526–532.

By: M. Hoq n , Y. Shi n , J. Leinonen *, D. Babalola n, C. Lynch n, T. Price n , B. Akram n

author keywords: ChatGPT; large language model; artificial intelligence; introductory programming course; CS1; cheat detection; plagiarism detection
TL;DR: This work evaluated the performance of both traditional machine learning models and Abstract Syntax Tree-based (AST-based) deep learning models in detecting ChatGPT code from student code submissions, and suggested that both traditional machine learning models and AST-based deep learning models are effective in identifying ChatGPT-generated code with accuracy above 90%. (via Semantic Scholar)
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Added: March 8, 2024

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