Damilola John Babalola
College of Engineering
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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.
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)
open access via dl.acm.org (publisher PDF)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science; OpenAlex)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: March 8, 2024
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