Damilola John Babalola
College of Engineering
Is this your profile?
Claim your Citation Index profile in order to display more information about you and gain access to Libraries services. Just create or connect your ORCID iD.
Create or connect your ORCID iD
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)
dl.acm.org (publisher PDF)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: March 8, 2024
Citation Index includes data from a number of different sources. If you have questions about the sources of data in the Citation Index or need a set of data which is free to re-distribute, please contact us.
Certain data included herein are derived from the Web of Science© and InCites© (2024) of Clarivate Analytics. All rights reserved. You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.