Assessing AI Detectors in Identifying AI-Generated Code: Implications for Education
Wei Hung PanSchool of Information Technology, Monash University Malaysia, Subang Jaya, Selangor, MalaysiaMing Jie ChokSchool of Information Technology, Monash University Malaysia, Subang Jaya, Selangor, MalaysiaJonathan Leong Shan WongSchool of Information Technology, Monash University Malaysia, Subang Jaya, Selangor, MalaysiaY. ShinSchool of Information Technology, Monash University Malaysia, Subang Jaya, Selangor, MalaysiaYeong Shian PoonSchool of Information Technology, Monash University Malaysia, Subang Jaya, Selangor, MalaysiaZhou YangSchool of Computing and Information Systems, Singapore Management University, Singapore, SingaporeChun Yong ChongSchool of Information Technology, Monash University Malaysia, Subang Jaya, Selangor, MalaysiaDavid LoSchool of Computing and Information Systems, Singapore Management University, Singapore, SingaporeMei Kuan LimSchool of Information and Technology, Monash University Malaysia, Subang Jaya, Selangor, Malaysia
2024en
ABI
Abstract
Educators are increasingly concerned about the usage of Large Language Models (LLMs) such as ChatGPT in programming education, particularly regarding the potential exploitation of imperfections in Artificial Intelligence Generated Content (AIGC) Detectors for academic misconduct.
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