TY - JOUR
T1 - Survey on AI-Generated Plagiarism Detection
T2 - The Impact of Large Language Models on Academic Integrity
AU - Pudasaini, Shushanta
AU - Miralles-Pechuán, Luis
AU - Lillis, David
AU - Llorens Salvador, Marisa
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to Springer Nature B.V. 2024.
PY - 2025/9
Y1 - 2025/9
N2 - A survey conducted in 2023 surveyed 3,017 high school and college students. It found that almost one-third of them confessed to using ChatGPT for assistance with their homework. The rise of Large Language Models (LLMs) such as ChatGPT and Gemini has led to a surge in academic misconduct. Students can now complete their assignments and exams just by asking an LLM for solutions to the given problem, without putting in the effort required for learning. And, what is more worrying, educators do not have the proper tools to detect it. The more advanced AI tools become, the more human-like text they generate, and the more difficult they are to detect. Additionally, some educators find it difficult to adapt their teaching and assessment methods to avoid plagiarism. This paper is focused on how LLMs and AI-Generated Content (AIGC) have affected education. It first shows the relationship between LLMs and academic dishonesty. Then, it reviews state-of-the-art solutions for preventing academic plagiarism in detail, including a survey of the main datasets, algorithms, tools, and evasion strategies for plagiarism detection. Lastly, it identifies gaps in existing solutions and presents potential long-term solutions based on AI tools and educational approaches to address plagiarism in an ever-changing world.
AB - A survey conducted in 2023 surveyed 3,017 high school and college students. It found that almost one-third of them confessed to using ChatGPT for assistance with their homework. The rise of Large Language Models (LLMs) such as ChatGPT and Gemini has led to a surge in academic misconduct. Students can now complete their assignments and exams just by asking an LLM for solutions to the given problem, without putting in the effort required for learning. And, what is more worrying, educators do not have the proper tools to detect it. The more advanced AI tools become, the more human-like text they generate, and the more difficult they are to detect. Additionally, some educators find it difficult to adapt their teaching and assessment methods to avoid plagiarism. This paper is focused on how LLMs and AI-Generated Content (AIGC) have affected education. It first shows the relationship between LLMs and academic dishonesty. Then, it reviews state-of-the-art solutions for preventing academic plagiarism in detail, including a survey of the main datasets, algorithms, tools, and evasion strategies for plagiarism detection. Lastly, it identifies gaps in existing solutions and presents potential long-term solutions based on AI tools and educational approaches to address plagiarism in an ever-changing world.
KW - Academic cheating
KW - Artificial intelligence generated content
KW - Large language models
KW - Plagiarism
UR - https://www.scopus.com/pages/publications/85208129908
U2 - 10.1007/s10805-024-09576-x
DO - 10.1007/s10805-024-09576-x
M3 - Article
AN - SCOPUS:85208129908
SN - 1570-1727
VL - 23
SP - 1137
EP - 1170
JO - Journal of Academic Ethics
JF - Journal of Academic Ethics
IS - 3
ER -