@article{Pudasaini2024,
 title = {Survey on {{Plagiarism Detection}} in {{Large Language Models}}: {{The Impact}} of {{ChatGPT}} and {{Gemini}} on {{Academic Integrity}}},
 author = {Pudasaini, Shushanta and {Miralles-Pechu{\'a}n}, Luis and Lillis, David and Salvador, Marisa Llorens},
 year = {2024},
 journal = {Journal of Academic Ethics},
 abstract = {The rise of Large Language Models (LLMs) such as ChatGPT and Gemini has posed new challenges for the academic community. With the help of these models, students can easily complete their assignments and exams, while educators struggle to detect AI-generated content. This has led to a surge in academic misconduct, as students present work generated by LLMs as their own, without putting in the effort required for learning. As AI tools become more advanced and produce increasingly human-like text, detecting such content becomes more challenging. This development has significantly impacted the academic world, where many educators are finding it difficult to adapt their assessment methods to this challenge. This research first demonstrates how LLMs have increased academic dishonesty, and then reviews state-of-the-art solutions for academic plagiarism in detail. A survey of datasets, algorithms, tools, and evasion strategies for plagiarism detection has been conducted, focusing on how LLMs and AI-generated content (AIGC) detection have affected this area. The survey aims to identify the gaps in existing solutions. Lastly, potential long-term solutions are presented to address the issue of academic plagiarism using LLMs based on AI tools and educational approaches in an ever-changing world.},
 keywords = {Computer Science - Artificial Intelligence,Computer Science - Computers and Society,Computer Science - Machine Learning},
}