  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.
