This thesis aims to evaluate the efficacy of plagiarism detection systems used by universities in verifying the authenticity of students' knowledge, specifically in the context of ChatGPT and similar AI models. By assessing the capabilities and limitations of these systems within the university environment, this research will explore whether they can effectively identify instances of plagiarism involving AI-generated content and ensure the authenticity of student work. 

Advancing Plagiarism Detection Technology. The findings of this thesis can provide insights into the capabilities and limitations of current plagiarism detection systems when dealing with AI-generated content. This information can guide the development and improvement of future systems to better address the challenges posed by evolving technologies.

The widespread availability and accessibility of AI language models, such as ChatGPT, have revolutionized the way individuals interact with and generate textual content. However, this technological advancement has also raised concerns regarding the authenticity of knowledge and the potential for increased instances of plagiarism within educational institutions. As universities strive to maintain academic integrity and ensure that students' work reflects their own understanding and effort, it becomes crucial to assess the effectiveness of existing plagiarism detection systems in addressing the unique challenges posed by AI-generated content. By evaluating the efficacy of these systems in the era of ChatGPT, this thesis seeks to contribute to propose recommendations and guidelines to enhance the efficacy of plagiarism detection systems in universities in the era of ChatGPT.

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