Are AI detection and plagiarism similarity scores worthwhile in the age of ChatGPT and other Generative AI?

Abstract

Recent advancements in chatbots have provided students and academics with a new mode of how knowledge can be sourced and composed. Within a very short space of time, students and academics have flocked to use ChatGPT and other Generative Artificial Intelligence (GAI) platforms owing to their capable responses. Additionally, apart from the generative chatbots (such as ChatGPT and Gemini), AI writing tools for paraphrasing, summarising, and co-writing have also become capable and increasingly prevalent to such a degree that the public is spoilt for choice. Having conducted tests on popular chatbots and AI writing tools, it became clear that while programs like Turnitin are developing new algorithms to detect plagiarism and AI-generated content, the initial findings from this study suggest that this may be an increasingly difficult task. These tests were published on YouTube, and within a few weeks, the evidence garnered tens of thousands of views as students and educators seemed uncertain about the strengths, weaknesses, and legalities of these AI tools. What is clear is that we have passed the tipping point, and AI assistance is no longer just a grammar fixer. The implications of this are concerning and far-reaching, as plagiarism is already a significant problem in universities. This position paper reports on tests conducted using Turnitin software and AI writing tools such as ChatGPT and QuillBot. These real-world tests support the paper’s position that it is becoming increasingly difficult to determine what constitutes original work in a world of GAI. The aim of this article is to provide evidence that educators who rely on similarity checking and AI detectors in their current form may inadvertently be supporting plagiarism rather than reducing it. A new method of academic plagiarism detection is proposed, utilizing large language models to generate and track ideas, thereby serving as an idea database. The proposed method focuses on the "understanding" of the work rather than on text similarity.