AI-Generated Text and Academic Integrity: A Systematic Review of Detection Tools in Educational Settings

Abstract

Arslan Akram

The rapid expansion of generative artificial intelligence and large language models has dramatically altered the creation of written material. While these technologies can enhance the learning process, but, as well, they have created significant concerns regarding academic integrity, authorship and the fair assessment of students. In response, numerous institutions have already implemented AI-detection software that aims at determining whether some text was authored by a human or generated by AI systems. But there are concerns about the validity, correctness and equity of such tools in the actual fields of education. This paper gives a systematic review of empirical studies concerning AI text detection tools in learning institutions. In order to identify literature published between 2022 and 2026, it was thoroughly searched in large academic databases such as Scopus, Web of Science, and ScienceDirect, following the PRISMA 2020 guidelines. Through the use of rigorous inclusion/exclusion criteria, a total of 17 empirical studies were selected for in-depth analysis. In the review, the researchers examine the different types of research methodologies used to evaluate the performance of AI detection tools, the accuracy of these tools in identifying AI-generated content, and the effectiveness, fairness, and usability of these tools. The results show that the tools, including Turnitin, GPTZero, Copyleaks, ZeroGPT, and Originality.ai, are frequently used, but their effectiveness varies depending on the type of text and writing conditions. It is often mentioned in the literature that such systems become quite easily affected by paraphrasing, translation or writing style, and that they can falsely categorize the texts written by multilingual or non- native writers of English. The review also presents a number of technical, ethical and pedagogical difficulties with the continuation of the use of automated detection systems only. Upon the evidence synthesized, the study elucidates the importance of careful and open usage of AI detection technologies in learning and suggests the directions of future research to enhance its consistency and at the same time encourages ethical academic procedures.

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