Testing the Limits: Evaluating AI Detectors’ Accuracy and the Impact of Obfuscation Techniques on AI-Generated Text

Makhmutova A. Sharimbayev B. Amirzhanov A. Shalkarbay-Uly A.
2026 Engineering and Technology Publishing

Journal of Advances in Information Technology
2026 #17 Issue 3 438 - 449 pp.

The rise of Artificial Intelligence (AI)-generated text has led to the development of numerous detection tools to distinguish between human and machine-authored content. However, the effectiveness of these tools, especially against manipulated texts, remains uncertain. This study evaluates nine widely used AI detection tools—Turnitin, ZeroGPT, Detecting-AI.com, GPTZero, QuillBot, Grammarly, Sapling, Copyleaks, and Originality.ai—using texts from four large language models—ChatGPT, DeepSeek, Gemini, and Grok—as well as human-written samples. Initial findings indicate that commercial tools, such as Copyleaks and Originality.ai, achieved near-perfect detection rates, while free tools, including Grammarly and QuillBot, performed less reliably, with some as low as 63.0%. On the other hand, paraphrasing and Non-Native English Speakers (NNES)-style rewriting techniques reduced detection accuracy across most detectors. Turnitin dropped to 45.7%, while Grammarly fell to 19.0% in some cases. Only Copyleaks, GPTZero, and Sapling maintained strong performance under obfuscation. The study highlights three issues: inconsistent detector performance, the impact of obfuscation, and ethical risks, including bias and false positives. The study suggests that while some detectors offer robust baseline performance, combining them with pedagogical strategies and policies is essential to uphold academic integrity.

academic integrity , AI detection tools , ethical implications , large language models , obfuscation techniques

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Department of General Education, New Uzbekistan University, Tashkent, Uzbekistan
Department of Information Systems, SDU University, Kaskelen, Kazakhstan
Department of Mathematics and Natural Sciences, SDU University, Kaskelen, Kazakhstan
Institute of Digital Transformation and Artificial Intelligence, Narxoz University, Almaty, Kazakhstan

Department of General Education
Department of Information Systems
Department of Mathematics and Natural Sciences
Institute of Digital Transformation and Artificial Intelligence

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Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026