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Abstract

The rapid integration of artificial intelligence (AI) in content generation has encouraged the development of AI detection tools aimed at distinguishing between human- and AI-authored texts. These tools are increasingly adopted not only in academia but also in sensitive decision-making contexts, including candidate screening by hiring agencies in government and private sectors. This extensive reliance raises serious questions about their reliability, fairness, and appropriateness for high-stakes applications. This study evaluates the performance of six widely used AI content detection tools, namely Undetectable AI, Zerogpt.com, Zerogpt.net, Brandwell.ai, Gowinston.ai, and Crossplag, referred to as Tools A through F in this study. The assessment focused on the ability of the tools to identify human versus AI-generated content across multiple domains. Verified human-authored texts were gathered from reputable sources, including university websites, pre-ChatGPT publications in Nature and Science, government portals, and media outlets (e.g., BBC, US News). Complementary datasets of AI-generated texts were produced using ChatGPT-4o, encompassing coherent essays, nonsensical passages, and hybrid texts with grammatical errors, to test tool robustness. The results reveal significant performance limitations. The accuracy ranged from 14.3% (Tool B) to 71.4% (Tool D), with the precision and recall metrics showing inconsistent detection capabilities. The tools were also highly sensitive to minor textual modifications, where slight changes in phrasing could flip classifications between “AI-generated” and “human-authored.” Overall, the current AI detection tools lack the robustness and reliability needed for enforcing academic integrity or making employment-related decisions. The findings highlight an urgent need for more transparent, accurate, and context-aware frameworks before these tools can be responsibly incorporated into critical institutional or societal processes.