The hidden cost of plagiarism and artificial intelligence detection tools in academic writing

Plagiarism-detection and AI-detection tools are now widely used in academic publishing. These systems were introduced to support research integrity by helping journals identify potential plagiarism, inappropriate text reuse, and concerns related to undisclosed use of artificial intelligence (AI). When used appropriately, they can serve as useful screening tools and support editorial decision-making. However, their growing use has also created new challenges. Similarity scores are often interpreted as direct measures of plagiarism, even though they only indicate matching text and require contextual evaluation. Likewise, AI-detection tools can produce uncertain or incorrect classifications, yet their output may influence perceptions of authorship and manuscript quality. As a result, researchers may spend considerable time reducing similarity scores or worrying about AI-detection reports, even when the underlying writing is appropriate. These challenges may be particularly relevant for early-career researchers and authors writing in a second language. This article discusses the benefits and limitations of both similarity-detection and AI-detection systems and argues that their output should be viewed as screening indicators rather than definitive judgments. Human interpretation should remain central to the evaluation of originality, authorship, and research quality.