False Positives in Artificial Intelligence Detectors in Academic Assignments: A Comparative Analysis of Digital Platforms in Educational Contexts
DOI:
https://doi.org/10.70577/asce.v5i2.861Keywords:
Artificial Intelligence, false positives, AI detectors, academic evaluation, education, academic assignments, educational ethics, studentsAbstract
This research aimed to analyze the reliability of different Artificial Intelligence detection tools applied to academic assignments in educational contexts. The study was conducted using a quantitative, descriptive, and comparative approach, involving five teachers who evaluated the same textual content corresponding to an old poem by Mario Benedetti through the platforms JustDone, Reprism, Grammarly, Sidekicker, and ZeroGPT.
The results revealed significant differences among the analyzed tools. JustDone, Reprism, and Sidekicker classified the human-written content with high AI detection percentages, reaching values of up to 99%, whereas ZeroGPT and Grammarly identified the text as entirely human, reporting 0% AI-generated content. Additionally, a survey was conducted with participating teachers, whose responses reflected distrust regarding the accuracy of these tools and concern about their potential negative impact on Basic Education and High School students. The study concludes that some AI detectors present false positives and important limitations in accuracy; therefore, they should not be used as definitive evidence in academic evaluation processes. It is recommended that these tools be complemented with human review, contextual analysis, and pedagogical criteria before making judgments about the authenticity of student academic work.
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Copyright (c) 2026 Paúl Alexánder Yanangómez Suárez , Enma Regina Díaz Díaz , Lilia Margarita Ojeda Cabrera , Carmen Beneranda Yaguachi Quichimbo , María Cristina Carrillo Díaz
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