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the claim

Plagiarism detection software accurately identifies plagiarized passages in student essays.

the verdict
CONTESTED PARTIAL
Recorded sources
2 sources for · 4 against

Counts group repeated records of the same source within each side. They do not measure evidence strength or source independence.

Is it true that plagiarism detection software accurately identifies plagiarized passages in student essays?

Partly. The retrieved items offer only partial and mixed coverage: one source says detection software can compare student papers to published works quickly, while several others note accuracy concerns, documented loopholes, and limits in detecting AI-generated text. None provides a measured accuracy rate for identifying plagiarized passages in student essays.

Verdict: CONTESTED PARTIAL · 2 sources for, 4 against · Last checked

The private library was unavailable for some searches in this check. Missing library results are not evidence against the claim.

Evidence for · 2
Comparative analysis of text-based plagiarism detection techniques -
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Comparative analysis of text-based plagiarism detection techniques - PMC Lock. (n.d.). pmc.ncbi.nlm.nih.gov. https://doi.org/10.1371/journal.pone.0319551

Evidence against · 4
Plagiarism
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Plagiarism. (n.d.). en.wikipedia.org. https://en.wikipedia.org/wiki/Plagiarism

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More for · 1
Plagiarism Detection of Doctoral Theses
2016
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Plagiarism Detection of Doctoral Theses. (2016). lilloa.hal.science. https://lilloa.hal.science/hal-01398398v1

More against · 3
Student and staff perceptions of the effectiveness of plagiarism detection software
2008
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Atkinson, D. & Yeoh, S. (2008). Student and staff perceptions of the effectiveness of plagiarism detection software. doi.org. https://doi.org/10.14742/ajet.1224

Turnitoff: Identifying and Fixing a Hole in Current Plagiarism Detection Software
2010
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James, H. (2010). Turnitoff: Identifying and Fixing a Hole in Current Plagiarism Detection Software. eric.ed.gov. https://eric.ed.gov/?id=EJ901011

AI Content Self-Detection for Transformer-based Large Language Models
2023
Get citation
Caiado, A. J. A. & Hahsler, M. (2023). AI Content Self-Detection for Transformer-based Large Language Models. arxiv.org. https://doi.org/10.48550/arXiv.2312.17289

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