Detecting AI / Blog
Research, practical guides, and careful reporting for people who review authorship. No certainty theater, just the evidence that matters and the limits around it.
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What a text detector can and cannot tell you about a math solution, plus a fair review process for equations, written reasoning, and student work.
Reporting for educators, editors, researchers, and anyone making decisions about AI-assisted work.
Why AI detector scores disagree, what the numbers actually mean, and how to compare results without cherry-picking a verdict.
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Explore the complexities of ownership surrounding AI-generated content, including copyright implications and verification tools for creators.
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Five practical risks of relying too heavily on generative AI for academic writing, with steps for protecting evidence, privacy, learning, and…
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A current, non-ranked review of free AI detector access, what each result shows, and how to compare tools without trusting a single score.
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Why human writing can trigger an AI detector, which texts need extra caution, and how to review a result without making a false accusation.
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The practical difference between AI detection and source matching, what each method can miss, and when a reviewer should use both.
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A fair review process for possible AI use in student writing, from assignment policy and drafts to detector signals and student conversation.
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Seven practical checks for reviewing possible AI use in a research paper without treating style, a detector score, or one error as proof.
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