Short or formulaic submissions
Short answers, lab summaries, resumes, and rubric-driven paragraphs can lack enough stylistic variation for stable scoring. Reviewers should request more context before escalating.
Resources
Common false-positive examples in AI detection, including short samples, translated writing, templates, polished edits, and citation-heavy documents.
Open core guideShort answers, lab summaries, resumes, and rubric-driven paragraphs can lack enough stylistic variation for stable scoring. Reviewers should request more context before escalating.
Translation tools and second-language revision can smooth sentence rhythm and vocabulary. That pattern may resemble AI-generated text even when the author wrote the underlying ideas.
Cover letters, policy memos, product descriptions, and citation-heavy papers often use conventional phrasing. A responsible workflow compares highlighted passages with drafts, sources, and policy.
A false positive happens when human-authored writing is flagged as AI-generated. It should trigger careful review, not an automatic accusation or rejection.
Teams should document the pattern, inspect passage evidence, collect drafts or source context, and adjust thresholds for document types that are more likely to look formulaic.