← Back to all news
arXiv AI By Hyeonchu Park, Gahye Jeong, Bugeun Kim

Style as a Confound: False Positives in AI Detection of Non-Native Academic Writing

Read the original on arXiv AI →

The study examines how professional English editing influences AI text detectors’ false-positive rates for non-native academic writing. Using 135,389 pairs of original and edited manuscripts, researchers found that detector responses varied widely—some editors increased AI scores while others decreased them—and that score changes correlated with the extent of editing. These results highlight professional editing style as a key confounding factor in AI detection, complicating the distinction between AI authorship and linguistic style.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv Machine Learning
Aug 13

Why AI Detection Fails for Academic Integrity

arXiv:2608. 11256v1 Announce Type: new Abstract: Institutions use commercial AI detectors for academic integrity, yet detectors cannot distinguish AI editing from full LLM drafts and may treat both as misconduct.

By Jonathan A. Karr Jr, Grigorii Khvatskii, Ting Hua, Nitesh V. Chawla
llms
More like this →
arXiv Machine Learning
Jun 5

Operation-Guided Progressive Human-to-AI Text Transformation Benchmark for Multi-Granularity AI-Text Detection

arXiv:2606. 06481v1 Announce Type: cross Abstract: As AI writing assistants become increasingly integrated into real-world drafting and revision workflows, many documents are no longer purely human-written or AI-generated, but instead result from progressive human-AI co-editing.

By Sondos Mahmoud Bsharat, Jiacheng Liu, Xiaohan Zhao, Tianjun Yao, Xinyi Shang, Yi Tang, Jiacheng Cui, Ahmed Elhagry, Salwa K. Al Khatib, Hao Li, Salman Khan, Zhiqiang Shen
benchmarks
More like this →
arXiv Computation and Language
4d ago

AI Writers Have a Consistent Stylometric Footprint, but AI Editors Do Not

The study demonstrates that text produced by large language models (LLMs) leaves a distinct stylometric footprint—primarily increased entropy and lexical diversity—across multiple models and domains. In contrast, AI editing of human text does not replicate this footprint; edited texts show only modest lexical diversity gains and reduced entropy, with lexical density emerging as the key distinguishing feature. Consequently, stylometric analysis can differentiate AI-generated from AI-edited content, but is less effective at distinguishing either from purely human writing.

By Zhengyang Shan, Yukyung Lee, Sophie Hao
llms
More like this →