Detecting GPT-Assisted Writing Using Interpretable Stylometric Features

2026-09-22

AI Summary

本文通过从文本中提取可解释的文体特征，并使用机器学习分类器，特别是随机森林，来区分GPT辅助和独立完成的学生写作，以应对学术界面临的挑战。

Abstract

Distinguishing GPT-assisted from independently authored student writing has become a critical challenge in academia. This paper evaluates the discriminative capability of interpretable stylometric features extracted solely from submitted text. Using data from 90 participants who wrote both independently and with ChatGPT assistance, we evaluate eight machine learning classifiers while keeping data from the same participant together during validation. On the held-out test set, Random Forest achieved an ROC-AUC of 0.87 and an F1-score of 0.84, with False Positive and False Negative rates of 22.2% and 11.1%, respectively. SHAP analysis shows that lexical and grammatical characteristics drive the resulting predictions. The findings suggest that transparent, text-intrinsic features provide measurable signal for detecting GPT-assisted writing.

Problem

• GPT-assisted writing
• independently authored
• stylometric features

Innovation

• interpretable stylometric features
• GPT-assisted writing detection
• machine learning classifiers
• SHAP analysis