Using Keystroke Dynamics to Detect Nonoriginal Text

Paul Deane; Mo Zhang; Jiangang Hao; Chen Li

doi:10.1111/jedm.12431

journal article Apr 03, 2025

Using Keystroke Dynamics to Detect Nonoriginal Text

Paul Deane Mo Zhang

Jiangang Hao Chen Li

Journal of Educational Measurement Vol. 63 No. 1 · Wiley

View at Publisher Save 10.1111/jedm.12431

Abstract

Keystroke analysis has often been used for security purposes, most often to authenticate users and identify impostors. This paper examines the use of keystroke analysis to distinguish between the behavior of writers who are composing an original text, vs. copying or otherwise reproducing a non‐original texts. Recent advances in text generation using large language models makes the use of behavioral cues to identify plagiarism more pressing, since users seeking an advantage on a writing assessment may be able to submit unique AI‐generated texts. We examine the use of keystroke log analysis to detect non‐original text under three conditions: a laboratory study, where participants were either copying a known text or drafting an original essay, and two studies from operational assessments, where it was possible to identify essays that were non‐original by refernece to their content. Our results indicate that it is possible to achieve accuracies inexcess of 94% under ideal conditions where the nature of each writing sessionis known in advance, and greater than 89% in operational conditions where proxies for non‐original status, such as similarity to other submitted essays, must be used.

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Published: Apr 03, 2025
Vol/Issue: 63(1)
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Cite This Article

Paul Deane, Mo Zhang, Jiangang Hao, et al. (2025). Using Keystroke Dynamics to Detect Nonoriginal Text. Journal of Educational Measurement, 63(1). https://doi.org/10.1111/jedm.12431

Using Keystroke Dynamics to Detect Nonoriginal Text

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