Keystroke Dynamics Against Academic Dishonesty in the Age of LLMs
Debnath Kundu, Atharva Mehta, Rajesh Kumar, Naman Lal, Avinash Anand,, Apoorv Singh, Rajiv Ratn Shah

TL;DR
This paper introduces a keystroke dynamics-based method to detect academic dishonesty involving AI-assisted writing, achieving up to 85.72% accuracy, thereby improving integrity verification in online education.
Contribution
It presents a novel keystroke pattern analysis approach using a modified TypeNet architecture to distinguish genuine from AI-assisted writing in academic settings.
Findings
Keystroke dynamics differ significantly between genuine and assisted writing.
The proposed detector achieved up to 85.72% accuracy in condition-specific scenarios.
The study provides insights into user-AI interaction patterns in educational contexts.
Abstract
The transition to online examinations and assignments raises significant concerns about academic integrity. Traditional plagiarism detection systems often struggle to identify instances of intelligent cheating, particularly when students utilize advanced generative AI tools to craft their responses. This study proposes a keystroke dynamics-based method to differentiate between bona fide and assisted writing within academic contexts. To facilitate this, a dataset was developed to capture the keystroke patterns of individuals engaged in writing tasks, both with and without the assistance of generative AI. The detector, trained using a modified TypeNet architecture, achieved accuracies ranging from 74.98% to 85.72% in condition-specific scenarios and from 52.24% to 80.54% in condition-agnostic scenarios. The findings highlight significant differences in keystroke dynamics between genuine…
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Taxonomy
TopicsAcademic integrity and plagiarism · Legal Education and Practice Innovations · Law, AI, and Intellectual Property
