Detecting LLM-Assisted Academic Dishonesty using Keystroke Dynamics
For educators and institutions, this provides a behavioral detection method that addresses the limitations of text-based plagiarism detectors against AI-assisted writing.
This paper extends prior work on detecting AI-assisted academic dishonesty using keystroke dynamics, expanding the dataset to 130 participants and introducing a paraphrasing condition. Keystroke-based models significantly outperform text-only detectors in practical settings, achieving higher accuracy in distinguishing genuine from assisted writing.
The rapid adoption of generative AI tools has heightened concerns regarding academic integrity, as students increasingly engage in dishonest practices by copying or paraphrasing AI-generated content. Existing plagiarism detection systems, which rely primarily on text-intrinsic features, are ineffective at identifying AI-assisted or paraphrased submissions. Our prior conference work introduced a behavioral detection approach that leverages how text is produced, captured through keystroke dynamics, in addition to what is written, enabling discrimination between genuine and assisted writing. That study, conducted on keystroke data from 40 participants, demonstrated promising performance. This paper substantially extends and systemizes the prior work by: (1) expanding the dataset with 90 additional participants and introducing an explicit paraphrasing condition to model realistic plagiarism strategies; (2) formalizing a threat model and evaluating detection under adversarial and deception-oriented scenarios; and (3) performing a comprehensive empirical comparison against state-of-the-art text-only detectors and human evaluators. Experimental results demonstrate that keystroke-based models significantly outperform text-based approaches in practical deployment settings, while revealing limitations under more challenging adversarial conditions.