  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 and assisted writing. The
outcomes of this study enhance our understanding of how users interact with
generative AI and have implications for improving the reliability of digital
educational platforms.
