  This study investigates the efficacy of six major Generative AI (GenAI) text
detectors when confronted with machine-generated content that has been modified
using techniques designed to evade detection by these tools (n=805). The
results demonstrate that the detectors' already low accuracy rates (39.5%) show
major reductions in accuracy (17.4%) when faced with manipulated content, with
some techniques proving more effective than others in evading detection.
  The accuracy limitations and the potential for false accusations demonstrate
that these tools cannot currently be recommended for determining whether
violations of academic integrity have occurred, underscoring the challenges
educators face in maintaining inclusive and fair assessment practices. However,
they may have a role in supporting student learning and maintaining academic
integrity when used in a non-punitive manner.
  These results underscore the need for a combined approach to addressing the
challenges posed by GenAI in academia to promote the responsible and equitable
use of these emerging technologies. The study concludes that the current
limitations of AI text detectors require a critical approach for any possible
implementation in HE and highlight possible alternatives to AI assessment
strategies.
