GenAI Detection Tools, Adversarial Techniques and Implications for Inclusivity in Higher Education
It addresses the problem of unreliable AI detection tools for educators in higher education, highlighting their limitations for fair assessment, but notes it is incremental as it builds on existing concerns about such tools.
This study tested six Generative AI text detectors against manipulated machine-generated content and found their accuracy dropped from 39.5% to 17.4% when faced with adversarial techniques, showing they are unreliable for detecting academic integrity violations.
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.