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Zusammenfassungen
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.
Von Mike Perkins, Jasper Roe, Binh H. Vu, Darius Postma, Don Hickerson, James McGaughran, Huy Q. Khuat im Text GenAI Detection Tools Adversarial Techniques and Implications for Inclusivity in Higher Education (2024) 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.
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Personen KB IB clear | Alla Anohina-Naumeca , Sonja Bjelobaba , Tomáš Foltýnek , Jean Guerrero-Dib , Olumide Popoola , Petr Šigut , Lorna Waddington , Debora Weber-Wulff | ||||||||||||||||||
Begriffe KB IB clear | bias , Bildungeducation (Bildung) , Chat-GPT , ContentContent , digital dividedigital divide , false positive rate , Fremdsprachesecond language , Generative Machine-Learning-Systeme (GMLS)computer-generated text , GMLS-Detektor , GPT Zero , Internetinternet , Künstliche Intelligenz (KI / AI)artificial intelligence , Lernenlearning , machine learning , Originality.AI , prompt engineering , Schreibenwriting , Textgeneratoren & Bildung , Textgeneratoren & Hochschule , wissenschaftliches Schreibenscientific writing | ||||||||||||||||||
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