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@article{2355979,
author = {WeberandWulff, Debora and AnohinaandNaumeca, Alla and Bjelobaba, Sonja and Foltýnek, Tomáš and GuerreroandDib, Jean and Popoola, Olumide and Šigut, Petr and Waddington, Lorna},
article_number = {26},
doi = {http://dx.doi.org/10.1007/s40979-023-00146-z},
keywords = {Artifcial intelligence; Generative pre-trained transformers; Machine-generated text; Detection of AI-generated text; Academic integrity; ChatGPT; AI detectors},
language = {eng},
issn = {1833-2595},
journal = {International Journal for Educational Integrity},
title = {Testing of detection tools for AI-generated text},
url = {https://link.springer.com/article/10.1007/s40979-023-00146-z},
volume = {19},
year = {2023}
}
TY - JOUR
ID - 2355979
AU - Weber-Wulff, Debora - Anohina-Naumeca, Alla - Bjelobaba, Sonja - Foltýnek, Tomáš - Guerrero-Dib, Jean - Popoola, Olumide - Šigut, Petr - Waddington, Lorna
PY - 2023
TI - Testing of detection tools for AI-generated text
JF - International Journal for Educational Integrity
VL - 19
IS - 26
SP - 1-39
EP - 1-39
SN - 18332595
KW - Artifcial intelligence
KW - Generative pre-trained transformers
KW - Machine-generated text
KW - Detection of AI-generated text
KW - Academic integrity
KW - ChatGPT
KW - AI detectors
UR - https://link.springer.com/article/10.1007/s40979-023-00146-z
N2 - Recent advances in generative pre-trained transformer large language models have emphasised the potential risks of unfair use of artifcial intelligence (AI) generated content in an academic environment and intensifed eforts in searching for solutions to detect such content. The paper examines the general functionality of detection tools for AI-generated text and evaluates them based on accuracy and error type analysis. Specifcally, the study seeks to answer research questions about whether existing detection tools can reliably diferentiate between human-written text and ChatGPTgenerated text, and whether machine translation and content obfuscation techniques afect the detection of AI-generated text. The research covers 12 publicly available tools and two commercial systems (Turnitin and PlagiarismCheck) that are widely used in the academic setting. The researchers conclude that the available detection tools are neither accurate nor reliable and have a main bias towards classifying the output as human-written rather than detecting AI-generated text. Furthermore, content obfuscation techniques signifcantly worsen the performance of tools. The study makes several signifcant contributions. First, it summarises up-to-date similar scientific and non-scientifc eforts in the feld. Second, it presents the result of one of the most comprehensive tests conducted so far, based on a rigorous research methodology, an original document set, and a broad coverage of tools. Third, it discusses the implications and drawbacks of using detection tools for AI-generated text in academic settings.
ER -
WEBER-WULFF, Debora, Alla ANOHINA-NAUMECA, Sonja BJELOBABA, Tomáš FOLTÝNEK, Jean GUERRERO-DIB, Olumide POPOOLA, Petr ŠIGUT a Lorna WADDINGTON. Testing of detection tools for AI-generated text. \textit{International Journal for Educational Integrity}. 2023, roč.~19, č.~26, s.~1-39. ISSN~1833-2595. Dostupné z: https://dx.doi.org/10.1007/s40979-023-00146-z.
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