Testing of detection tools for AI-generated text
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F23%3A00132774" target="_blank" >RIV/00216224:14330/23:00132774 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/article/10.1007/s40979-023-00146-z" target="_blank" >https://link.springer.com/article/10.1007/s40979-023-00146-z</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s40979-023-00146-z" target="_blank" >10.1007/s40979-023-00146-z</a>
Alternative languages
Result language
angličtina
Original language name
Testing of detection tools for AI-generated text
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal for Educational Integrity
ISSN
1833-2595
e-ISSN
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Volume of the periodical
19
Issue of the periodical within the volume
26
Country of publishing house
DE - GERMANY
Number of pages
39
Pages from-to
1-39
UT code for WoS article
001129231700001
EID of the result in the Scopus database
2-s2.0-85180443619