TL;DR
Evaluating the effectiveness of existing systems for identifying AI-generated text in scientific papers calls into question the objectivity of current tools and emphasizes the need to develop specialized methods adapted to the scientific style of writing.
It is argued that automated detection should not replace expert linguistic analysis but should serve as an auxiliary tool for evaluating the probable origin of a text in media linguistics, fact-checking, and educational practic.
Aim. To develop a database structure for determining connotative characteristics of news text. Methodology. The key research methods are modeling, experimentation, and the author’s method of professionally oriented programming. The procedure includes design, writing, and debugging the programming code. R...
This paper compares the effectiveness of selected publicly available tools for detecting AI-generated text: GPTZero AI Detector, ZeroGPT AI Detector, Copyleaks AI Detector, and Scribbr AI Detector. The experiment included 150 text samples in Polish and English, divided into human-written texts, AI-generated texts, and...
The proliferation of AI text generation tools in educational settings has created unprecedented challenges for academic integrity. As AI-generated content grows more sophisticated, traditional assessment methods may no longer suffice to distinguish human from machine-authored work. This mixed-methods study compared hum...
The review shows that current detection approaches face several challenges, including a shortage of suitable datasets, ambiguous labels for human–AI collaborative text, insufficient coverage of generative models, limited generalization, and inadequate interpretability.
This study investigates the register variation in texts written by humans and comparable texts produced by large language models (LLMs). Multidimensional analysis (MDA) is applied to a sample of human-written texts and AI-generated counterpart texts to find the dimensions of variation in which LLMs differ most si...
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