ChatGPT Texts Detectability - The Model Consistently Identifies AI-Generated Plagiarism


AI, with its ever-expanding capabilities, has found its way into various aspects of our lives, and this includes the concerning issue of plagiarism. In recent developments, a program developed at the University of Kansas has claimed to have made significant strides in detecting AI-generated texts and reliably unmasking them. This achievement is a substantial leap forward compared to previous efforts in distinguishing artificially created content from human-written text. Let's delve into the details of this groundbreaking development.

Understanding AI and Plagiarism

Artificial Intelligence (AI) is a powerful technology capable of performing various tasks, and it's not exempt from being misused for plagiarism. It's essential to address this issue as AI-generated texts become more prevalent in online content.

The University of Kansas Breakthrough

At the forefront of this endeavor is the University of Kansas, where a team of researchers has made a remarkable achievement. This development, published on November 6, 2023, on sciencedirect.com, focuses on detecting AI-generated content, specifically in scientific articles. Their system claims to reliably flag artificially created scientific articles.

The Challenge of Detecting AI-Generated Texts

Identifying AI-generated texts is a complex task. Articles generated by systems like ChatGPT aim to mimic human writing, making it challenging to distinguish them from genuine human-authored content. Previous attempts at automatic detection had a success rate well below 50%, leaving room for significant improvement.

The Remarkable 99% Detection Rate

What sets this program from the University of Kansas apart is its impressive detection rate. According to the authors, out of 200 texts compared, 198 were correctly identified as AI-generated. This equates to an astounding 99% detection rate, a significant leap from the previous detection rates.

Factors Behind the Reliability

The success of this system can be attributed to several factors that enhance its reliability:

1. Text Features

The system relies on 20 distinct text features, including variable sentence length, the typical occurrence of certain words, and punctuation usage. These features enable precise differentiation between AI-generated content and human-written text.

2. Training with Scientific Texts

Extensive training with scientific texts, particularly in the field of chemistry, contributes to the system's accuracy. This specialization allows it to excel in identifying artificially generated scientific articles.

3. Focus on Specific Subject Areas

By concentrating on one subject area, in this case, chemistry, the system optimizes its performance. The classic structure and language of scientific texts, combined with this narrow focus, enhance the overall reliability of the system.

The Promise of Enhanced Detection

While this system has shown remarkable success in the domain of scientific articles, it's worth noting that its performance in other contexts, such as news articles, remains a challenge. In such cases, the detector had limited success in identifying artificially created content.

Potential for Text Analysis in Specific Subject Areas

Despite these challenges, the achievement of a 99% detection rate in specific subject areas like chemistry is a promising development. It demonstrates the potential for enhancing text analysis tools for specific domains, allowing for more accurate identification of AI-generated content.


The ability to reliably detect AI-generated texts, especially in scientific articles, is a significant step in addressing the issue of plagiarism. The program developed by the University of Kansas, with its 99% detection rate, sets a new standard for AI detection. While challenges remain in different subject areas, the promise of text analysis tools continues to grow. As AI evolves, so does our ability to discern its contributions from genuine human work.

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