- A UF engineering professor says commercially available tools used to detect AI-generated text in scientific literature are ineffective.
- Recent media reports indicate a growing problem concerning AI-generated text, or AIGT, in scholarly publications.
- UF’s Patrick Traynor, Ph.D., and colleagues examined the efficacy of AIGT detectors and found they are “poorly suited for deployment in academic or high-stakes contexts.”
Patrick Traynor, Ph.D., has questions.
When the professor and interim chair of the University of Florida Department of Computer & Information Science & Engineering saw reports in the media positing that scientific literature is increasingly being generated by artificial intelligence, he wondered, “How do they know?”
Traynor knows the detectors that determine the presence of AI-generated text, known as AIGT, in publications are themselves AI systems. They use the same large language models, called LLMs, that less than honest researchers could be using to generate their text.
How good could they be?
Spoiler alert: They’re not very good.
In a paper to be presented at this week’s 2026 IEEE Symposium on Security and Privacy, Traynor and his co-authors assert that current AIGT detectors are not effective or robust tools for determining the presence of AI-generated text. The results, the researchers said, indicate that commercially available AIGT detectors are “poorly suited for deployment in academic or high-stakes contexts.”