*AI was most definitely used in writing this article
Last week I received a frantic call from a Master’s student in Austria who was inconsolable. He had just submitted his thesis to his university for review and it had been flagged as being written by AI. The university had given him one more chance to revise and resubmit his work. If it passed the AI detection tool then they would review the work and give him a final grade. If it failed the automated check, then it would be automatically rejected and he would be dishonorably kicked out of his program with two years of study going down the drain.
In the popular press, online and in the scientific press there has been much hyperbolic and hysterical commentary about the power of LLMs, artificial intelligence and the potential impact on student learning and writing. We have seen a rush of lauded detectors that can apparently discern whether an artificial intelligence system has produced a paper. As highlighted by this piece published by Scholarly Kitchen, there is a very real risk of false positives where an entirely innocent student is accused of cheating using an artificial intelligence system. This is a risk institutions and publishers need to take seriously and recognise that a detection system is probably not infallible.
The recent surge in the development of AI technologies in the realm of writing has led to the rise and proliferation of AI detectors in the academic world. These detectors promise to be the gatekeepers of academic integrity by combating plagiarism and AI-generated content. While the ambition is noble, their practical implementation has seen its fair share of critical shortcomings.
The fundamental assumption underlying the creation of AI detection tools seems to be that AI writing should be able to be detected the same way that plagiarism is detected. However, there is a critical distinction: plagiarism simply looks for exact matches with existing works, an objective criterion that can be identified, measured, and replicated. AI writing, on the other hand, is original in its own right (even if drawn from unoriginal sources), and can’t be easily traced to its source.
My opposition to scholarly publishers relying on detection tools stems from both pragmatic and ideological reasons. Let’s start with some of the pragmatic issues.
