This would be one approach. However devils advocate here - I took an essay question verbatim from a humanities assessed exercise, and very quickly (think secon

This would be one approach. However devils advocate here - I took an essay question verbatim from a humanities assessed exercise, and very quickly (think seconds of effort) created a prompt and got Chat GPT to write text each time it was prompted, until told to stop. I copied the output (which I'd requested references to be included in), and gave it to the professor who set the question. They very quickly confirmed the language model essay was considerably better than their human students' essays. It was more coherent and readable, better answered the question set, was more "on topic", and even better structured - it followed a structure that introduced a position, then substantiated the position, gave some mention to alternative viewpoints, and compared and justified the position in relation to them. The topic wasn't hard, you could easily Google copious essays on the topic, but the output wasn't pulled from the web verbatim (even in parts), and it was better than the actual students' work! The biggest failing was the references - language models generate plausible and readable text... So it invented lots of very interesting and plausible sounding references, none of which existed. The titles sounded relevant. The authors were appropriate to the subject matter, the journals or publication venues were also appropriate and relevant. It's just that none of them actually existed!