What are your training sample sizes? What about test? How was your data compiled? Labeled? What ai models? What were the sources of human writing?

Also, one more thing, at the end of the day there is no way to prove either way without having students record their screens and entire rooms (or only do in person) when writing papers

I did not have time to try this specific one but I have tried at least 10 others. Sorry, not trying to be negative or anything. They’re are just tons of different models, each of which would need a separate detection model. The model was trained on human writing, so it’s bound to have humanistic sound, and some humans are bound to have a writing voice similar to the output of AI content creators. There is also no real standard ‘human’ way of writing to clearly separate the two. Combine that with the difference in results based on the prompt and it quickly becomes an insurmountable task in my opinion.

At the end of the day, I applaud your efforts, truly but realistically I think your model is significantly overfit to a very small percentage of possible samples, both AI and human generated.

Too many false positives and ways to trick it, this has been proven over and over again

Not necessarily depending on how much you want to tweak the algorithms. There are many no- or low-code tools available with basic algorithms available, such as Alteryx.

There are different methods behind recommenders. Some are content based, such as the user watched a movie with will Ferrell, show more movies with Will Ferrell. Others are based on user behavior, such as other users who watched this will Ferrell movie also watched these movies.

Edit: here is a good overview I found https://towardsdatascience.com/recommendation-systems-explained-a42fc60591ed

It’s a recommendation system, same basic premise as what is used for Spotify and Netflix

Yes hahaha