That could work but that is changing the problem and moving the goal posts, a plagiarism detection system that is essentially trained on individual authors would be able to identify any time they skew too far from their rolling average.
I’m not even sure if ML is absolutely necessary for this or not.
The main failure mode I see here is students dramatically improving and throwing the system off. If someone gets a tutor or goes to writing workshops, you don’t want to accuse them of plagiarism just because they got better. But there may be ways you could deal with that, like having the student submit new samples.