80% accuracy could mean 0 false negatives and 20% false positives.
My point is that accuracy is a terrible metric here and sensitivity, specificity tell us much more relevant information to the task at hand. In that formulation, a specificity < 1 is going to have false positives and it isn't fair to those students to have to prove their innocence.
That's more like the false positive rate and false negative rate.
If we're being literal, accuracy is (number correct guesses) / (total number of guesses). Maybe the folks at turnitin don't actually mean 'accuracy', but if they're selling an AI/ML product they should at least know their metrics.