Real-life scoring scenarios often fall somewhere in between the supervised and the unsupervised case: some part of the data is typically unlabeled and should be labeled by the tool (and afterwards potentially re-checked by a teacher).
we assume that scenarios where a model is to be trained on existing labeled training data and applied on new unlabeled test-data will occur frequently; for example, when the same or similar prompts have already been used and scored in an earlier exam and are now administered to a new cohort of students.