More than a decade ago I worked as a research programmer on a similar AI tutoring product for hierarchical skills like mathematics at Carnegie Mellon (in conjunction with the Pittsburgh Science of Learning Center and Carnegie Learning).

The system would prompt students with problems that incorporated dozens of sub-skills, each of which incorporated other sub-skills and so on. If the student gets the problem right, all requisite sub-skills are marked as reasonably strong. If they get it wrong then the statistical model concludes that at least one sub-skill must be weak/faulty/missing, and when selecting subsequent problems the system would incorporate problems with different subsets of the original sub-skills to identify the missing sub-skill(s), with the goal being to spend time reinforcing only those skills that the student has not mastered.

I no longer work in that domain, but I recall it being amazingly efficient -- it often only took a dozen questions to assess a student's understanding of thousands of skills.