Not all of AI works on correlation, Bayesian Belief Networks are built around the probability that A causes B.
I'm working on a system to estimate the performance of students on questions based on their past performances.
I don't think you need causation for this. A past performance does not cause a current performance. Answering on an early question does not cause an answer on a later question.
But from the point of view of just building a system to pick questions that are likely to be of the appropriate difficulty level - does this distinction have any importance?
No, not for your example. I think correlation (or even simple extrapolation) would solve your problem very well. Assign a difficulty score to each of the questions and then feed questions to the students in increasingly difficult levels (which is how most exams work) and then when the student starts getting them wrong, you can wind back the difficulty. That's a feedback algorithm that is similar to the error minimisation performed on a neuron in a multi-layered perceptron. The non-trivial piece of input spaces like this is deciding what a difficult question is!
A better example of causation in AI would be:
My car is slowing down. My accelerator is on the floor. There is not much noise. There are lights on the dashboard. What is the probability that I've run out of fuel?
In this case, running out of fuel has caused the car to slow down. This is precisely the sort of problem that Bayesian Belief Networks solve.