This paper studies the problem of automatically adjusting the difficulty level of educational exercises to facilitate learning. Previous work on this topic either relies on large datasets or requires multiple interactions before it adjusts properly. Although this is sufficient for large-scale online courses, there are also scenarios where students are expected to only work through a few trials. In these cases, the adjustment needs to respond to only a few data points. To accommodate this, we propose a novel difficulty adjustment method that requires less data and adapts faster. Our proposed method refits an existing item response theory model to work on smaller datasets by generalizing based on attributes of the exercises. To adapt faster, we additionally introduce a discount value that weakens the influence of past interactions. We evaluate our proposed method on simulations and a user study using an example graph theory lecture. Our results show that our approach indeed succeeds in adjusting to learners quickly.