  Intelligent and adaptive online education systems aim to make high-quality
education available for a diverse range of students. However, existing systems
usually depend on a pool of hand-made questions, limiting how fine-grained and
open-ended they can be in adapting to individual students. We explore targeted
question generation as a controllable sequence generation task. We first show
how to fine-tune pre-trained language models for deep knowledge tracing
(LM-KT). This model accurately predicts the probability of a student answering
a question correctly, and generalizes to questions not seen in training. We
then use LM-KT to specify the objective and data for training a model to
generate questions conditioned on the student and target difficulty. Our
results show we succeed at generating novel, well-calibrated language
translation questions for second language learners from a real online education
platform.
