  Item Response Theory (IRT) is a well known method for assessing responses
from humans in education and psychology. In education, IRT is used to infer
student abilities and characteristics of test items from student responses.
Interactions with students are expensive, calling for methods that efficiently
gather information for inferring student abilities. Methods based on Optimal
Experimental Design (OED) are computationally costly, making them inapplicable
for interactive applications. In response, we propose incorporating amortised
experimental design into IRT. Here, the computational cost is shifted to a
precomputing phase by training a Deep Reinforcement Learning (DRL) agent with
synthetic data. The agent is trained to select optimally informative test items
for the distribution of students, and to conduct amortised inference
conditioned on the experiment outcomes. During deployment the agent estimates
parameters from data, and suggests the next test item for the student, in close
to real-time, by taking into account the history of experiments and outcomes.
