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Item Response Theory (IRT) is a well known method for assessing responsesfrom humans in education and psychology. In education, IRT is used to inferstudent abilities and characteristics of test items from student responses.Interactions with students are expensive, calling for methods that efficientlygather information for inferring student abilities. Methods based on OptimalExperimental Design (OED) are computationally costly, making them inapplicablefor interactive applications. In response, we propose incorporating amortisedexperimental design into IRT. Here, the computational cost is shifted to aprecomputing phase by training a Deep Reinforcement Learning (DRL) agent withsynthetic data. The agent is trained to select optimally informative test itemsfor the distribution of students, and to conduct amortised inferenceconditioned on the experiment outcomes. During deployment the agent estimatesparameters from data, and suggests the next test item for the student, in closeto real-time, by taking into account the history of experiments and outcomes.

Amortised Design Optimization for Item Response Theory