Generating and Evaluating Tests for K-12 Students with Language Model
  Simulations: A Case Study on Sentence Reading Efficiency
Eric Zelikman Wanjing Anya Ma Jasmine E. Tran Diyi Yang Jason D. Yeatman Nick Haber
Developing an educational test can be expensive and time-consuming, as each
item must be written by experts and then evaluated by collecting hundreds of
student responses. Moreover, many tests require multiple distinct sets of
questions administered throughout the school year to closely monitor students'
progress, known as parallel tests. In this study, we focus on tests of silent
sentence reading efficiency, used to assess students' reading ability over
time. To generate high-quality parallel tests, we propose to fine-tune large
language models (LLMs) to simulate how previous students would have responded
to unseen items. With these simulated responses, we can estimate each item's
difficulty and ambiguity. We first use GPT-4 to generate new test items
following a list of expert-developed rules and then apply a fine-tuned LLM to
filter the items based on criteria from psychological measurements. We also
propose an optimal-transport-inspired technique for generating parallel tests
and show the generated tests closely correspond to the original test's
difficulty and reliability based on crowdworker responses. Our evaluation of a
generated test with 234 students from grades 2 to 8 produces test scores highly
correlated (r=0.93) to those of a standard test form written by human experts
and evaluated across thousands of K-12 students.