The interactive reading task: Transformer-based automatic item generation
Yigal Attali, Andrew Runge, Geoffrey T. LaFlair, Kevin Yancey, Sarah Goodwin, Yena Park, Alina A. von Davier
Abstract
Automatic item generation (AIG) has the potential to greatly expand the number of items for educational assessments, while simultaneously allowing for a more construct-driven approach to item development. However, the traditional item modeling approach in AIG is limited in scope to content areas that are relatively easy to model (such as math problems), and depends on highly skilled content experts to create each model. In this paper we describe the interactive reading task, a transformer-based deep language modeling approach for creating reading comprehension assessments. This approach allows a fully automated process for the creation of source passages together with a wide range of comprehension questions about the passages. The format of the questions allows automatic scoring of responses with high fidelity (e.g., selected response questions). We present the results of a large-scale pilot of the interactive reading task, with hundreds of passages and thousands of questions. These passages were administered as part of the practice test of the Duolingo English Test. Human review of the materials and psychometric analyses of test taker results demonstrate the feasibility of this approach for automatic creation of complex educational assessments.