  This paper explores the contributions of Answer Set Programming (ASP) to the
study of an established theory from the field of Second Language Acquisition:
Input Processing. The theory describes default strategies that learners of a
second language use in extracting meaning out of a text, based on their
knowledge of the second language and their background knowledge about the
world. We formalized this theory in ASP, and as a result we were able to
determine opportunities for refining its natural language description, as well
as directions for future theory development. We applied our model to automating
the prediction of how learners of English would interpret sentences containing
the passive voice. We present a system, PIas, that uses these predictions to
assist language instructors in designing teaching materials. To appear in
Theory and Practice of Logic Programming (TPLP).

Artificial Intelligence