We present a recent randomized field trial delivered in Carnegie Learning's MATHia's intelligent tutoring system to a sample of 12,374 learners intended to test whether rewriting content in a selection of so-called "word problems" improves student mathematics performance within this content, especially among students who are emerging as English language readers. In addition to describing facets of word problems targeted for rewriting and the design of the experiment, we present an artificial intelligence-driven approach to evaluating the effectiveness of the rewrite intervention for a sub-population of learners of interest. We hypothesize that the intervention may be especially effective to emerging readers using MATHia. Data about students' reading ability is generally neither collected nor available to MATHia's developers. Instead, we rely on a recently developed neural network predictive model that infers whether a student is an emerging reader. We present the results of the intervention on a variety of performance metrics in MATHia and compare performance of the intervention group to the entire user base of MATHia, as well as by comparing likely emerging readers to those who are not inferred to be emerging readers. We conclude with areas for future work using these kinds of more comprehensive models of learners. [This paper was published in: "The 24th International Conference on Artificial Intelligence in Education (AIED 2023): Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky," 2023.]