This study builds upon previous work aimed at developing a student model of reading comprehension ability within the intelligent tutoring system, iSTART. Currently, the system evaluates students' self-explanation performance using a local, sentence-level algorithm and does not adapt content based on reading ability. The current study leverages natural language processing tools to build models of students' comprehension ability from the linguistic properties of their self-explanations. Students (n = 126) interacted with iSTART across eight training sessions where they self-explained target sentences from complex science texts. Coh-Metrix was then used to calculate the linguistic properties of their aggregated self-explanations. The results of this study indicated that the linguistic indices were predictive of students' reading comprehension ability, over and above the current system algorithms. These results suggest that natural language processing techniques can inform stealth assessments and ultimately improve student models within intelligent tutoring systems.