It is well documented that reading strategies of low-literacy readers are suboptimal when text requires deeper levelsof comprehension. Deep comprehension demands causal or goal-oriented reasoning and functional conceptual knowledge.Alternatively, shallow comprehension entails recall of definitions or text features without necessitating a coherent understandingof the text. The Center for Adult Literacy (CSAL) AutoTutor is an interactive intelligent tutoring system designed to foster deepand shallow comprehension in low-literacy readers. The present work represents the first empirical study of the effect of CSALAutoTutor on comprehension type in low- and high-literacy readers. Community members and students interacted with CSALAutotutor and then were assessed on recall ability for the structure (shallow) and meaning (deep) of sentences from lessontext. Preliminary analysis suggests CSAL AutoTutor promotes comparable deep level comprehension in low- and high-literacyreaders. Implications for CSAL AutoTutor as a literacy intervention and future goals are discussed.

Gradient Boosting Classification (GBC) models are well known to machine learning and artificial intelligence.Having the ability to predict user performance is imperative to the outcomes and purpose of an intelligent tutoring system.The Center for the Study of Adult Literacy (CSAL) intelligent tutoring system aims to improve reading comprehension inlow-literacy adult learners. A GBC was applied to preliminary data gathered from high-literacy adult readers (N =1800 ob-servations). Our model was shown high accuracy in predicting users’ correct/incorrect responses to our multiple choice items.Specifically, users’ reaction times and order of question presentation are important features of the model to consider. Lessimportant features are difficulty of the item and the users reading ability. Our next steps are to apply GBC to high-literacycollege students, followed by low-literacy readers, as a test set. Our eventual goal is to predict correctness prior to scoring.