We present a framework for evaluating adaptive personalization of educational reading materials with theory-grounded simulated learners. Unlike typical intelligent tutoring systems that adapt questions or feedback, we treat reading as the primary intervention and use question answering only as an observation channel for Bayesian Knowledge Tracing (BKT). This enables controlled comparison of LLM-powered adaptive and non-adaptive reading policies before classroom deployment.The framework links open educational content to a shared ontology of learning objectives and knowledge components, which is used to generate aligned reading–assessment pairs targeting one objective at a time. Simulated learners update their knowledge through a comprehension-and-memory process that models encoding, integration with prior knowledge, and misconception revision.The learner model combines established theories of reading with constrained answer selection, ensuring responses are generated only from information the learner has plausibly retained. Together, these components provide an interpretable offline testbed for studying whether adaptive reading improves learning outcomes.