We present a framework for evaluating adaptive personalization of educational reading materials with theory-grounded simulated learners. The system builds a learning-objective and knowledge-component ontology from open textbooks, curates it in a browser-based Ontology Atlas, labels textbook chunks with ontology entities, and generates aligned reading-assessment pairs. Simulated readers learn from passages through a Construction-Integration-inspired memory model with DIME-style reader factors, KREC-style misconception revision, and an open New Dale-Chall readability signal. Answers are produced by score-based option selection over the learner's explicit memory state, while BKT drives adaptation. Across three sampled subject ontologies and matched cohorts of 50 simulated learners per condition, adaptive reading significantly improved outcomes in computer science, yielded smaller positive but inconclusive gains in inorganic chemistry, and was neutral to slightly negative in general biology.

翻译：我们提出了一种基于理论驱动的模拟学习者来评估教育阅读材料自适应个性化的框架。该系统从开放教科书中构建学习目标和知识组件本体，在基于浏览器的本体图谱中进行整理，用本体实体标记教科书章节，并生成对齐的阅读-评估配对。模拟读者通过一个基于建构-整合启发的记忆模型来学习段落，该模型融合了DIME风格的读者因素、KREC风格的误区修正以及开放式新戴尔-查尔可读性信号。答案通过基于分数的选项选择从学习者的显式记忆状态中生成，而BKT驱动自适应过程。在三个采样的学科本体和每条件下50名模拟学习者的匹配队列中，自适应阅读显著改善了计算机科学的学习成果，在无机化学中产生了较小但非决定性的正向增益，而在普通生物学中则表现为中性至略为负面。