Year: 2026
Type: conference-paper
Abstract: 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 Wikibooks 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, KRECstyle 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.Under this explicit simulator parameterization and three sampled subject ontologies, 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.
Authors: Ryan Woo, Anmol Rao, Aryan Keluskar, Yinong Chen
Institution: Arizona State University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W7166840725