Evaluating Adaptive Personalization of Educational Readings with Simulated Learners

2026-04-17

AI Summary

This study addresses the challenge of effectively evaluating the adaptive personalization of educational reading materials in the absence of large-scale real learner data. The authors propose a theory-driven simulated learner framework that integrates, for the first time, the Construction-Integration memory model, DIME reader characteristics, the KREC misconception correction mechanism, and the New Dale-Chall readability metric, further incorporating knowledge ontologies and Bayesian Knowledge Tracing (BKT) to enable dynamic content adaptation. This approach allows for the evaluation of adaptive reading systems without requiring human participants. Experimental results demonstrate significantly improved learning outcomes in computer science, a small positive (though statistically non-significant) trend in inorganic chemistry, and neutral to slightly negative effects in general biology.

Technology Category

Cognitive Modeling & Cognitive Systems: Adaptive BehaviorData Mining & Knowledge Management: Conversational Systems for Recommendation & RetrievalMachine Learning: Learning Preferences or Rankings

Application Category

User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating successSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingSemantics and Knowledge: Applications of semantic technologies for improving search, browsing, recommendation, personalization

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 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.

Problem

• adaptive personalization
• educational readings
• simulated learners
• learning outcomes
• ontology

Innovation

• adaptive personalization
• simulated learners
• knowledge-component ontology
• Construction-Integration model
• Bayesian Knowledge Tracing