Personalized Education Recommendation through Reasoning-Aware Language Models
Keywords:
personalized learning; language models; reasoning; recommendation systems; educational technology; governanceAbstract
Personalized education recommendation has long been constrained by the difficulty of modeling learner cognition, prerequisite relationships, and pedagogical intent within a single adaptive framework. The emergence of large language models offers new capacities for representing learner states and generating explanatory recommendations, but recommendation quality depends heavily on the model's ability to reason over incomplete educational histories, heterogeneous resources, and long-term learning goals. This paper presents a system-level analysis of reasoning-aware language models for personalized education recommendation. It examines architectural trade-offs between response quality, computational cost, and institutional governance, and discusses the integration of reasoning traces into recommendation pipelines. The analysis emphasizes structural rather than algorithmic details, focusing on data infrastructure, fairness, deployment sustainability, robustness, and policy implications. It argues that reasoning-aware models should be understood not as isolated predictors but as components of broader socio-technical infrastructures that require explicit governance, model documentation, and operational accountability. Drawing on cross-domain perspectives from intelligent tutoring, learning analytics, and responsible artificial intelligence, the paper provides a forward-looking research agenda for designing education recommendation systems that are explainable, fair, and institutionally sustainable.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
