Memory-Enhanced Large Language Models for Personalized Educational Resource Recommendation
Keywords:
memory-enhanced large language models; personalized educational recommendation; retrieval-augmented generation; learner modeling; fairness; governance; educational technologyAbstract
The application of large language models to educational resource recommendation introduces a fundamental tension between broad reasoning capabilities and persistent learner-specific contextualization. Static models cannot reliably retain fine-grained learner histories across sessions, while conventional recommender systems often lack the semantic flexibility required to map evolving learning goals to heterogeneous educational content. This paper examines memory-enhanced large language models as a system-level response to this tension. It analyzes architecture designs that integrate episodic, semantic, and procedural memory stores with retrieval-augmented generation and conversational recommendation interfaces. The discussion emphasizes structural trade-offs between memory granularity, latency, privacy, and interpretability. The paper further examines governance requirements arising from the use of learner data in persistent memory systems, including consent, data minimization, bias auditing, and regulatory alignment. It argues that memory-enhanced language models can improve pedagogical alignment only when memory update, retrieval, and deletion mechanisms are designed as governable socio-technical processes rather than as purely technical optimizations. A forward-looking research agenda is presented spanning evaluation, sustainability, and institutional deployment.
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