Towards Personalized AI Education: Context‐Aware Retrieval‐Augmented Generation With Grade‐Level LLM Adaptation

ABSTRACT While retrieval‐augmented generation (RAG) systems have substantially improved the factual accuracy of Large Language Models (LLMs) in educational contexts, they exhibit a fundamental limitation: an inability to adapt responses to a student's specific academic proficiency. This is a particularly critical gap in Artificial Intelligence (AI) education, where a learner's foundational knowledge in subjects like mathematics, programming, and core AI concepts exhibits significant heterogeneity. To address this, we introduce Personalized RAG for Education (PRAG‐EDU), a novel context‐aware RAG framework that dynamically calibrates response complexity by leveraging students' historical module grades as pedagogical signals. Unlike conventional RAG implementations that treat all learners uniformly, our model integrates these academic profiles with retrieved course materials to generate responses precisely tailored to individual proficiency levels. We establish the first benchmark for grade‐aware educational RAG within the AI domain, comprising 250 expert‐validated question‐answer pairs linked to specific academic profiles and difficulty‐calibrated reference responses. Through a rigorous evaluation of seven open‐source LLMs against our framework, we demonstrate that PRAG‐EDU achieves a 23.7% improvement in BERTScore F1 (0.555 vs. 0.451) and 18.3% higher ROUGE‐L over non‐personalized baselines. A qualitative analysis of 250 student evaluations further confirms its pedagogical efficacy, with expert raters awarding an average of 4.09/5 stars, significantly outperforming the next‐best model ( Qwen3:1.7B at 3.94). This work reveals a notable trade‐off between factual alignment and generative fluency, as our method leads in accuracy while a model like Smollm2:1.7B excels in expressiveness. Ultimately, this research bridges the gap between technical RAG implementations and domain‐specific educational theory by operationalizing academic performance data as a personalization mechanism, offering a scalable solution for heterogeneous AI engineering classrooms.