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Multi-agent large language models promise flexible, modular architectures for delivering personalized educational content. Drawing on a pilot randomized controlled trial with middle school students (n = 23), we introduce a two-agent GPT-4 framework in which a Profiler agent infers learner-specific preferences and a Rewrite agent dynamically adapts science passages via an explicit message-passing protocol. We implement structured system and user prompts as inter-agent communication schemas to enable real-time content adaptation. The results of an ordinal logistic regression analysis hinted that students may be more likely to prefer texts aligned with their profile, demonstrating the feasibility of multi-agent system-driven personalization and highlighting the need for additional work to build upon this pilot study. Beyond empirical validation, we present a modular multi-agent architecture detailing agent roles, communication interfaces, and scalability considerations. We discuss design best practices, ethical safeguards, and pathways for extending this framework to collaborative agent networks—such as feedback-analysis agents—in K-12 settings. These results advance both our theoretical and applied understanding of multi-agent LLM systems for personalized learning. 

Generative artificial intelligence and large language models (LLMs) have proven to be disruptors in education due to their ability to produce human-like text, placing these models under heavy scrutiny. However, LLMs embody a diverse knowledge base and have been shown to be few-shot learners (Brown et al., 2020) that can quickly adapt their output in response to user-provided context. Together, these facets situate LLMs as powerful tools capable of developing personalized learning materials for K-12 students without the need for expansive training data. As this potential has yet to be evaluated in literature, this study aims to investigate the ability of LLMs to adapt science texts to middle school students’ learning preferences.