Multi-Agentic LLMs for Personalizing STEM Texts
Michael Vaccaro; Mikayla Friday; Arash E. Zaghi · 2025 · Applied Sciences
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
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
Abstract by Michael Vaccaro; Mikayla Friday; Arash E. Zaghi, Applied Sciences (2025) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.3390/app15137579