Evaluating the capability of large language models to personalize science texts for diverse middle-school-age learners

This paper presents a randomized controlled trial (n=23) evaluating GPT-4's ability to personalize science texts for middle school students by profiling their learning preferences and rewriting content accordingly. The study measures student preference for personalized versus non-personalized texts, finding significant preference for aligned content.

Large language models (LLMs), including OpenAI's GPT-series, have made significant advancements in recent years. Known for their expertise across diverse subject areas and quick adaptability to user-provided prompts, LLMs hold unique potential as Personalized Learning (PL) tools. Despite this potential, their application in K-12 education remains largely unexplored. This paper presents one of the first randomized controlled trials (n = 23) to evaluate the effectiveness of GPT-4 in personalizing

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