Year: 2025
Type: conference-paper
Abstract: 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.
Authors: Michael Vaccaro, Mikayla Friday, Arash Zaghi
Institution: University of Connecticut
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
OpenAlex ID: W4412479847