Large Language Models for Education: A Survey and Outlook
Shen Wang, Tianlong Xu, Hang Li, Chaoli Zhang, Joleen Liang, Jiliang, Tang, Philip S. Yu, Qingsong Wen

TL;DR
This survey comprehensively reviews the role of Large Language Models in education, covering technological advancements, applications, datasets, challenges, and future research directions to enhance personalized learning.
Contribution
It provides a systematic overview of LLM applications in education, organizing datasets and benchmarks, and identifying key challenges and future opportunities.
Findings
Summarizes current LLM applications in education.
Organizes datasets and benchmarks for educational LLMs.
Highlights challenges and future research directions.
Abstract
The advent of Large Language Models (LLMs) has brought in a new era of possibilities in the realm of education. This survey paper summarizes the various technologies of LLMs in educational settings from multifaceted perspectives, encompassing student and teacher assistance, adaptive learning, and commercial tools. We systematically review the technological advancements in each perspective, organize related datasets and benchmarks, and identify the risks and challenges associated with deploying LLMs in education. Furthermore, we outline future research opportunities, highlighting the potential promising directions. Our survey aims to provide a comprehensive technological picture for educators, researchers, and policymakers to harness the power of LLMs to revolutionize educational practices and foster a more effective personalized learning environment.
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Taxonomy
TopicsTopic Modeling
