CLAIMar 26, 2024

Large Language Models for Education: A Survey and Outlook

arXiv:2403.18105v2325 citationsh-index: 13IEEE Signal Processing Magazine
Originality Synthesis-oriented
AI Analysis

It provides a comprehensive overview for educators, researchers, and policymakers on using LLMs to improve educational practices, but it is incremental as it synthesizes existing knowledge without new empirical results.

This survey paper summarizes the technologies of Large Language Models (LLMs) in education, covering student and teacher assistance, adaptive learning, and commercial tools, and identifies risks and future opportunities to help educators and researchers harness LLMs for personalized learning.

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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