Large Language Models for Education: A survey and outlook

By Shen Wang, Tianlong Xu, Hang Li, Chaoli Zhang, Joleen Liang, Jiliang Tang, Philip S. Yu, Qingsong Wen. IEEE Signal Processing Magazine, 2024

365 citations recorded by the source corpus.

The advent of large language models (LLMs) has ushered in a new era of possibilities in the realm of education. This survey article summarizes recent progress in the application of LLMs in educational settings from multiple perspectives, including student and teacher assistance, adaptive learning, and commercial tools. Additionally, it systematically reviews technological advancements in each area, compiles related datasets and benchmarks, and identifies the risks and challenges associated with deploying LLMs in education. Furthermore, the article outlines future research opportunities, highlighting promising directions. This article aims to provide a comprehensive technological overview for educators, researchers, and policy makers to harness the power of LLMs, revolutionize educational practices, and foster a more effective personalized learning environment.

Subfields: Artificial Intelligence, Developmental and Educational Psychology, General Social Sciences

View Paper: https://www.semanticscholar.org/paper/c8996f183b883f4a16c05b4eaff2b892fb6bf57d

Citations in the corpus

• Interpretability Framework for LLMs in Undergraduate Calculus (2025, unknown): “Kang et al. [24] and Wang et al. [25] raise concerns regarding the accuracy and ethical implications of AI generated content.”
• ArchRAG: Attributed Community-based Hierarchical Retrieval-Augmented Generation (2025, unknown): “1 [11] have received tremendous attention from both industry and academia [17, 26, 36, 39, 48, 66, 68, 82].”