# Large Language Models for Education: A Survey and Outlook

**Authors:** Shen Wang, Tianlong Xu, Hang Li, Chaoli Zhang, Joleen Liang, Jiliang Tang, Philip S. Yu, Qingsong Wen  
**arXiv:** [2403.18105](https://arxiv.org/abs/2403.18105)  
**Date:** 2024-03-26  
**Summary page:** https://www.summarizepaper.com/en/paper/2403.18105v1/

## Key points

- Large Language Models (LLMs) have revolutionized education by offering new possibilities
- The survey paper authored by Shen Wang, Tianlong Xu, Hang Li, Chaoli Zhang, Joleen Liang, Jiliang Tang, Philip S. Yu, and Qingsong Wen systematically examines LLM technologies in education from various perspectives
- Perspectives covered include student and teacher assistance, adaptive learning, and commercial tools
- The authors review technological advancements within each perspective and organize related datasets and benchmarks for a comprehensive overview
- Potential risks and challenges of deploying LLMs in education are identified along with future research opportunities
- LLMs have shown significant traction in educational applications with state-of-the-art performances across subjects like physics and computer science
- Concerns include potential bias in AI-generated content and inequitable access for non-English speakers
- The survey aims to provide educators, researchers, and policymakers with a comprehensive understanding of how LLMs can enhance educational practices
- QuestionWell is highlighted as an LLM-based tool that generates unlimited questions for teachers to improve assessment practices

## Summary

The emergence of Large Language Models (LLMs) has ushered in a new era of possibilities in the field of education. This survey paper, authored by Shen Wang, Tianlong Xu, Hang Li, Chaoli Zhang, Joleen Liang, Jiliang Tang, Philip S. Yu, and Qingsong Wen from institutions such as Squirrel AI in the USA and Michigan State University, systematically examines the various technologies of LLMs in educational settings from diverse perspectives. These perspectives include student and teacher assistance, adaptive learning, and commercial tools. The authors meticulously review the technological advancements within each perspective while also organizing related datasets and benchmarks to provide a comprehensive overview. They identify potential risks and challenges associated with deploying LLMs in education while outlining future research opportunities that highlight promising directions for further exploration. In recent years  has gained significant traction in educational applications with methods like educational data mining being widely adopted for cognitive diagnosis , content recommendations . Large language models have emerged as a powerful paradigm across different domains and have demonstrated state-of-the-art performances in various educational scenarios. Studies have shown that LLMs can achieve student-level performance on standardized tests across multiple subjects like physics and computer science. Additionally . However , potential bias in AI-generated content , and inequitable access for non-English speakers. Despite the rapid developments and promising results of LLMs in education . This survey aims to fill that gap by providing educators, researchers, and policymakers with a comprehensive understanding of how LLMs can revolutionize educational practices and create more effective personalized learning environments. Furthermore . QuestionWell is highlighted as an LLM-based tool that generates unlimited questions for teachers to enhance their assessment practices. Overall .