Summary of Large Language Models For Education: a Survey, by Hanyi Xu et al.
Large Language Models for Education: A Survey
by Hanyi Xu, Wensheng Gan, Zhenlian Qi, Jiayang Wu, Philip S. Yu
First submitted to arxiv on: 12 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a systematic review of smart education (LLMEdu) that leverages large language models (LLMs). LLMs have revolutionized various fields, including natural language processing, computer vision, and autonomous driving. In education, LLMs can improve teaching quality, change education models, and modify teacher roles. The paper summarizes the current state of LLMEdu, introducing the characteristics of LLMs and education, as well as the benefits of integrating LLMs into education. It also reviews the process of integrating LLMs into the education industry and introduces related technologies. However, LLMEdu still faces several challenges. This study aims to provide a comprehensive overview of the current state of LLMEdu, its characteristics, and future developments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how artificial intelligence (AI) can help improve education. AI has already changed many things, like language translation and self-driving cars. Now, experts are exploring ways to use AI in schools. They want to see if AI can make teaching better, change the way we learn, or even give teachers new roles. The paper looks at what’s happening now with AI in education and tries to figure out what might happen next. While AI has many benefits, there are also some challenges that need to be addressed. |
Keywords
* Artificial intelligence * Natural language processing * Translation
