Summary of Large Language Models For Education: a Survey and Outlook, by Shen Wang et al.
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
First submitted to arxiv on: 26 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 survey paper summarizes the various technologies of Large Language Models (LLMs) in educational settings from multifaceted perspectives. It reviews technological advancements in student and teacher assistance, adaptive learning, and commercial tools, organizing related datasets and benchmarks. The paper identifies risks and challenges associated with deploying LLMs in education and outlines future research opportunities highlighting potential promising directions. This comprehensive survey aims to provide a 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how Large Language Models (LLMs) can help change education. It looks at different ways LLMs are used in schools, from helping students and teachers to adapting learning to commercial tools. The authors review what’s been done so far, list related datasets and benchmarks, and talk about the challenges and risks of using LLMs in education. They also suggest areas where more research is needed. This paper aims to give educators, researchers, and policymakers a big-picture view of how LLMs can be used to improve education. |
