Summary of Survey Of Natural Language Processing For Education: Taxonomy, Systematic Review, and Future Trends, by Yunshi Lan et al.
Survey of Natural Language Processing for Education: Taxonomy, Systematic Review, and Future Trends
by Yunshi Lan, Xinyuan Li, Hanyue Du, Xuesong Lu, Ming Gao, Weining Qian, Aoying Zhou
First submitted to arxiv on: 15 Jan 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 reviews recent advances in Natural Language Processing (NLP) with a focus on solving problems relevant to the education domain. NLP has been widely applied in education, enabling applications such as question answering, question construction, automated assessment, and error correction. The paper introduces the related background, real-world scenarios, and a taxonomy of NLP in education. It highlights cutting-edge techniques, including LLM-involved methods, and showcases off-the-shelf demonstrations. The authors conclude with six promising directions for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary NLP helps computers understand text or speech. In education, it can help teachers and students. This paper looks at the latest NLP advancements that can solve problems in education. It explains what NLP is, how it’s used, and some cool applications like answering questions, making new ones, grading work, and fixing mistakes. The authors also suggest six ways to make NLP even better for education. |
Keywords
* Artificial intelligence * Natural language processing * Nlp * Question answering
