Natural Language Processing (NLP) aims to analyze text or speech via techniques in the computer science field. It serves the applications in domains of healthcare, commerce, education and so on. Particularly, NLP has been widely applied to the education domain and its applications have enormous potential to help teaching and learning. In this survey, we review recent advances in NLP with the focus on solving problems relevant to the education domain. In detail, we begin with introducing the related background and the real-world scenarios in education where NLP techniques could contribute. Then, we present a taxonomy of NLP in the education domain and highlight typical NLP applications including question answering, question construction, automated assessment, and error correction. Next, we illustrate the task definition, challenges, and corresponding cutting-edge techniques based on the above taxonomy. In particular, LLM-involved methods are included for discussion due to the wide usage of LLMs in diverse NLP applications. After that, we showcase some off-the-shelf demonstrations in this domain. At last, we conclude with six promising directions for future research, including more datasets in education domain, controllable usage of LLMs, intervention of difficulty-level control, interpretable educational NLP, methods with adaptive learning, and integrated systems for education. We organize all relevant datasets and papers in the open-available Github Link for better review~\url{https://github.com/LiXinyuan1015/NLP-for-Education}.

翻译：自然语言处理（NLP）旨在通过计算机科学技术分析文本或语音。它在医疗、商业、教育等领域均有应用。特别是，NLP已被广泛应用于教育领域，其应用在辅助教学与学习方面具有巨大潜力。本综述聚焦于教育领域相关问题，对NLP近期的研究进展进行系统梳理。具体而言，我们首先介绍相关背景知识及NLP技术能够发挥作用的教育实际场景；其次，提出教育领域NLP的分类体系，并重点阐述典型NLP应用，包括问答系统、题目生成、自动评估和纠错；接着，基于上述分类体系，阐释任务定义、挑战及相应的前沿技术，其中特别纳入涉及大语言模型（LLM）的方法进行讨论，因为LLM已在各类NLP应用中得到广泛使用；随后，展示该领域若干现成的示范系统；最后，总结未来研究的六个有前景的方向，包括教育领域更多数据集、LLM的可控使用、难度级别的干预调控、可解释的教育NLP、自适应学习方法以及教育集成系统。为便于综述查阅，我们在开源GitHub链接中整理了所有相关数据集与论文：\url{https://github.com/LiXinyuan1015/NLP-for-Education}。