  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}.
