  Artificial intelligence (AI) has a profound impact on traditional education.
In recent years, large language models (LLMs) have been increasingly used in
various applications such as natural language processing, computer vision,
speech recognition, and autonomous driving. LLMs have also been applied in many
fields, including recommendation, finance, government, education, legal
affairs, and finance. As powerful auxiliary tools, LLMs incorporate various
technologies such as deep learning, pre-training, fine-tuning, and
reinforcement learning. The use of LLMs for smart education (LLMEdu) has been a
significant strategic direction for countries worldwide. While LLMs have shown
great promise in improving teaching quality, changing education models, and
modifying teacher roles, the technologies are still facing several challenges.
In this paper, we conduct a systematic review of LLMEdu, focusing on current
technologies, challenges, and future developments. We first summarize the
current state of LLMEdu and then introduce the characteristics of LLMs and
education, as well as the benefits of integrating LLMs into education. We also
review the process of integrating LLMs into the education industry, as well as
the introduction of related technologies. Finally, we discuss the challenges
and problems faced by LLMEdu, as well as prospects for future optimization of
LLMEdu.
