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.

人工智能在教育领域有深远的影响，特别是大型语言模型（LLMs）在智能教育（LLMEdu）中的应用，对提高教学质量、改变教育模式和调整教师角色有巨大的潜力，然而仍面临着挑战和问题。本文系统综述了 LLMEdu 的当前技术、挑战和未来发展，并介绍了 LLMs 与教育的特点以及将 LLMs 整合到教育中的好处。同时还回顾了将 LLMs 整合到教育行业的过程和相关技术的引入，并讨论了 LLMEdu 所面临的挑战和问题，以及未来优化 LLMEdu 的前景。

教育中的大型语言模型概述

Large Language Models for Education: A Survey

This study explores the impact of Large Language Models (LLMs) in higher
education, focusing on an automated oral examination simulation using a
prototype. The design considerations of the prototype are described, and the
system is evaluated with a select group of educators and students. Technical
and pedagogical observations are discussed. The prototype proved to be
effective in simulating oral exams, providing personalized feedback, and
streamlining educators' workloads. The promising results of the prototype show
the potential for LLMs in democratizing education, inclusion of diverse student
populations, and improvement of teaching quality and efficiency.

本研究探讨了大型语言模型在高等教育中的影响，重点是通过原型实现自动口试模拟。描述了原型的设计考虑，并对教育工作者和学生进行了评估。讨论了技术和教育观察结果。该原型在模拟口试、提供个性化反馈和简化教育工作者工作量方面表现出了良好的效果。原型的有 promising 结果显示了大型语言模型在普及教育、包容多样化学生群体以及提高教学质量和效率方面的潜力。