Online education platforms, leveraging the internet to distribute education resources, seek to provide convenient education but often fall short in real-time communication with students. They often struggle to address the diverse obstacles students encounter throughout their learning journey. Solving the problems encountered by students poses a significant challenge for traditional deep learning models, as it requires not only a broad spectrum of subject knowledge but also the ability to understand what constitutes a student's individual difficulties. It's challenging for traditional machine learning models, as they lack the capacity to comprehend students' personalized needs. Recently, the emergence of large language models (LLMs) offers the possibility for resolving this issue by comprehending individual requests. Although LLMs have been successful in various fields, creating an LLM-based education system is still challenging for the wide range of educational skills required. This paper reviews the recently emerged LLM research related to educational capabilities, including mathematics, writing, programming, reasoning, and knowledge-based question answering, with the aim to explore their potential in constructing the next-generation intelligent education system. Specifically, for each capability, we focus on investigating two aspects. Firstly, we examine the current state of LLMs regarding this capability: how advanced they have become, whether they surpass human abilities, and what deficiencies might exist. Secondly, we evaluate whether the development methods for LLMs in this area are generalizable, that is, whether these methods can be applied to construct a comprehensive educational supermodel with strengths across various capabilities, rather than being effective in only a singular aspect.

翻译：在线教育平台借助互联网分发教育资源，旨在提供便捷的教育服务，但在与学生的实时互动方面常显不足，难以应对学生在学习过程中遇到的各种障碍。解决学生遇到的学习问题对传统深度学习模型构成重大挑战，因为这不仅需要广泛的多学科知识，还需具备识别学生个性化困难的能力。传统机器学习模型难以应对这一挑战，因其缺乏理解学生个性化需求的能力。近年来，大语言模型（LLMs）的出现为通过理解个体请求解决该问题提供了可能性。尽管LLMs已在多个领域取得成功，但构建基于LLMs的教育系统仍因所需教育技能的广泛性而面临挑战。本文综述了近期与教育能力相关的LLM研究，涵盖数学、写作、编程、推理及基于知识的问题回答能力，旨在探索其在构建下一代智能教育系统中的潜力。具体而言，针对每项能力，我们从两个维度展开研究：首先，分析LLMs在该能力上的现状——它们的发展程度、是否超越人类能力、以及可能存在的缺陷；其次，评估该领域LLM发展方法的可推广性，即这些方法能否用于构建在多种能力上均表现卓越的综合性教育超级模型，而非仅局限于单一能力。