  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.
