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 offer
personalized education resources due to the challenge of addressing the diverse
obstacles students encounter throughout their learning journey. Recently, the
emergence of large language models (LLMs), such as ChatGPT, 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 researches 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. Based on the current development status, we further outline two
approaches for an LLM-based education system: a unified approach and a
mixture-of-expert (MoE) approach. Finally, we explore the challenges and future
directions, providing new research opportunities and perspectives on adapting
LLMs for education.

基于大型语言模型的教育系统研究，评估其在教育能力方面的潜力，并提出统一和专家混合两种方法构建下一代智能教育系统，同时探讨了挑战和未来发展方向。

为教育适应大型语言模型：基本能力、潜力和挑战

Adapting Large Language Models for Education: Foundational Capabilities,  Potentials, and Challenges

Online education platforms enable teachers to share a large number of
educational resources such as questions to form exercises and quizzes for
students. With large volumes of available questions, it is important to have an
automated way to quantify their properties and intelligently select them for
students, enabling effective and personalized learning experiences. In this
work, we propose a framework for mining insights from educational questions at
scale. We utilize the state-of-the-art Bayesian deep learning method, in
particular partial variational auto-encoders (p-VAE), to analyze real students'
answers to a large collection of questions. Based on p-VAE, we propose two
novel metrics that quantify question quality and difficulty, respectively, and
a personalized strategy to adaptively select questions for students. We apply
our proposed framework to a real-world dataset with tens of thousands of
questions and tens of millions of answers from an online education platform.
Our framework not only demonstrates promising results in terms of statistical
metrics but also obtains highly consistent results with domain experts'
evaluation.

本文提出了一个用于在规模上挖掘教育问题洞见的框架。使用贝叶斯深度学习的技术，我们提出了两种量化问题质量和难度的新指标，并提出了一种个性化的策略，以适应学生的需求。将该框架应用于一个拥有数万个问题和数百万个答案的在线教育平台的真实数据集，我们不仅表现出有前途的统计指标，而且与领域专家的评估结果高度一致。