[1]肖建力,黄星宇,姜飞.智慧教育中的大语言模型综述[J].智能系统学报,2025,20(5):1054-1070.[doi:10.11992/tis.202406040]
　XIAO Jianli,HUANG Xingyu,JIANG Fei.A survey of large language models in smart education[J].CAAI Transactions on Intelligent Systems,2025,20(5):1054-1070.[doi:10.11992/tis.202406040]
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智慧教育中的大语言模型综述

《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷: 20 期数: 2025年第5期 页码: 1054-1070 栏目: 综述 出版日期: 2025-09-05
Title:
A survey of large language models in smart education
作者:
肖建力1, 黄星宇1, 姜飞2
1. 上海理工大学 光电信息与计算机工程学院, 上海 200093;
2. 重庆市科学技术研究院, 重庆 401123
Author(s):
XIAO Jianli1, HUANG Xingyu1, JIANG Fei2
1. School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;
2. Chongqing Academy of Science and Technology, Chongqing 401123, China
关键词:
人工智能; 智慧教育; 大模型; 教育技术; 自然语言处理; 教育应用; 多模态学习; 学习分析
Keywords:
artificial intelligence; smart education; large language models; educational technology; natural language processing; educational applications; multimodal learning; learning analytics
分类号:
TP18
DOI:
10.11992/tis.202406040
摘要:
近年来，人工智能技术在教育领域的广泛应用正逐步革新现代教育的模式，教育面临新的机遇和挑战。特别是随着大语言模型的兴起，人工智能有望融入到教与学的过程中，教育模式由传统的师–生二元模式正转变为师–生–机三元模式。文章以教育领域内应用的大语言模型为研究焦点，介绍了大语言模型在教育中的特点。以当前主流的几种大语言模型为例，详细阐述这些模型在教育中的实际应用情况，总结了目前教育大模型的共性以及差异性特点。还探讨了如何开发和训练满足教育需求的定制化大语言模型，这一过程对实际应用至关重要。基于训练完成的教育大模型，进一步阐释了其存在的局限性，并展望了未来教育领域可能出现的新型大模型及其发展趋势。
Abstract:
In recent years, the application of artificial intelligence (AI) technology in education has gradually advanced the modern educational model. Education currently faces new opportunities and challenges. In particular, with the emergence of large language models (LLMs), AI is expected to be integrated into the teaching and learning processes. The traditional teacher-student binary model of education is transforming into a teacher–student–machine tripartite model. This study aims to focus on LLMs applied in the field of education and introduce the characteristics of them. It takes the current mainstream LLMs as examples and elaborates on their actual applications in education in detail. It summarizes the common and distinctive features of educational large models (EduLLMs). In addition, this study also discusses how to develop and train customized LLMs to meet the needs of education. This process is very important for practical applications. Based on the trained EduLLMs, this study further explains its limitations and explores the possibility and its development trend in new EduLLMs.
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备注/Memo

收稿日期:2024-6-24。
基金项目:国家自然科学基金项目(61603257).
作者简介:肖建力，副教授，吴文俊人工智能科学技术奖获得者，中国计算机学会杰出会员，中国自动化学会高级会员，中国人工智能学会会员，电气电子工程师学会(IEEE)高级会员，美国计算机协会(ACM)高级会员，主要研究方向为人工智能与大数据，著有图书《人工智能怎么学》。E-mail：audyxiao@sjtu.edu.cn。;黄星宇，硕士研究生，主要研究方向为智慧教育。E-mail：233350741@st.usst.edu.cn。;姜飞，副研究员，主要研究方向为智能教学。E-mail: fjiang@sjtu.edu.cn。
通讯作者:肖建力. E-mail：audyxiao@sjtu.edu.cn

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