[论文解读] Large Language Models for Education: A Survey

Hanyi Xu, Wensheng Gan+3 more|arXiv (Cornell University)|May 12, 2024

Topic Modeling被引用 13

一句话总结

本论文系统性地综述了大型语言模型（LLMs）在智慧教育中的应用（LLMEdu），概述了当前技术、集成过程、收益、挑战和未来方向。

ABSTRACT

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.

研究动机与目标

• 分析LLMEdu作为教育战略方向的发展及其作用。
• 总结与LLMEdu相关的LLMs和教育的特征。
• 描述将LLMs整合到教育领域的过程与技术。
• 识别挑战并提出LLMEdu未来优化的方向。

提出的方法

• 对LLMEdu文献与前沿进行系统综述。
• 界定与LLMEdu相关的LLM特征和教育需求。
• 评审教育领域的整合过程及相关技术。
• 讨论收益、对教师的影响及无障碍性含义。
• 突出LLMEdu的挑战及潜在未来优化方向。

实验结果

研究问题

• RQ1 哪些是使LLMs能够在教育中使用的关键特征？
• RQ2 LLMs如何整合到教育领域，哪些技术支持这一整合？
• RQ3 LLMEdu对学习者和教育者的主要收益和影响是什么？
• RQ4 围绕LLMEdu存在哪些挑战与风险，以及潜在的未来优化路径？

主要发现

• LLMs提供个性化学习支持、自适应引导和可跨越多学科的实时辅导。
• LLMs提供广泛的学科知识覆盖并支持跨学科学习。
• LLMs通过使教学形式出现新的指导、评估与资源提供方式来影响教学角色。
• 挑战包括个性化学习需求、资源限制、教育质量与标准以及隐私/安全方面的担忧。
• 本文讨论了LLMEdu的三种模式——工具型、伴随型、信息型——并强调了可扩展、AI驱动教育的潜力。
• 未来方向强调多模态LLMs、面向教育者的AI驱动专业发展，以及确保可访问性与包容性。