[论文解读] AGI: Artificial General Intelligence for Education

Ehsan Latif, Gengchen Mai+7 more|arXiv (Cornell University)|Apr 24, 2023

Artificial Intelligence in Healthcare and Education被引用 36

一句话总结

一篇立场性论文，回顾由大型预训练模型如 GPT-4 和 ChatGPT 所驱动的 AGI，如何通过个性化辅导、自适应评估、课程设计和伦理考量改变教育，并强调教育者与 AI 工程师之间的协作。

ABSTRACT

Artificial general intelligence (AGI) has gained global recognition as a future technology due to the emergence of breakthrough large language models and chatbots such as GPT-4 and ChatGPT, respectively. Compared to conventional AI models, typically designed for a limited range of tasks, demand significant amounts of domain-specific data for training and may not always consider intricate interpersonal dynamics in education. AGI, driven by the recent large pre-trained models, represents a significant leap in the capability of machines to perform tasks that require human-level intelligence, such as reasoning, problem-solving, decision-making, and even understanding human emotions and social interactions. This position paper reviews AGI's key concepts, capabilities, scope, and potential within future education, including achieving future educational goals, designing pedagogy and curriculum, and performing assessments. It highlights that AGI can significantly improve intelligent tutoring systems, educational assessment, and evaluation procedures. AGI systems can adapt to individual student needs, offering tailored learning experiences. They can also provide comprehensive feedback on student performance and dynamically adjust teaching methods based on student progress. The paper emphasizes that AGI's capabilities extend to understanding human emotions and social interactions, which are critical in educational settings. The paper discusses that ethical issues in education with AGI include data bias, fairness, and privacy and emphasizes the need for codes of conduct to ensure responsible AGI use in academic settings like homework, teaching, and recruitment. We also conclude that the development of AGI necessitates interdisciplinary collaborations between educators and AI engineers to advance research and application efforts.

研究动机与目标

• 在教育领域界定 AGI 及其核心能力。
• 调查 AGI 如何支持教学法、课程设计和评估。
• 讨论对智能辅导系统及教育结果的潜在影响。
• 强调伦理考量、数据隐私，以及教育中人机协作的必要性。

提出的方法

• 综合现有文献中关于 AGI、LLMs 与教育的概念。
• 概述核心 AGI 原理（认知、知识表示、学习、规划、语言、多模态处理）。
• 区分教育情境中的 ANI 与 AGI 并分析其影响。
• 讨论在 ITS、评估设计和课程开发中应用 AGI 的实际应用。
• 审查在教育中负责任使用的伦理、公平与治理方面的考虑。

实验结果

研究问题

• RQ1 与教育相关的 AGI 的定义特征与核心能力是什么？
• RQ2 AGI 如何提升教育环境中的教学法、课程设计与评估？
• RQ3 人类教育者应在与 AGI 搭配使用中扮演何种角色，以确保高效且合伦理？
• RQ4 在教育领域部署 AGI 时有哪些关键伦理、公平和隐私问题，如何可以缓解？

主要发现

• AGI 可能实现高度个性化、自适应的学习体验以及全面、类人反馈。
• LLMs（如 GPT-4、ChatGPT）被视为通往具广泛教育应用的 AGI 的垫脚石。
• AGI 能支持智能辅导系统、教学知识增强和动态评估方法。
• 论文强调教育者与 AI 工程师之间跨学科协作的必要性。
• 伦理考量包括数据偏见、公平、隐私，以及在教育中负责任使用 AGI 的行为准则需求。
• 人类教育者的角色被描述为对 AGI 的互补，聚焦于引导、情感支持和内容的发展适宜性。