Foundation Models for Education: Promises and Prospects

IF 5.6 3区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE IEEE Intelligent Systems Pub Date : 2024-06-24 DOI:10.1109/mis.2024.3398191

Tianlong Xu, Richard Tong, Jing Liang, Xing Fan, Haoyang Li, Qingsong Wen

引用次数: 0

Abstract

With the advent of foundation models like ChatGPT, educators are excited about the transformative role that artificial intelligence (AI) might play in propelling the next education revolution. The developing speed and the profound impact of foundation models in various industries force us to think deeply about the changes they will make to education, a domain that is critically important for the future of humans. In this article, we discuss the strengths of foundation models, such as personalized learning, education inequality, and reasoning capabilities, as well as the development of agent architecture tailored for education, which integrates AI agents with pedagogical frameworks to create adaptive learning environments. Furthermore, we highlight the risks and opportunities of AI overreliance and creativity. Finally, we envision a future where foundation models in education harmonize human and AI capabilities, fostering a dynamic, inclusive, and adaptive educational ecosystem.

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教育基金会模式：承诺与前景

随着 ChatGPT 等基础模型的出现，教育工作者对人工智能（AI）在推动下一场教育革命中可能发挥的变革作用感到兴奋不已。基础模型在各行各业的发展速度和深远影响迫使我们深入思考它们将给教育这个对人类未来至关重要的领域带来的变革。在本文中，我们将讨论基础模型的优势，如个性化学习、教育不平等和推理能力，以及为教育量身定制的代理架构的发展，该架构将人工智能代理与教学框架相结合，以创建自适应学习环境。此外，我们还强调了过度依赖人工智能和创造力的风险与机遇。最后，我们展望未来，教育领域的基础模型将协调人类和人工智能的能力，促进动态、包容和自适应的教育生态系统。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Intelligent Systems 工程技术-工程：电子与电气

CiteScore

13.80

自引率

3.10%

发文量

122

审稿时长

1 months

期刊介绍： IEEE Intelligent Systems serves users, managers, developers, researchers, and purchasers who are interested in intelligent systems and artificial intelligence, with particular emphasis on applications. Typically they are degreed professionals, with backgrounds in engineering, hard science, or business. The publication emphasizes current practice and experience, together with promising new ideas that are likely to be used in the near future. Sample topic areas for feature articles include knowledge-based systems, intelligent software agents, natural-language processing, technologies for knowledge management, machine learning, data mining, adaptive and intelligent robotics, knowledge-intensive processing on the Web, and social issues relevant to intelligent systems. Also encouraged are application features, covering practice at one or more companies or laboratories; full-length product stories (which require refereeing by at least three reviewers); tutorials; surveys; and case studies. Often issues are theme-based and collect articles around a contemporary topic under the auspices of a Guest Editor working with the EIC.

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