Foundation Models for Education: Promises and Prospects

• URL: http://arxiv.org/abs/2405.10959v1
• Date: Mon, 8 Apr 2024 15:59:37 GMT
• Title: Foundation Models for Education: Promises and Prospects
• Authors: Tianlong Xu, Richard Tong, Jing Liang, Xing Fan, Haoyang Li, Qingsong Wen,
• Abstract summary: We discuss the strengths of foundation models, such as personalized learning, education inequality, and reasoning capabilities. We highlight the risks and opportunities of AI overreliance and creativity. We envision a future where foundation models in education harmonize human and AI capabilities, fostering a dynamic, inclusive, and adaptive educational ecosystem.
• Score: 24.75073974210808
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the advent of foundation models like ChatGPT, educators are excited about the transformative role that 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 paper, 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. Lastly, 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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