Foundation Models for Education: Promises and Prospects

Tianlong Xu, Richard Tong, Jing Liang, Xing Fan, Haoyang Li +1 more
4/8/2024
cs.CYcs.LG

No confident code implementation found for this paper.

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Cite this paper

@article{xu2024foundation,
  title  = {Foundation Models for Education: Promises and Prospects},
  author = {Tianlong Xu and Richard Tong and Jing Liang and Xing Fan and Haoyang Li and Qingsong Wen},
  year   = {2024},
  eprint = {2405.10959},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2405.10959v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2405.10959.svg)](https://codeofpaper.com/paper/2405.10959)

Discussion