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<p>The rapid advancement of artificial intelligence has significantly impacted education, with large-scale foundation models (LFMs) emerging as transformative tools. While LFMs have demonstrated exceptional performance across diverse domains, their integration into K-12 education remains in its early stages, requiring alignment with pedagogical principles, cognitive development, and curriculum standards. This paper provides a comprehensive technological review of LFM applications in K-12 education, examining current workflows, challenges, and future opportunities. We explore how LFMs facilitate personalized learning, teacher-student collaboration, and automated assessment while highlighting critical issues such as motivation, engagement, and age-appropriate instructional strategies. By analyzing global developments, this study offers valuable insights for educators seeking to optimize AI-driven teaching methods and for students leveraging AI for self-directed learning. Our findings aim to inform future research and drive innovation in educational AI, ensuring the effective and ethical integration of LFMs into the evolving K-12 educational landscape.</p>

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Current Trends and Future Prospects of Large-Scale Foundation Model in K-12 Education

  • 期刊论文

  • 发表日期：

    2025年5月8日

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  • Qiannan Zhu,
  • Mei Wang,
  • Ting Zhang,
  • Hua Huang

摘要

The rapid advancement of artificial intelligence has significantly impacted education, with large-scale foundation models (LFMs) emerging as transformative tools. While LFMs have demonstrated exceptional performance across diverse domains, their integration into K-12 education remains in its early stages, requiring alignment with pedagogical principles, cognitive development, and curriculum standards. This paper provides a comprehensive technological review of LFM applications in K-12 education, examining current workflows, challenges, and future opportunities. We explore how LFMs facilitate personalized learning, teacher-student collaboration, and automated assessment while highlighting critical issues such as motivation, engagement, and age-appropriate instructional strategies. By analyzing global developments, this study offers valuable insights for educators seeking to optimize AI-driven teaching methods and for students leveraging AI for self-directed learning. Our findings aim to inform future research and drive innovation in educational AI, ensuring the effective and ethical integration of LFMs into the evolving K-12 educational landscape.