Bringing Generative AI to Adaptive Learning in Education

Hang Li, Tianlong Xu, Chaoli Zhang, Eason Chen, Jing Liang +4 more
2/2/2024
cs.CYcs.AIcs.HC

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Abstract

The recent surge in generative AI technologies, such as large language models and diffusion models, has boosted the development of AI applications in various domains, including science, finance, and education. Concurrently, adaptive learning, a concept that has gained substantial interest in the educational sphere, has proven its efficacy in enhancing students' learning efficiency. In this position paper, we aim to shed light on the intersectional studies of these two methods, which combine generative AI with adaptive learning concepts. By presenting discussions about the benefits, challenges, and potentials in this field, we argue that this union will contribute significantly to the development of the next-stage learning format in education.

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Cite this paper

@article{li2024bringing,
  title  = {Bringing Generative AI to Adaptive Learning in Education},
  author = {Hang Li and Tianlong Xu and Chaoli Zhang and Eason Chen and Jing Liang and Xing Fan and Haoyang Li and Jiliang Tang and Qingsong Wen},
  year   = {2024},
  eprint = {2402.14601},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2402.14601v3}
}

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