Bringing Generative AI to Adaptive Learning in Education
Fuente:
arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917708551946240 |
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| author | Li, Hang Xu, Tianlong Zhang, Chaoli Chen, Eason Liang, Jing Fan, Xing Li, Haoyang Tang, Jiliang Wen, Qingsong |
| author_facet | Li, Hang Xu, Tianlong Zhang, Chaoli Chen, Eason Liang, Jing Fan, Xing Li, Haoyang Tang, Jiliang Wen, Qingsong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_14601 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bringing Generative AI to Adaptive Learning in Education Li, Hang Xu, Tianlong Zhang, Chaoli Chen, Eason Liang, Jing Fan, Xing Li, Haoyang Tang, Jiliang Wen, Qingsong Computers and Society Artificial Intelligence Human-Computer Interaction Machine Learning 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. |
| title | Bringing Generative AI to Adaptive Learning in Education |
| topic | Computers and Society Artificial Intelligence Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2402.14601 |