Imitating Mistakes in a Learning Companion AI Agent for Online Peer Learning
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908454201851904 |
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| author | Moribe, Sosui Ushiama, Taketoshi |
| author_facet | Moribe, Sosui Ushiama, Taketoshi |
| contents | In recent years, peer learning has gained attention as a method that promotes spontaneous thinking among learners, and its effectiveness has been confirmed by numerous studies. This study aims to develop an AI Agent as a learning companion that enables peer learning anytime and anywhere. However, peer learning between humans has various limitations, and it is not always effective. Effective peer learning requires companions at the same proficiency levels. In this study, we assume that a learner's peers with the same proficiency level as the learner make the same mistakes as the learner does and focus on English composition as a specific example to validate this approach. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_12801 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Imitating Mistakes in a Learning Companion AI Agent for Online Peer Learning Moribe, Sosui Ushiama, Taketoshi Artificial Intelligence Multiagent Systems In recent years, peer learning has gained attention as a method that promotes spontaneous thinking among learners, and its effectiveness has been confirmed by numerous studies. This study aims to develop an AI Agent as a learning companion that enables peer learning anytime and anywhere. However, peer learning between humans has various limitations, and it is not always effective. Effective peer learning requires companions at the same proficiency levels. In this study, we assume that a learner's peers with the same proficiency level as the learner make the same mistakes as the learner does and focus on English composition as a specific example to validate this approach. |
| title | Imitating Mistakes in a Learning Companion AI Agent for Online Peer Learning |
| topic | Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2507.12801 |