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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.03174 |
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| _version_ | 1866916971137728512 |
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| author | Yang, Wen-Xi Zhao, Tian-Fang Liu, Guan Yang, Liang Liu, Zi-Tao Chen, Wei-Neng |
| author_facet | Yang, Wen-Xi Zhao, Tian-Fang Liu, Guan Yang, Liang Liu, Zi-Tao Chen, Wei-Neng |
| contents | Collaborative partnership matters in inquiry-oriented education. However, most study partners are selected either rely on experience-based assignments with little scientific planning or build on rule-based machine assistants, encountering difficulties in knowledge expansion and inadequate flexibility. This paper proposes an LLM-empowered agent model for simulating and selecting learning partners tailored to inquiry-oriented learning, named InqEduAgent. Generative agents are designed to capture cognitive and evaluative features of learners in real-world scenarios. Then, an adaptive matching algorithm with Gaussian process augmentation is formulated to identify patterns within prior knowledge. Optimal learning-partner matches are provided for learners facing different exercises. The experimental results show the optimal performance of InqEduAgent in most knowledge-learning scenarios and LLM environment with different levels of capabilities. This study promotes the intelligent allocation of human-based learning partners and the formulation of AI-based learning partners. The code, data, and appendix are publicly available at https://github.com/InqEduAgent/InqEduAgent. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_03174 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | InqEduAgent: Adaptive AI Learning Partners with Gaussian Process Augmentation Yang, Wen-Xi Zhao, Tian-Fang Liu, Guan Yang, Liang Liu, Zi-Tao Chen, Wei-Neng Artificial Intelligence Collaborative partnership matters in inquiry-oriented education. However, most study partners are selected either rely on experience-based assignments with little scientific planning or build on rule-based machine assistants, encountering difficulties in knowledge expansion and inadequate flexibility. This paper proposes an LLM-empowered agent model for simulating and selecting learning partners tailored to inquiry-oriented learning, named InqEduAgent. Generative agents are designed to capture cognitive and evaluative features of learners in real-world scenarios. Then, an adaptive matching algorithm with Gaussian process augmentation is formulated to identify patterns within prior knowledge. Optimal learning-partner matches are provided for learners facing different exercises. The experimental results show the optimal performance of InqEduAgent in most knowledge-learning scenarios and LLM environment with different levels of capabilities. This study promotes the intelligent allocation of human-based learning partners and the formulation of AI-based learning partners. The code, data, and appendix are publicly available at https://github.com/InqEduAgent/InqEduAgent. |
| title | InqEduAgent: Adaptive AI Learning Partners with Gaussian Process Augmentation |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2508.03174 |