InqEduAgent: Adaptive AI Learning Partners with Gaussian Process Augmentation

• URL: http://arxiv.org/abs/2508.03174v2
• Date: Wed, 06 Aug 2025 15:28:04 GMT
• Title: InqEduAgent: Adaptive AI Learning Partners with Gaussian Process Augmentation
• Authors: Tian-Fang Zhao, Wen-Xi Yang, Guan Liu, Liang Yang,
• Abstract summary: This paper proposes an LLM-empowered agent model for simulating and selecting learning partners tailored to inquiry-oriented learning.<n>Generative agents are designed to capture cognitive and evaluative features of learners in real-world scenarios.<n>The experimental results show the optimal performance of InqEduAgent in most knowledge-learning scenarios and LLM environment.
• Score: 4.96669107440958
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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