InqEduAgent: Adaptive AI Learning Partners with Gaussian Process Augmentation
This addresses the need for intelligent allocation of learning partners in education, but it appears incremental as it builds on existing LLM and Gaussian process methods.
The paper tackles the problem of selecting learning partners in inquiry-oriented education by proposing InqEduAgent, an LLM-empowered agent model with Gaussian process augmentation, which shows optimal performance in most knowledge-learning scenarios and LLM environments.
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.