InqEduAgent: Adaptive AI Learning Partners with Gaussian Process Augmentation

TL;DR

This paper introduces InqEduAgent, an adaptive AI learning partner system that uses Gaussian process augmentation to personalize inquiry-based learning experiences, demonstrating superior performance across various scenarios.

Contribution

It presents a novel LLM-empowered agent model with an adaptive matching algorithm for personalized learning partner selection in inquiry-oriented education.

Findings

InqEduAgent outperforms baseline methods in knowledge-learning scenarios.

The Gaussian process augmentation enhances partner matching accuracy.

The system adapts effectively to different learner capabilities.

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.