InqEduAgent: Adaptive AI Learning Partners with Gaussian Process Augmentation
arXiv:2508.03174
The paper presents InqEduAgent, a framework that uses large language models combined with a Gaussian‑process‑based matching mechanism to adaptively select AI learning partners for inquiry‑based education, improving personalization and performance.
Abstract
Collaborative partnerships play a crucial role in inquiry-oriented education. However, most learning partners are currently assigned through experience-driven heuristics or rule-based machine assistants, which often result in limited knowledge expansion and low adaptability. To address these challenges, this study introduces InqEduAgent, an LLM-empowered generative agent framework designed to simulate and select adaptive learning partners for inquiry-based learning. InqEduAgent integrates a Gaussian process-augmented matching mechanism to model the cognitive and evaluative characteristics of learners, allowing adaptive partner selection based on prior knowledge patterns. Comprehensive experiments demonstrate that InqEduAgent consistently achieves superior performance across diverse learning scenarios and large language model configurations. This study advances human-AI collaborative learning by enabling intelligent pairing between human- and AI-based learning partners, and contributes to adaptive user modeling and personalized recommendation within Web-based educational environments.
Accepted by the 2026 8th Asia Conference on Machine Learning and Computing (ACMLC 2026)