用高斯过程动态匹配学习伙伴，提升探究式教育的适应性。
InqEduAgent: Adaptive AI Learning Partners with Gaussian Process Augmentation
- 基于高斯过程建模学习者认知特征，实现智能配对。
- 在多种场景下表现优于传统规则方法，适配不同大模型。
- 适合需要个性化学习伙伴的在线教育系统使用。
探究式教育中协作学习伙伴至关重要，但现有方法多依赖经验启发或规则驱动，难以拓展知识且适应性差。本文提出 InqEduAgent，一种基于大语言模型的生成式代理框架，用于模拟与选择自适应学习伙伴。该框架引入高斯过程增强的匹配机制，建模学习者的认知与评价特征，依据先验知识模式实现动态伙伴匹配。大量实验表明，InqEduAgent 在多种学习场景与大模型配置下均表现优异。本研究推动了人机协同学习发展，实现了人类与AI学习伙伴的智能配对，为网络教育环境中的自适应用户建模与个性化推荐提供支持。
原文摘要 · Abstract (English)
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.
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