提出智能教育对话代理框架，支持三种沟通模式提升教学效果。
AgentSME for Simulating Diverse Communication Modes in Smart Education
- 构建三种沟通模式：独白、单向与回声式，模拟不同自主性互动
- 回声模式准确率最高，DeepSeek模型多样性表现最优
- 适用于教育智能体开发，尤其适合个性化教学场景
针对智能教育中生成式代理模型发展不足的问题，本文提出AgentSME框架，基于大语言模型（LLM）构建统一的生成代理系统。该框架涵盖三种通信模式：Solo（独白）、Mono（单向）和Echo（回声），分别体现不同的代理自主性与交互对称性。以准确率为首要评估指标，并引入三项多样性指数衡量推理内容的丰富程度。在六种主流大模型上进行测试，分为基础容量与高容量两类配置，验证其跨模型鲁棒性。实验结果表明，采用回声通信模式的代理取得最高准确率，而DeepSeek模型展现出最强的多样性。研究为提升智能教育代理的学习能力提供了关键参考。
原文摘要 · Abstract (English)
Generative agent models specifically tailored for smart education are critical, yet remain relatively underdeveloped. A key challenge stems from the inherent complexity of educational contexts: learners are human beings with various cognitive behaviors, and pedagogy is fundamentally centered on personalized human-to-human communication. To address this issue, this paper proposes AgentSME, a unified generative agent framework powered by LLM. Three directional communication modes are considered in the models, namely Solo, Mono, and Echo, reflecting different types of agency autonomy and communicative reciprocity. Accuracy is adopted as the primary evaluation metric, complemented by three diversity indices designed to assess the diversity of reasoning contents. Six widely used LLMs are tested to validate the robustness of communication modes across different model tiers, which are equally divided into base-capacity and high-capacity configurations. The results show that generative agents that employ the Echo communication mode achieve the highest accuracy scores, while DeepSeek exhibits the greatest diversity. This study provides valuable information to improve agent learning capabilities and inspire smart education models.
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