ExpertAgent通过动态规划与检索增强，实现个性化自适应学习。
ExpertAgent: Enhancing Personalized Education through Dynamic Planning and Retrieval-Augmented Long-Chain Reasoning
- 基于实时更新的学生模型动态规划学习内容与策略。
- 内容源自验证过的课程库，显著降低大模型幻觉风险。
- 适合需要个性化教学的教育科技开发者与研究者。
生成式人工智能在教育中的应用常受限于内容的实时适应性、个性化和可靠性不足。为此，我们提出ExpertAgent——一种面向个性化教育的智能代理框架，可提供可靠知识并实现高度自适应的学习体验。该框架基于持续更新的学生模型，动态规划学习内容与策略，突破传统静态内容的局限，实现实时优化的教学方案与学习体验。所有教学内容均源自经过验证的课程资源库，有效降低大语言模型的幻觉风险，提升内容的可靠性与可信度。
原文摘要 · Abstract (English)
The application of advanced generative artificial intelligence in education is often constrained by the lack of real-time adaptability, personalization, and reliability of the content. To address these challenges, we propose ExpertAgent - an intelligent agent framework designed for personalized education that provides reliable knowledge and enables highly adaptive learning experiences. Therefore, we developed ExpertAgent, an innovative learning agent that provides users with a proactive and personalized learning experience. ExpertAgent dynamic planning of the learning content and strategy based on a continuously updated student model. Therefore, overcoming the limitations of traditional static learning content to provide optimized teaching strategies and learning experience in real time. All instructional content is grounded in a validated curriculum repository, effectively reducing hallucination risks in large language models and improving reliability and trustworthiness.
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