ExpertAgent: Enhancing Personalized Education through Dynamic Planning and Retrieval-Augmented Long-Chain Reasoning
This addresses the need for more adaptive and trustworthy AI tutors in personalized education, though it appears incremental in combining existing techniques like dynamic planning and retrieval-augmented generation.
The paper tackled the problem of lack of real-time adaptability, personalization, and reliability in AI-driven education by proposing ExpertAgent, a framework that uses dynamic planning and retrieval-augmented reasoning to provide personalized learning experiences, reducing hallucination risks and improving reliability.
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.