AgentTutor: Empowering Personalized Learning with Multi-Turn Interactive Teaching in Intelligent Education Systems

AgentTutor is an LLM-powered multi-turn interactive tutoring system that uses generative agents to deliver personalized instruction by decomposing curricula, assessing learner cognitive levels based on Bloom's Taxonomy, dynamically adjusting teaching strategies, and providing reflective feedback through multi-turn dialogue. The system evaluates its effectiveness on benchmark datasets by measuring learning outcomes and teaching quality in interactive educational contexts.

The rapid advancement of large-scale language models (LLMs) has shown their potential to transform intelligent education systems (IESs) through automated teaching and learning support applications. However, current IESs often rely on single-turn static question-answering, which fails to assess learners'cognitive levels, cannot adjust teaching strategies based on real-time feedback, and is limited to providing simple one-off responses. To address these issues, we introduce AgentTutor, a multi-tur

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