AgentTutor: Empowering Personalized Learning with Multi-Turn Interactive Teaching in Intelligent Education Systems
Yuxin Liu, Zeqing Song, Jiong Lou, Chentao Wu, Jie Li

TL;DR
AgentTutor is a multi-turn interactive education system leveraging large language models to personalize teaching strategies dynamically based on learners' profiles, significantly improving learning outcomes and interaction quality.
Contribution
This paper introduces AgentTutor, a novel multi-agent, multi-turn interactive system that personalizes education using LLMs and learner profiles, addressing limitations of static question-answering.
Findings
Significantly improves learners' performance
Demonstrates strong effectiveness in multi-turn interactions
Competitive in teaching quality compared to baselines
Abstract
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-turn interactive intelligent education system to empower personalized learning. It features an LLM-powered generative multi-agent system and a learner-specific personalized learning profile environment that dynamically optimizes and delivers teaching strategies based on learners' learning status, personalized goals, learning preferences, and multimodal study materials. It includes five key modules:…
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Taxonomy
TopicsIntelligent Tutoring Systems and Adaptive Learning · Online Learning and Analytics · Innovative Teaching and Learning Methods
