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

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: curriculum decomposition, learner assessment, dynamic strategy, teaching reflection, and knowledge & experience memory. We conducted extensive experiments on multiple benchmark datasets, AgentTutor significantly enhances learners' performance while demonstrating strong effectiveness in multi-turn interactions and competitiveness in teaching quality among other baselines.