用多轮互动教学提升个性化学习效果
AgentTutor: Empowering Personalized Learning with Multi-Turn Interactive Teaching in Intelligent Education Systems
- 构建多智能体系统实现动态教学策略调整
- 在多个数据集上显著提升学习者表现
- 适合需要自适应教学的教育科技场景
大规模语言模型（LLMs）的快速发展为智能教育系统（IESs）提供了自动化教学支持的潜力。然而，现有系统多依赖单轮静态问答，无法评估学习者认知水平，不能根据实时反馈调整教学策略，仅能提供一次性简单回答。为此，我们提出AgentTutor，一种支持多轮交互的智能教育系统，以推动个性化学习。该系统基于大语言模型的生成式多智能体架构，结合学习者专属的个性化学习档案环境，能够依据学习状态、个人目标、学习偏好及多模态学习材料，动态优化并交付教学策略。系统包含五大模块：课程分解、学习者评估、动态策略、教学反思与知识与经验记忆。我们在多个基准数据集上进行了广泛实验，结果表明，AgentTutor在多轮交互中表现出显著学习性能提升，且在教学质量方面优于其他基线方法。
原文摘要 · Abstract (English)
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.
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