综述大语言模型在教育智能助教中的应用与设计趋势
A Scoping Review of Large Language Model-Based Pedagogical Agents
- 梳理52项研究，归纳四类智能助教设计维度
- 发现多智能体系统与虚拟学生模拟是新趋势
- 适合教育技术研究者和智能教学系统开发者
本篇范围综述基于PRISMA-ScR指南，分析了2022年11月至2025年1月期间来自五大数据库的52项研究，探讨大语言模型（LLM）驱动的教育智能助教在基础教育、高等教育及非正式学习场景中的应用。研究覆盖多个学科领域，识别出四类关键设计维度：交互方式（反应式与主动式）、领域范围（专用型与通用型）、角色复杂度（单角色与多角色）以及系统集成方式（独立运行与嵌入式）。新兴趋势包括多智能体系统构建自然学习环境、虚拟学生用于助教评估、与沉浸式技术融合以及结合学习分析。同时指出隐私保护、准确性与学生自主性等伦理挑战。该综述为研究人员与实践者提供了全面理解，并指明未来关键发展方向。
原文摘要 · Abstract (English)
This scoping review examines the emerging field of Large Language Model (LLM)-based pedagogical agents in educational settings. While traditional pedagogical agents have been extensively studied, the integration of LLMs represents a transformative advancement with unprecedented capabilities in natural language understanding, reasoning, and adaptation. Following PRISMA-ScR guidelines, we analyzed 52 studies across five major databases from November 2022 to January 2025. Our findings reveal diverse LLM-based agents spanning K-12, higher education, and informal learning contexts across multiple subject domains. We identified four key design dimensions characterizing these agents: interaction approach (reactive vs. proactive), domain scope (domain-specific vs. general-purpose), role complexity (single-role vs. multi-role), and system integration (standalone vs. integrated). Emerging trends include multi-agent systems that simulate naturalistic learning environments, virtual student simulation for agent evaluation, integration with immersive technologies, and combinations with learning analytics. We also discuss significant research gaps and ethical considerations regarding privacy, accuracy, and student autonomy. This review provides researchers and practitioners with a comprehensive understanding of LLM-based pedagogical agents while identifying crucial areas for future development in this rapidly evolving field.
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