用教育理论指导大模型，让智能辅导更精准有效。
A Theory of Adaptive Scaffolding for LLM-Based Pedagogical Agents
- 融合认知科学理论构建自适应辅导框架
- 系统提供符合学习理论的高质量反馈
- 适合教育AI研究者与智能教学开发者
大型语言模型（LLMs）为创建能够支持学生学习的对话式教学代理提供了新机遇。然而，当前课堂中使用的LLM系统往往缺乏早期智能辅导系统所具备的坚实理论基础。为此，我们提出一个结合证据中心设计、社会认知理论和最近发展区理论的自适应支架框架，应用于基于LLM的STEM+C学习教学代理。我们以Inquizzitor为例，该代理整合了人机混合智能，提供基于认知科学原则的反馈。研究结果表明，Inquizzitor能提供与核心学习理论一致的高质量评估与互动，学生普遍认可其指导价值。本研究展示了理论驱动的LLM教育应用潜力，证明此类系统可实现自适应且有据可依的教学支持。
原文摘要 · Abstract (English)
Large language models (LLMs) present new opportunities for creating pedagogical agents that engage in meaningful dialogue to support student learning. However, current LLM systems used in classrooms often lack the solid theoretical foundations found in earlier intelligent tutoring systems. To bridge this gap, we propose a framework that combines Evidence-Centered Design with Social Cognitive Theory and Zone of Proximal Development for adaptive scaffolding in LLM-based agents focused on STEM+C learning. We instantiate this framework with Inquizzitor, an LLM-based formative assessment agent that integrates human-AI hybrid intelligence and provides feedback grounded in cognitive science principles. Our findings show that Inquizzitor delivers high-quality assessment and interaction aligned with core learning theories, offering effective guidance that students value. This research demonstrates the potential for theory-driven LLM integration in education, highlighting the ability of these systems to provide adaptive and principled instruction.
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