基于大语言模型的教育代理自适应支架理论
A Theory of Adaptive Scaffolding for LLM-Based Pedagogical Agents
AI总结：
本文提出结合证据中心设计与社会认知理论的自适应支架框架，开发出Inquizzitor评估代理，通过人机混合智能提供基于认知科学的反馈，验证了理论驱动的大语言模型在教育中的应用潜力。
摘要：
大语言模型（LLMs）为创建能够进行有意义对话以支持学生学习的教育代理提供了新机遇。然而，目前课堂中使用的LLM系统往往缺乏早期智能辅导系统中发现的坚实理论基础。为此，我们提出一个框架，结合证据中心设计、社会认知理论和最近发展区理论，用于LLM基于代理的自适应支架。我们通过Inquizzitor，一个整合人机混合智能的形成性评估代理，实例化了该框架，该代理提供基于认知科学原理的反馈。我们的发现表明，Inquizzitor能够提供高质量的评估和互动，与核心学习理论一致，提供学生重视的有效指导。本研究展示了理论驱动的LLM在教育中的潜力，突显了这些系统能够提供适应性和原则性教学的能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
