大型语言模型的对话式教学法：将对话式人工智能与成熟的学习理论对齐
Dialogic Pedagogy for Large Language Models: Aligning Conversational AI with Proven Theories of Learning
AI总结：
本文综述LLM对话代理在教育中的应用，结合维果茨基、苏格拉底和劳里拉德理论，提出通过提示设计和RAG对齐教学法，弥合理论与实践的差距。
摘要：
大型语言模型（LLMs）正通过实现丰富的对话式学习体验，迅速变革教育领域。本文对基于LLM的对话代理在高等教育中的应用进行了全面综述，并将其扩展至中等教育和终身学习情境。我们综合了关于教育中LLM的现有文献，以及对话式和对话式教学法的理论——包括维果茨基的社会文化学习理论（脚手架和最近发展区）、苏格拉底式方法，以及劳里拉德的对话框架——并考察了提示策略和检索增强生成（RAG）如何使LLM行为与这些教学理论对齐，以及它如何支持个性化、自适应学习。我们将教育理论映射到LLM能力上，突出LLM驱动的对话在哪些方面支持既定的学习原则，在哪些方面挑战或未能达到传统教学假设。我们识别了将先前理论应用于LLM时存在的显著差距，例如模型倾向于提供直接答案而非促进知识的共同建构，以及需要考虑LLM导师的持续可用性和广泛但非人类的专业知识。为此，我们提出了实用策略，以更好地将LLM交互与健全的教学法对齐——例如，设计鼓励苏格拉底式提问、脚手架式指导和反思的提示，以及集成检索机制以确保准确性和上下文相关性。我们的目标是弥合教育理论与AI驱动的对话式学习新兴实践之间的差距，为使基于LLM的对话更具教育生产力和理论一致性提供见解和工具。
英文摘要：
Large Language Models (LLMs) are rapidly transforming education by enabling rich conversational learning experiences. This article provides a comprehensive review of how LLM-based conversational agents are being used in higher education, with extensions to secondary and lifelong learning contexts. We synthesize existing literature on LLMs in education and theories of conversational and dialogic pedagogy - including Vygotsky's sociocultural learning (scaffolding and the Zone of Proximal Development), the Socratic method, and Laurillard's conversational framework - and examine how prompting strategies and retrieval-augmented generation (RAG) can align LLM behaviors with these pedagogical theories, and how it can support personalized, adaptive learning. We map educational theories to LLM capabilities, highlighting where LLM-driven dialogue supports established learning principles and where it challenges or falls short of traditional pedagogical assumptions. Notable gaps in applying prior theories to LLMs are identified, such as the models tendency to provide direct answers instead of fostering co-construction of knowledge, and the need to account for the constant availability and broad but non-human expertise of LLM tutors. In response, we propose practical strategies to better align LLM interactions with sound pedagogy - for example, designing prompts that encourage Socratic questioning, scaffolded guidance, and student reflection, as well as integrating retrieval mechanisms to ensure accuracy and contextual relevance. Our aim is to bridge the gap between educational theory and the emerging practice of AI-driven conversational learning, offering insights and tools for making LLM-based dialogues more educationally productive and theory-aligned.
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。
