为何在人工智能泛滥的时代教学抵抗自动化：人类判断、非模块化工作与委托的局限
Why teaching resists automation in an AI-inundated era: Human judgment, non-modular work, and the limits of delegation
AI总结：
本文探讨了在人工智能普及背景下，教学工作难以自动化的原因，指出教学本质上具有解释性、关联性和专业判断，无法被完全自动化或委托给技术。
摘要：
关于教育中人工智能的讨论往往将教学视为一个模块化和程序化的工作，可以越来越多地被自动化或委托给技术。本文简短的通信论文认为，这种说法依赖于将教学视为比实践中更可分离的假设。基于最近对大型语言模型和检索增强生成系统的研究，我论证尽管人工智能可以支持某些有限功能，但教学工作仍然难以以有意义的方式自动化，因为其本质上是解释性、关联性和基于专业判断的。更根本的是，教学和学习受人类认知、行为、动机和社会互动的影响，这些影响无法完全指定、预测或穷尽地建模。在原则上可能看似可分离的任务，其实际教学价值源于学习者、情境和关系之间的持续上下文解释。只要教育实践依赖于对人类认知和学习的新兴理解，教学仍将是一种抵抗自动化的专业工作。人工智能可能提高信息获取的便利性，并支持某些教学活动，但并不能消除有效教学所需的人类判断和关系责任。
英文摘要：
Debates about artificial intelligence (AI) in education often portray teaching as a modular and procedural job that can increasingly be automated or delegated to technology. This brief communication paper argues that such claims depend on treating teaching as more separable than it is in practice. Drawing on recent literature and empirical studies of large language models and retrieval-augmented generation systems, I argue that although AI can support some bounded functions, instructional work remains difficult to automate in meaningful ways because it is inherently interpretive, relational, and grounded in professional judgment. More fundamentally, teaching and learning are shaped by human cognition, behavior, motivation, and social interaction in ways that cannot be fully specified, predicted, or exhaustively modeled. Tasks that may appear separable in principle derive their instructional value in practice from ongoing contextual interpretation across learners, situations, and relationships. As long as educational practice relies on emergent understanding of human cognition and learning, teaching remains a form of professional work that resists automation. AI may improve access to information and support selected instructional activities, but it does not remove the need for human judgment and relational accountability that effective teaching requires.
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。
