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.

翻译：关于人工智能（AI）在教育领域应用的辩论，常将教学描绘成一种模块化、程序化的工作，可以逐渐被自动化或委托给技术。这篇简短通讯论文认为，此类论断的前提是将教学视为比实际更可分拆的活动。基于近期文献以及对大型语言模型和检索增强生成系统的实证研究，我主张：尽管AI能够支持某些边界明确的功能，但教学工作在本质上依然是解释性的、关系性的，且根植于专业判断，因此难以在真正意义上实现自动化。更深层而言，教学与学习受到人类认知、行为、动机与社会互动的塑造，这些因素无法被完整规定、预测或穷尽建模。那些在原则上看似可分拆的任务，其教学价值实际上源自实践过程中针对不同学习者、情境及关系的持续性情境化诠释。只要教育实践依赖于对人类认知与学习的涌现性理解，教学就仍然是一种抗拒自动化的专业工作。AI或许能改善信息获取渠道并支持某些选择性教学活动，但它无法消除有效教学所必需的人类判断与关系问责。