Human
Interdependence
Paradigm Human
Interdependence
Paradigm

Artificial Intelligence and Education: End the Grammar of Schooling 人工智能与教育：终结学校教育的语法

This article explores how artificial intelligence can fundamentally transform the structures and practices of education that have persisted for over a century — what has been called the "grammar of schooling." It examines why these deeply entrenched patterns of age-graded classrooms, standardized curricula, and uniform assessments have resisted change despite decades of reform efforts. The article argues that AI presents a unique opportunity to finally disrupt these patterns by enabling truly personalized learning experiences, reimagining the role of teachers, and creating new possibilities for how, when, and where learning occurs. 本文探讨人工智能如何从根本上改变持续了一个多世纪的教育结构和实践——即所谓的"学校教育的语法"。文章审视了按年龄分级的课堂、标准化课程和统一评估等根深蒂固的模式为何在数十年的改革努力下仍然顽固不化。文章认为，人工智能提供了一个独特的机会，通过实现真正个性化的学习体验、重新构想教师角色以及创造学习方式、时间和地点的新可能性，最终打破这些模式。

Education Paradigm Shifts in the Age of AI: A Spatiotemporal Analysis of Learning AI时代的教育范式转换：学习的时空分析

This article offers a spatiotemporal analysis of education paradigm shifts in the age of artificial intelligence. It examines how AI is reshaping when, where, and how learning occurs, moving beyond fixed school structures toward more flexible, personalized, and networked forms of learning. By analyzing the temporal and spatial dimensions of educational change, the paper argues that the age of AI requires a fundamental rethinking of learning environments, institutional arrangements, and the relationships among learners, teachers, and knowledge. 本文对人工智能时代教育范式转变进行了时空分析。文章考察人工智能如何重塑学习发生的时间、空间和方式，推动教育从固定的学校结构走向更灵活、个性化和网络化的学习形态。通过分析教育变革的时间维度与空间维度，论文指出，AI时代要求我们从根本上重新思考学习环境、制度安排以及学习者、教师与知识之间的关系。

The Death and Rebirth of Research in Education in the Age of AI: Problems and Promises 人工智能时代教育研究的消亡与重生：问题与前景

This article critically examines the enduring problems and emerging possibilities of educational research in light of rapid advances in artificial intelligence (AI). It seeks to understand why educational research has struggled to influence practice and policy meaningfully and explores how AI necessitates a fundamental rethinking of research purposes, methods, and epistemologies. The article identifies and analyzes seven major problems in traditional educational research, including flaws in peer review, quantification bias, methodological fragmentation, overgeneralization, neglect of individual learner diversity, limited educational imagination, and narrow outcome measures. It then explores how AI technologies challenge and reshape core assumptions about knowledge production and educational inquiry. 本文批判性地审视了在人工智能快速发展背景下教育研究中持续存在的问题和新兴的可能性。文章试图理解为什么教育研究一直难以对实践和政策产生有意义的影响，并探讨人工智能如何要求从根本上重新思考研究目的、方法和认识论。文章识别并分析了传统教育研究中的七大问题，包括同行评审缺陷、量化偏见、方法论碎片化、过度概括化、忽视个体学习者多样性、有限的教育想象力和狭隘的成果衡量标准。随后探讨了人工智能技术如何挑战和重塑关于知识生产和教育探究的核心假设。

Education in the age of AI faces an epistemic paradox: It can increasingly observe learning through data and analytics, yet it still struggles to understand learning as a process of meaning. While modern systems exhibit a degree of technical reflexivity, their feedback remains largely procedural and detached from interpretation. Education in the age of AI faces an epistemic paradox: It can increasingly observe learning through data and analytics, yet it still struggles to understand learning as a process of meaning. While modern systems exhibit a degree of technical reflexivity, their feedback remains largely procedural and detached from interpretation.