学校教育与人工智能深度融合的演进梳理、实施机制与推进策略  

The Evolution,Implementation Mechanism,and Promotion Strategies of Deep Integration of School Education and Artificial Intelligence

作　　者：戴岭 Dai Ling(East China Normal University)

机构地区：[1]华东师范大学教育学部 [2]新加坡南洋理工大学国立教育学院

出　　处：《终身教育研究》2025年第2期28-37,共10页Lifelong Education Research

基　　金：国家社会科学基金重大项目“信息化促进新时代基础教育公平的研究”(18ZDA335)。

摘　　要：人工智能在推进教育数字化建设、赋能教育高质量发展中发挥着重要作用。人工智能技术的发展历经符号主义与逻辑推理主导的萌芽期、专家系统与知识驱动的探索期、机器学习与统计模型崛起的转型期、深度学习与大数据驱动的爆发期,以及以大语言模型为代表的通用人工智能前沿期五个时期,与教育的融合可分为浅层交互、系统优化和深度融合三个阶段。每一技术节点的突破都进一步促进了人工智能与教育融合的进程,揭示出技术革新在教学范式塑造中的强大驱动力。人工智能与教育的深度融合不仅是技术赋能教育发展的应用手段,更是对教育组织形态和教育生态的彻底重塑。基于教育变革理论和行动者网络理论,进一步推进学校教育与人工智能深度融合的实施机制涵盖需求分析与规划、资源配置与管理、应用过程与运行、评估与持续改进等多个环节,需建立数据驱动的动态需求识别与动态规划机制,构建智能调控的资源配置结构与协同管理体系,设计自适应智能教学系统与适应性学习环境,完善基于学习分析的精细化评估体系与闭环反馈机制。Artificial intelligence plays a pivotal role in advancing digital education and empowering high-quality educational development.The evolution of AI technology spans five distinct phases:the embryonic phase dominated by symbolism and logical reasoning,the exploration phase driven by the expert system and knowledge,the transformation phase of the rise of machine learning and statistical models,the outbreak phase driven by deep learning and big data,and the frontier phase of general AI represented by large language models.The integration of AI into education can be categorized into three stages:shallow interaction,system optimization,and deep fusion.The breakthrough of each technological node has further illuminated the progress of AI-education integration,underscoring the transformative force of technological innovation in shaping educational paradigm.The deep integration of AI and education is not merely a technological enhancement to drive educational development but rather a comprehensive transformation of educational organizational structures and ecosystems.Based on Educational Change Theory and Actor-Network Theory,the mechanisms for advancing the deep integration of AI in school education encompass multiple components:demand analysis and strategic planning,resource allocation and management,application processes and operationalization,as well as evaluation and continuous improvement.This comprehensive framework requires the establishment of data-driven dynamic demand identification and planning,the construction of an intelligent resource allocation and collaborative management system,the design of adaptive intelligent teaching system and adaptive learning environment,and the enhancement of precision-based evaluation systems founded on learning analytics and closed-loop feedback mechanisms.

关 键 词：人工智能 人工智能教育应用 学校教育 深度融合 实施机制 

分 类 号：G63[文化科学—教育学]