中国电化教育

2026, 02, No.469 112-122

人工智能赋能个性化教学的实践路径与策略研究——基于美国、英国、芬兰三国的政策与行动分析

1.华中师范大学教育大数据应用技术国家工程研究中心 2.华中师范大学人工智能教育学部

基金项目(Foundation): 2025年度国家自然科学基金重点项目“面向智慧教育的多模态模型构建方法”（项目编号：62437002）; 2024年度国家自然科学基金面上项目“融合情绪感知与归因推理的异步讨论多策略组合干预方法研究”（项目编号：62377016）研究成果

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摘要：

在教育数字化转型持续推进的背景下,人工智能正深度重构个性化教学的实现机制。该研究以美国、英国和芬兰三国的主要政策和实践为主要分析对象,围绕基本思路、实践行动和典型项目进行文献分析。跨案例综合分析表明,三国的主要推进路径表现为教育政策引领、学习路径重构、教育资源适配、教师专业赋能、学生素养培育与公平普惠保障等六方面。基于上述研究发现,考量我国国情与发展现状,提出优化顶层设计与多部门协同、深化行为档案驱动机制、完善智能资源生成与适配体系、系统强化师生数字能力与AI素养、构建协同治理的数字公平生态等五项针对性的实施策略,以期为我国AI赋能大规模因材施教的本土化路径探索提供比较视野与实践启示。

关键词： 人工智能; 个性化教学; 国际比较; 教育政策;

Abstract：

Against the backdrop of the ongoing educational digital transformation, Artificial Intelligence(AI) is fundamentally reconstructing the operational mechanisms of personalized instruction. This study takes the major policies and practices of the United States, the United Kingdom, and Finland as its primary analytical objects, conducting document-analysis centered on their fundamental approaches, practical actions, and representative projects. Cross-case synthesis reveals that the principal advancement pathways in these three countries manifest in six key dimensions:educational policy leadership, learning pathway reconstruction, educational resource adaptation, teacher professional empowerment, student competency cultivation, and equity and universal benefit safeguards. Building upon these research findings and considering China' s national context and developmental status, this study proposes five targeted strategic recommendations: optimizing top-level design and multidepartmental coordination; deepening learner-profile-driven mechanisms; refining intelligent resource generation and adaptation systems; systematically strengthening the digital capabilities and AI literacy of both teachers and students; and constructing a collaboratively governed digital equity ecosystem. These recommendations aim to provide comparative perspectives and practical insights for exploring localized pathways for AIenabled personalized instruction in China.

KeyWords： Artificial Intelligence; personalized instruction; international comparison; education policy;

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参考文献

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(1)橡树国家学院(Oak National Academy, https://www. thenational. academy/)是由英国教育部支持的一家公益性在线教育平台,致力于提供高质量的数字课程资源与个性化学习支持,通过人工智能技术为教师和学生提供在线课程、练习和测验,帮助学生按照自身节奏实现有效学习。

(2)Century Tech(https://www. century. tech/)是英国一家AI赋能的个性化学习平台,提供自适应学习路径与实时反馈,促进学生自主学习。

(3)Sparx Maths(https://sparx-learning. com/)是一个聚焦数学教学的智能在线平台,通过AI驱动的个性化作业与即时反馈提升学生数学能力。

(4)Opin. fi(https://opin. fi)是芬兰的开放高等教育课程平台,汇聚高校共享的数字课程资源,支持学生跨校自主选课与个性化学习路径构建。

(5)Wilma系统(https://www. visma. fi/wilma/)是芬兰广泛使用的学校信息管理平台,支持学生、教师、家长在线协作与个性化学习路径管理。

(6)Claned(https://claned. com/)是芬兰开发的AI赋能学习平台,通过学习分析与智能推荐帮助学习者实现个性化、高效学习。

(1)Jisc(https://www. jisc. ac. uk)是英国专注于教育与研究数字化转型的非营利机构,为大学、学院和研究机构提供数字基础设施、技术标准和创新解决方案。其在人工智能、学习分析、教育数据互操作等领域引领政策研究与实践探索。

基本信息:

中图分类号:G434;G511

引用信息:

[1]刘智,余曼丽,龙陶陶,等.人工智能赋能个性化教学的实践路径与策略研究——基于美国、英国、芬兰三国的政策与行动分析[J].中国电化教育,2026,No.469(02):112-122.

基金信息:

2025年度国家自然科学基金重点项目“面向智慧教育的多模态模型构建方法”（项目编号：62437002）; 2024年度国家自然科学基金面上项目“融合情绪感知与归因推理的异步讨论多策略组合干预方法研究”（项目编号：62377016）研究成果

发布时间：

2026-02-10

出版时间：

2026-02-10

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