华为：2025大模型背景下高等教育数智化转型研究报告

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大 模 型 背 景 下 高等教育数智化转型研究报告 智 慧 之 钥 解 锁 未 来 潜 能 编委会 主编 孙鹏飞 吴永和 范小骞 副主编 肖玉敏 吴战杰 陈 浩 党张波 赵 爽 编委会委员 曹 鹏 陈慧娟 黄和平 谭 方 编者（按姓氏音序） 陈 明 陈圆圆 侯 磊 刘万飞 刘桢桢 明守刚 吴慧娜 吴 优 许秋璇 颜 欢 张文轩 郑 浩 摘 要 自 2022 年 ChatGPT 3.5 发布以来，以大模型为代表的人工智能技术突飞猛进，对教育变革带来巨大影响。教 育领域的数字化转型正在深度融合人工智能技术，加速迈向数智化转型。 本报告系统梳理国际与国内政策进展，深入分析国际社会高度重视人工智能教育应用的政策动向，以及国内以 人工智能为基础推动教育创新发展的政策布局。通过政策梳理与分析，提炼出“人才培养、科学研究、社会服 务与国际合作”四大导向，明确高等教育数智化转型的发展方向和战略目标。 报告从技术、社会与教育三个维度深入审视机遇与挑战：在技术层面，算力、数据与算法的突破孕育创新红利， 但面临安全、伦理、偏差与治理等现实约束；在社会层面，发展契机与潜在风险并存；在教育层面，知识去中 心化、学习个性化的进程中，需要同步重塑价值导向与能力结构。基于深入分析，报告提出推动高等教育走向 智能、高效、开放、可持续发展的总体目标，涵盖升级智慧教育环境建设、创新人才培养模式、促进跨学科和 人机协同科研、提升社会服务能力、加强国际交流合作、助力文化传承传播、建构数据驱动的教育治理体系等 七个重要方面。报告的核心关切聚焦于如何在拥抱人工智能技术创新的同时，建立有效的治理机制，并确保高 等教育的人文价值和社会责任得到维护。 围绕教育大模型的能力与要素，报告构建了完整的分析框架。在能力维度上，提出“通用能力＋教育能力”的 能力谱系；在要素分析上，形成“五要素”框架：算力作为构建基石与场景适配关键，数据作为必备燃料与领 域属性特征，算法作为核心引擎与风险应对策略，开发工具作为全栈式工具矩阵，安全、伦理和隐私保护作为 有效保障。在技术路线上，构建“参考架构—智能体应用—标准体系”的完整技术路径，以“性能—成本—应用” 协同优化为抓手，支撑模型从训练、推理、部署、协同到应用增强的全链路落地。 面向教育新范式，报告深入分析教育大模型赋能高等教育创新发展的具体路径，总结了九个重构方向：提供精 准适需的教育内容、实现个性灵活的教学方式、支持沉浸互动的学习体验、重塑教育主体的角色与能力、助力 数据驱动的教育评价、推动智能高效的教育治理、构建安全可信的伦理治理体系、配置优质均衡的教育资源、 强化智能协同的科研创新。据此提出“统筹规划、分步建设、优选场景、协同发展”的教育大模型建设原则， 阐明基于通用大模型研发教育大模型的具体实践，倡导通过算力、数据、算法协同优化的工程创新来实现教育 大模型的高算效和高能效。 应用层面，报告系统归纳教学、管理、科研与社会服务等主要应用场景，汇聚典型案例包括 DeepSeek 助力数 字化实训、大模型赋能智慧教室、人工智能数智化学习新模式、师范生实践教学能力提升、在线个性化学习、 全栈人工智能科研创新等，验证了智能导学、人工智能助教、智慧教室、数字化实训与科研助手等应用的实践 成效，充分展现了教育大模型在提升教学质量、优化学习体验、促进科研创新等方面的显著价值。 治理层面，报告从四个维度提出系统性对策：法制建设与标准化工作方面，完善法律法规保障教育应用的规范 性，落实标准体系建设；数据安全与伦理隐私方面，实施安全监测与防范，开展伦理治理与监督，强化隐私保 护与管控；模型演进与技术支撑方面，注重算法优化与迭代，数据规范与优化，算力支持与扩展；内容高质量 与教育包容性方面，强调内容管理与优化监管，采取包容策略促进教育发展的普惠性。构建“政府—高校—企业— 社会组织”多主体协同的治理框架，强调可解释性、公平性、可靠性与绿色低碳。 面向未来，报告从五个方面提出发展愿景：构建智算生态体系赋能智慧教育环境，重塑高教专业体系优化人才 培养机制，变革知识生产范式重塑科研新样态，推动教育公平承担社会责任，着眼共同发展为世界高等教育提 供中国方案。同时，报告正视大模型应用中的算法偏差与“黑箱”问题、内容同质化风险、知识产权与数据权 益等挑战，强调强化教师与管理者人工智能素养，保护学生主体性与创造力，完善数据确权与合规流通机制， 推进绿色计算与可持续运营。 总体而言，本报告以“以人为本、立德树人”为根本遵循，主张以标准化与场景化并重、能力建设与制度供给 并行的策略，持续提升高等教育的质量、效率与公平，协同共创智能、高效、开放、可持续的高等教育新格局， 实现人机协同共创高等教育美好的未来。通过深入分析大模型技术对高等教育的深远影响，提出切实可行的发 展路径和治理对策，为推动我国高等教育数智化转型提供了重要的理论指导和实践参考。 关键词 教育大模型，高等教育数智化转型，人机协同，教育场景，个性化学习，智能教育治理，数据安全，伦理隐私， 技术标准，参考架构 目录 第一章 赋能高等教育数智化转型的政策概览 ···················· 1 1.1 政策概述：世界各地人工智能赋能教育的政策 ····································· 2 1.1.1 国际政策：高度重视人工智能的影响，发布政策推动应用 ································ 2 1.1.2 国内政策：以人工智能为基础，推动教育创新发展 ···································· 12 1.2 政策导向：明晰智能时代高等教育数智化转型的战略方向 ··························· 20 1.2.1 人才培养导向：优化教育教学方式，培养高素质人工智能人才 ··························· 20 1.2.2 科学研究导向：加强基础与应用研究，推动人工智能技术健康发展与深化应用 ·············· 21 1.2.3 社会服务导向：履行社会责任，利用人工智能推动社会全面进步 ························· 23 1.2.4 国际交流合作导向：加强国际合作，共同应对人工智能的挑战与机遇 ······················ 23 1.3 智能时代高等教育的发展战略 ················································ 25 第二章 大模型背景下高等教育体系的重塑 ···················· 27 2.1 大模型赋能高等教育的机遇与挑战 ············································ 29 2.1.1 技术层面：算力、数据、算法的突破机遇与三重隐忧并存 ······························· 29 2.1.2 社会层面：发展契机与潜在风险并存 ··············································· 30 2.1.3 教育层面：知识平权化机遇与价值重塑挑战并存 ······································ 33 2.2 推动高等教育走向智能、高效、开放、可持续发展 ································ 35 2.2.1 升级智慧教育环境建设 ·························································· 35 2.2.2 创新人才培养模式 ····························································· 36 2.2.3 促进跨学科和人机协同的科研 ···················································· 37 2.2.4 提升社会服务能力和水平 ························································ 38 2.2.5 加强国际交流合作 ····························································· 38 2.2.6 助力文化传承与传播 ··························································· 39 2.2.7 建构数据驱动的教育治理体系 ···················································· 40 第三章 大模型赋能高等教育数智化转型 ······················ 41 3.1 教育大模型能力维度分析 ··················································· 43 3.1.1 大模型通用能力分析 ··························································· 43 3.1.2 教育大模型的教育能力分析 ······················································ 44 3.2 教育大模型核心要素分析 ··················································· 45 3.2.1 算力：教育大模型的构建基石与场景适配关键 ········································ 46 3.2.2 数据：教育大模型的必备燃料与领域属性特征 ········································ 47 3.2.3 算法：教育大模型的核心引擎与风险应对策略 ········································ 48 3.2.4 开发工具：教育大模型全栈开发工具矩阵 ··········································· 49 3.2.5 安全、伦理和隐私保护：教育大模型建设和应用的有效保障 ····························· 50 3.3 教育大模型赋能高等教育创新发展 ············································ 51 3.3.1 提供精准适需的教育内容 ························································ 52 3.3.2 实现个性灵活的教学方式 ························································ 52 3.3.3 支持沉浸互动的学习体验 ························································ 53 3.3.4 重塑教育主体的角色与能力 ······················································ 54 3.3.5 助力数据驱动的教育评价 ························································ 55 3.3.6 推动智能高效的教育治理 ························································ 55 3.3.7 构建安全可信、普惠包容的伦理治理体系 ··········································· 56 3.3.8 配置优质均衡的教育资源 ························································ 57 3.3.9 强化智能协同的科研创新 ························································ 58 第四章 教育大模型技术架构与标准体系 ······················ 60 4.1 教育大模型的参考架构 ····················································· 61 4.1.1 教育大模型总体参考框架 ························································ 61 4.1.2 教育大模型智能体应用 ·························································· 63 4.2 教育大模型的核心技术 ····················································· 64 4.2.1 从训练推理、部署、协同到应用增强的核心技术解析 ·································· 65 4.2.2 以 DeepSeek 代表的“性能－成本－应用”技术协同创新突破 ··························· 66 4.3 教育大模型的标准体系 ····················································· 67 4.3.1 教育大模型的标准架构 ·························································· 68 4.3.2 教育大模型设计和开发的原则 ···················································· 69 4.4 教育大模型的实施路径 ····················································· 71 4.4.1 以算力、数据、算法的协同优化实现高算效和高能效 ·································· 71 4.4.2 以统筹规划、分步建设、优选场景和协同发展增强现实性 ······························· 73 4.4.3 以通用大模型为基础开展教育大模型研发 ··········································· 73 第五章 教育大模型应用探索与实践 ·························· 76 5.1 教育大模型应用主要场景分析 ················································ 77 5.2 教育大模型应用典型案例分享 ················································ 81 5.2.1 案例一：DeepSeek 助力数字化实训赋能数智人才培养 ································· 81 5.2.2 案例二：大模型赋能智慧教室助力教学模式创新发展 ·································· 82 5.2.3 案例三：DeepSeek 构建人工智能数智化学习新模式 ·································· 83 5.2.4 案例四：大模型赋能师范生实践教学能力提升 ········································ 84 5.2.5 案例五：大模型全链路支持在线个性化学习 ········································· 85 5.2.6 案例六：全栈人工智能解决方案赋能高校科研创新 ···································· 86 第六章 教育大模型治理 ·································· 88 6.1 法制建设与标准化工作 ····················································· 89 6.1.1 完善法律法规，保障教育应用的规范性 ············································· 90 6.1.2 落实标准体系建设，促进教育大模型的可持续发展 ···································· 91 6.2 数据安全与伦理隐私 ······················································· 92 6.2.1 实施安全监测与防范，确保教育数据的安全性 ········································ 92 6.2.2 开展伦理治理与监督，保障数据使用的道德性 ········································ 93 6.2.3 强化隐私保护与管控，维护教育数据的合规性 ········································ 94 6.3 模型演进与技术支撑 ······················································· 95 6.3.1 算法优化与迭代，确保教育应用的可靠性 ··········································· 95 6.3.2 数据规范与优化，提升教育模型的准确性 ··········································· 96 6.3.3 算力支持与扩展，提升教育服务的可及性 ··········································· 97 6.4 内容高质量与教育包容性 ··················································· 98 6.4.1 注重内容管理与优化监管，保障生成内容科学性 ······································ 98 6.4.2 采取包容策略与方法，促进教育发展的普惠性 ········································ 99 第七章 大模型背景下的高等教育未来展望 ····················101 7.1 构建智算生态体系：赋能智慧教育环境 ········································ 102 7.2 重塑高教专业体系：优化人才培养机制 ········································ 104 7.3 变革知识生产范式：重塑科研新样态 ·········································· 104 7