Rethinking Higher Education/Chapter 1/en-zh

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Ethical Frameworks for AI in Higher Education: Between European Regulation and Chinese Innovation	(高等教育中人工智能的伦理框架：欧洲规范与中国创新之间)
Martin Woesler	(Martin Woesler)
'Abstract'	(摘要)
The rapid integration of artificial intelligence into higher education has outpaced the development of ethical and regulatory frameworks to govern it. This article provides a systematic comparison of the European Union‘s AI Act — the world’s first comprehensive AI legislation — and China‘s evolving sector-specific approach to AI governance in education. The EU AI Act classifies educational AI applications such as automated grading, proctoring, and admissions decisions as „high-risk,“ bans emotion recognition in educational settings, and mandates AI literacy for all deployers. China’s approach, by contrast, combines the 2023 Interim Measures for Generative AI Services with Ministry of Education directives, mandatory AI education from September 2025, and institution-specific guidelines such as Fudan University’s January 2026 policy. Drawing on empirical data from the EDUCAUSE 2024 AI Landscape Study, the HEPI 2025 UK student survey, and comparative legal analyses, we document a significant „readiness gap“ in both systems: 80 percent of faculty use AI tools, yet fewer than one in four are aware of their institution’s AI policy. We argue that neither the EU’s horizontal, rights-based approach nor China’s centralized, sector-specific model is sufficient alone. A synthesis combining European ethical rigour with Chinese implementation speed offers the most promising path toward responsible AI integration in higher education.	(人工智能迅速融入高等教育，其发展速度已超越了相关伦理与监管框架的制定。本文对欧盟《人工智能法案》（全球首部综合性人工智能立法）与中国在教育领域逐步形成的针对特定场景的人工智能治理方法进行了系统比较。欧盟《人工智能法案》将自动评分、在线监考及招生决策等教育人工智能应用划定为“高风险”，禁止在教育场景中使用情绪识别技术，并要求所有部署方具备人工智能素养。相比之下，中国的路径结合了《生成式人工智能服务管理暂行办法》（2023年）、教育部的相关指令、自2025年9月起推行的人工智能必修教育，以及各高校自行制定的指导方针，例如复旦大学于2026年1月出台的政策。借助EDUCAUSE《2024年人工智能 landscape 研究》、HEPI《2025年英国学生调查》的实证数据以及比较法律分析，我们发现两套体系中都存在显著的“准备就绪差距”：80%的教职员工使用人工智能工具，但了解所在机构人工智能政策的人员比例不足四分之一。本文认为，无论是欧盟基于权利保障的横向监管路径，还是中国集中式、面向特定场景的模式，单独依靠任何一种都不足以解决问题。将欧洲的伦理严谨性与中国的实施速度相结合，方能为人工智能在高等教育中的负责任融合提供最具前景的路径。)
Keywords: AI ethics, EU AI Act, higher education, China AI governance, AI literacy, academic integrity, proctoring, Brussels Effect, generative AI policy	(关键词： 人工智能伦理；欧盟《人工智能法案》；高等教育；中国人工智能治理；人工智能素养；学术诚信；在线监考；布鲁塞尔效应；生成式人工智能政策)
'1. Introduction'	(1. 引言)
On 1 August 2024, the European Union‘s Artificial Intelligence Act entered into force — the world’s first comprehensive legislation governing artificial intelligence. Among its most consequential provisions for higher education are the classification of educational AI systems as „high-risk,“ the prohibition of emotion recognition in schools and universities, and the mandate that all organizations deploying AI systems ensure adequate AI literacy among their staff. These provisions, which took effect in stages beginning February 2025, establish a regulatory framework without precedent in any other jurisdiction.	(2024年8月1日，欧盟《人工智能法案》正式生效，这是全球首部全面规范人工智能的立法文件。该法案对高等教育影响最为深远的规定包括：将教育领域的人工智能系统划定为“高风险”，禁止在中小学校及大学中使用情绪识别技术，以及要求所有部署人工智能系统的组织确保其员工具备充分的人工智能素养。这些规定自2025年2月起分阶段生效，建立了一个在任何其他司法管辖区都前所未有的监管框架。)
Meanwhile, China has pursued a different path. Rather than a single horizontal law governing all AI applications, China has developed a series of sector-specific regulations — the 2023 Interim Measures for the Management of Generative AI Services, Ministry of Education directives prohibiting AI-submitted academic work, and the September 2025 mandate for AI education in all primary and secondary schools — that collectively govern AI in education through a combination of national policy, institutional guidelines, and technical standards.	(与此同时，中国走上了一条不同的路径。中国没有制定一部横向规制所有人工智能应用的统一法律，而是制定了一系列针对特定领域的法规——《生成式人工智能服务管理暂行办法》（2023年）、教育部关于禁止提交人工智能生成学术作品的指令，以及自2025年9月起在所有中小学推行人工智能教育的强制性要求——通过国家政策、机构指导方针与技术标准相结合的方式，共同对教育领域中的人工智能应用实施治理。)
This article compares these two approaches to AI governance in education, examining their philosophical foundations, practical implications, and effectiveness in addressing the ethical challenges that AI poses for higher education. It contributes to the broader anthology project by connecting the regulatory dimension with the companion chapters on AI in language learning, data protection, and the university of the future (Woesler, this volume).	(本文比较了这两种教育领域人工智能治理的路径，考察了它们的哲学基础、实践意涵以及在应对人工智能给高等教育带来的伦理挑战方面的有效性。本文通过将监管维度与本论文集中关于语言学习中的人工智能、数据保护以及未来大学等相关章节（参见 Woesler，本卷）相衔接，为这一更广泛的文集项目做出了贡献。)
Three questions structure our inquiry. First, how do the EU and Chinese regulatory frameworks address the specific ethical challenges of AI in education — academic integrity, surveillance, fairness, and transparency? Second, what is the current state of institutional readiness for AI governance in both systems? Third, what can each system learn from the other?	(本研究围绕三个核心问题展开。第一，欧盟与中国的监管框架如何应对人工智能在教育领域所带来的具体伦理挑战，即学术诚信、监控、公平性与透明度？第二，两套体系中机构层面的人工智能治理准备就绪程度当前处于何种状态？第三，两套体系之间能够相互借鉴什么？)
'2. The EU AI Act and Education'	(2. 欧盟《人工智能法案》与教育)
'2.1 High-Risk Classification'	(2.1 高风险分类)
The EU AI Act (Regulation 2024/1689) establishes a risk-based framework that categorizes AI systems into four tiers: unacceptable risk (prohibited), high risk (regulated), limited risk (transparency obligations), and minimal risk (unregulated). Education figures prominently in the high-risk category. Annex III, Section 3 identifies four categories of educational AI as high-risk: AI systems used to determine access to educational institutions, AI systems evaluating learning outcomes including systems used to steer the learning process, AI systems assessing the appropriate level of education for an individual, and AI systems monitoring and detecting prohibited behaviour of students during tests (European Parliament and Council 2024).	(欧盟《人工智能法案》（第2024/1689号条例）建立了一个基于风险的分类框架，将人工智能系统划分为四个层级：不可接受风险（禁止）、高风险（受规制）、有限风险（透明度义务）以及最小风险（不受规制）。教育在高风险类别中占据突出位置。附件三第3节将四类教育领域的人工智能系统认定为高风险：用于决定是否录取进入教育机构的人工智能系统；用于评估学习成果的人工智能系统，包括用于引导学习过程的系统；用于评估个人适合接受何种教育水平的系统；以及用于监测和检测考试中学生被禁止行为的系统（欧洲议会与欧盟理事会，2024年）。)
The practical consequences are significant. Any university deploying an AI system that grades examinations, recommends study paths, determines admissions, or monitors student behaviour during assessments must comply with extensive obligations: conducting conformity assessments, maintaining technical documentation, implementing human oversight mechanisms, and ensuring data quality. These requirements apply regardless of whether the AI system is developed in-house or procured from a commercial provider.	(其实际影响十分重大。任何部署人工智能系统用于评分考试、推荐学习路径、决定录取或监控学生在考核期间行为的高校，都必须履行广泛的合规义务：进行符合性评估、维护技术文档、实施人工监督机制以及确保数据质量。无论该人工智能系统是校内自主开发还是从商业供应商处采购，这些要求均适用。)
'2.2 The Emotion Recognition Ban'	(2.2 情绪识别禁令)
Article 5(1)(f) of the AI Act prohibits AI systems that infer emotions of natural persons in the areas of workplaces and educational institutions, except where the AI system is intended to be put into service for medical or safety reasons. This prohibition, which took effect on 2 February 2025, has direct implications for AI-powered proctoring systems that monitor facial expressions, eye movements, and other biometric indicators to detect cheating during examinations. Several commercial proctoring platforms that rely on emotion inference — interpreting nervousness, distraction, or stress as indicators of academic dishonesty — may fall within the scope of this prohibition.	(《人工智能法案》第5条第1款(f)项禁止在工作场所和教育机构领域内推断自然人情绪的人工智能系统，但出于医疗或安全原因而投入使用的系统除外。该禁令于2025年2月2日生效，对通过监测面部表情、眼球运动及其他生物特征指标来检测考试中作弊行为的人工智能监考系统具有直接影响。若干依赖情绪推断——将紧张、分心或压力解释为学术不诚实信号——的商业监考平台可能落入该禁令的规制范围。)
The emotion recognition ban reflects a distinctly European approach to the relationship between technology and human dignity. The EU’s position is that monitoring emotional states in educational contexts constitutes an unacceptable intrusion into students’ psychological integrity, regardless of the stated purpose. This position is informed by the Charter of Fundamental Rights of the European Union, which guarantees the right to human dignity (Article 1) and the right to the protection of personal data (Article 8).	(情绪识别禁令反映了欧洲在处理技术与人的尊严之间关系时所持有的独特路径。欧盟的立场是，在教育场景中监测情绪状态，无论其宣称的目的为何，均构成对学生心理完整性不可接受的侵扰。这一立场源于《欧盟基本权利宪章》，该宪章保障了人的尊严权（第1条）以及个人数据受保护权（第8条）。)
'2.3 The AI Literacy Mandate'	(2.3 人工智能素养强制要求)
Article 4 of the AI Act requires that providers and deployers of AI systems ensure that their staff and other persons dealing with the operation and use of AI systems on their behalf have a sufficient level of AI literacy. This obligation entered into force on 2 February 2025 — before most other provisions of the Act — signalling the EU’s view that AI literacy is a prerequisite for responsible AI governance.	(《人工智能法案》第4条要求人工智能系统的提供者与部署者，确保其员工以及代表其处理人工智能系统运行与使用事宜的其他人员具备充分的人工智能素养。该义务于2025年2月2日生效——早于该法案的大部分其他条款——这表明欧盟认为，人工智能素养是负责任的人工智能治理的先决条件。)
For universities, this mandate has two dimensions. First, as deployers of AI systems (learning management systems, automated grading tools, plagiarism detection software, proctoring platforms), universities must ensure that faculty and administrative staff possess adequate AI literacy to use these tools responsibly. Second, as educational institutions, universities have a broader mission to develop AI literacy among their students — a mission that the AI Act’s literacy mandate reinforces without explicitly addressing.	(对高校而言，这一强制要求具有两个层面的内涵。第一，作为人工智能系统的部署者（如学习管理系统、自动评分工具、剽窃检测软件、监考平台等），高校必须确保教师与行政管理人员具备充分的人工智能素养，以便负责任地使用这些工具。第二，作为教育机构，高校肩负着培养学生人工智能素养的更广泛使命——这一使命得到了《人工智能法案》素养强制要求的强化，尽管法案本身并未对此予以明确阐述。)
The implementation challenge is substantial. The EDUCAUSE 2024 AI Landscape Study found that fewer than one in four faculty and staff were aware of their institution’s formal AI policy, even though 80 percent reported using AI tools in their work (EDUCAUSE 2024). This gap between AI use and AI awareness represents a significant institutional risk — and a direct challenge to the AI Act’s literacy mandate.	(实施方面的挑战十分严峻。EDUCAUSE《2024年人工智能格局研究》发现，尽管80%的教职员工报告称在工作中使用了人工智能工具，但了解所在机构正式人工智能政策的人员比例不足四分之一（EDUCAUSE，2024年）。这种人工智能使用与认知之间的差距构成了重大的机构风险——也是对《人工智能法案》素养强制要求的直接挑战。)
'3. China‘s AI Governance for Education'	(3. 中国教育领域的人工智能治理)
'3.1 The Interim Measures for Generative AI (2023)'	(3.1 《生成式人工智能服务管理暂行办法》（2023年）)
China‘s approach to AI governance in education operates through a layered system of national regulations, ministerial directives, and institutional policies. The foundational national regulation is the Interim Measures for the Management of Generative AI Services (生成式人工智能服务管理暂行办法), issued jointly by the Cyberspace Administration of China and six other agencies including the Ministry of Education, effective 15 August 2023. The Measures adopt what Chinese legal scholars describe as an „inclusive prudence“ (包容审慎) approach, combining development promotion with classified supervision (Migliorini 2024).	中国在教育领域的人工智能治理路径，通过由国家法规、部委指令与机构政策构成的多层级体系运行。基础性的国家法规是由国家互联网信息办公室联合教育部等其他六个部门共同发布的《生成式人工智能服务管理暂行办法》，自2023年8月15日起施行。该办法采取中国法学学者所描述的“包容审慎”路径，将促进发展与分类监管相结合（Migliorini，2024年）。
For education specifically, the Interim Measures include provisions requiring that generative AI service providers prevent minors from developing „excessive reliance on or addiction to“ generative AI, and that services targeting minors meet additional safety requirements. Notably, the Measures include a research exemption that permits universities and research institutes to develop and test generative AI systems without the full registration and compliance requirements applicable to commercial providers — a pragmatic recognition that overly restrictive regulation could impede academic research.	(具体到教育领域，《暂行办法》包含若干条款，要求生成式人工智能服务提供者防止未成年人“过度依赖或沉迷于”生成式人工智能，且面向未成年人的服务须满足额外的安全要求。值得注意的是，该办法设有一项研究豁免，允许高校及科研机构开发与测试生成式人工智能系统，无需履行适用于商业提供者的完整备案与合规要求——这是对过度限制性监管可能阻碍学术研究这一现实问题的务实承认。)
'3.2 Ministry of Education Directives'	(3.2 教育部专项指令)
The Ministry of Education has issued several directives specifically addressing AI in education. Most significantly, the Ministry has prohibited students from submitting AI-generated content as their own academic work — a directive that, while clear in principle, faces the same enforcement challenges encountered in other jurisdictions. Individual universities have supplemented this directive with institution-specific policies.	(教育部已发布若干专门针对教育领域人工智能应用的专项指令。其中最为重要的是，教育部禁止学生将人工智能生成内容作为本人学术成果提交——这一指令虽然在原则上清晰明确，但面临着与其他司法管辖区相同的执行挑战。各高校已通过制定校本政策对该指令进行补充。)
Fudan University became one of the first major Chinese universities to issue comprehensive AI guidelines in January 2026, establishing six specific prohibitions for thesis writing: banning the use of AI to generate or alter original data, experimental results, images, or thesis text; prohibiting AI tools in the thesis review process; and banning AI tools for language polishing and translation of theses. Penalties for severe violations include degree revocation (China Daily 2026).	(复旦大学于2026年1月成为中国首批发布全面人工智能指导方针的主要高校之一，针对论文写作设立了六项具体禁令：禁止使用人工智能生成或篡改原始数据、实验结果、图像或论文文本；禁止在论文评审过程中使用人工智能工具；以及禁止使用人工智能工具进行论文的语言润色与翻译。对严重违规行为的处罚包括撤销学位（《中国日报》，2026年）。)
Tianjin University of Science and Technology (天津科技大学) took a different approach in 2024, mandating that AI-generated content in undergraduate theses must not exceed 40 percent — a quantitative threshold that is measurable in principle but difficult to enforce in practice, particularly for texts that have been substantially rewritten by their human authors.	湖南师范大学
'3.3 Mandatory AI Education from September 2025'	(3.3 自2025年9月起推行的人工智能必修教育)
China‘s most sweeping policy intervention is the mandate, effective 1 September 2025, that AI education be incorporated into all primary and secondary school curricula. The policy requires at least eight hours of AI education annually for students at all grade levels, including children as young as six. At the primary level, the focus is on AI literacy and exposure; at the junior high school level, on logic and critical thinking; and at the senior high school level, on applied innovation and algorithm design (Asia Education Review 2025).	(中国最为全面的政策干预是自2025年9月1日起将人工智能教育纳入所有中小学课程体系的强制要求。该政策要求各年级学生（包括年仅六岁的儿童）每年接受至少八小时的人工智能教育。在小学阶段，重点在于人工智能素养的培养与基础认知；在初中阶段，侧重于逻辑与批判性思维；在高中阶段，则聚焦于应用创新与算法设计（《亚洲教育评论》，2025年）。)
This mandate has implications for higher education because it means that, within a few years, university students will arrive with a baseline of AI literacy — unlike the current generation, most of whom encounter AI tools for the first time at university. Chinese universities must therefore prepare to build on this foundation rather than starting from scratch, which will require fundamental curriculum redesign.	(这一强制要求对高等教育具有深远影响，因为这意味着在数年之内，进入大学的学生将具备一定基础的人工智能素养——这与当前一代学生形成鲜明对比，后者大多是在大学阶段才首次接触人工智能工具。因此，中国高校必须做好准备，在这一基础之上进行深化培养，而非从零开始，这将需要对课程体系进行根本性的重新设计。)
Simultaneously, the policy prohibits students at primary schools from independently using „open-ended content generation“ tools and bans teachers from using generative AI as a substitute for core teaching activities. This dual approach — promoting AI literacy while restricting certain AI uses — reflects a nuanced understanding that AI competence and AI dependence are distinct phenomena that require distinct policy responses.	(与此同时，该政策禁止小学生独立使用“开放式内容生成”工具，并禁止教师使用生成式人工智能替代核心教学活动。这种双重路径——在提升人工智能素养的同时限制某些人工智能用途——反映了一种精细化的理解，即人工智能能力与人工智能依赖是两种不同的现象，需要采取不同的政策应对。)
'4. Horizontal vs. Sector-Specific Regulation'	(4. 横向规制与针对特定场景的规制之比较)
'4.1 The EU’s Horizontal Approach'	(4.1 欧盟的横向规制路径)
The EU AI Act represents a horizontal regulatory strategy: a single legislative framework governing AI across all sectors, including education. The advantages of this approach include consistency (the same principles apply to AI in healthcare, employment, law enforcement, and education), legal certainty (organizations need to understand one framework rather than many), and comprehensiveness (no sector falls through the regulatory cracks).	(欧盟《人工智能法案》代表了一种横向规制策略：即通过一部统一的立法框架，对包括教育在内的所有行业领域中的人工智能应用进行规制。这一路径的优势包括：一致性（相同的原则适用于医疗、就业、执法及教育等领域的人工智能）、法律确定性（各组织只需理解一部框架而无需掌握多部法规）以及全面性（没有任何行业领域会落入监管的缝隙之中）。)
However, horizontal regulation also has disadvantages. The AI Act’s provisions are necessarily abstract — „high-risk“ classification criteria must accommodate AI systems as diverse as medical diagnostic tools and university admissions algorithms. This abstraction can make compliance challenging for specific sectors. Universities, accustomed to the relatively informal governance structures of academic life, may struggle to implement the formal conformity assessments, technical documentation requirements, and quality management systems that the AI Act demands of high-risk AI deployers.	(然而，横向规制也存在不足。《人工智能法案》的条款必然具有抽象性——“高风险”分类标准必须同时适用于医疗诊断工具与高校招生算法等迥异的人工智能系统。这种抽象性可能使特定行业领域的合规工作面临挑战。高校习惯于学术生活中相对非正式的治理结构，在落实《人工智能法案》对高风险人工智能部署者所要求的正式符合性评估、技术文档规范以及质量管理体系时，可能会遇到困难。)
'4.2 China‘s Sector-Specific Approach'	(4.2 中国针对特定场景的规制路径)
China‘s approach, by contrast, is predominantly sector-specific. Rather than a single comprehensive AI law, China has developed a series of targeted regulations addressing specific AI capabilities: the Deep Synthesis Provisions (2023) governing deepfakes, the Interim Measures for Generative AI (2023), the Algorithmic Recommendation Management Provisions (2022), and sector-specific directives from relevant ministries including the Ministry of Education.	(相比之下，中国的路径主要是针对特定场景的。中国并未制定一部统一的综合性人工智能法律，而是制定了一系列针对特定人工智能能力的精准法规：《深度合成管理规定》（2023年）用于规制深度伪造技术，《生成式人工智能服务管理暂行办法》（2023年），《互联网信息服务算法推荐管理规定》（2022年），以及包括教育部在内的相关部委发布的行业专项指令。)
As O’Shaughnessy and Sheehan (2023) observe in their analysis for the Carnegie Endowment for International Peace, „the EU’s AI Act leans toward a horizontal approach (one framework across all sectors), while China‘s algorithm regulations incline vertically (sector-specific laws targeting specific AI capabilities).“ Both approaches face trade-offs: horizontal regulation provides consistency but risks being too vague; vertical regulation provides specificity but risks gaps and overlaps between sectors.	(正如奥肖内西与希恩（2023年）在为其为卡内基国际和平基金会所作的分析中所指出的：“欧盟的《人工智能法案》倾向于采用横向路径（一个框架覆盖所有行业），而中国的算法法规则偏向纵向路径（针对特定人工智能能力制定的行业专项法律）。”两种路径均面临各自的权衡取舍：横向规制提供了一致性，但存在过于模糊的风险；纵向规制提供了针对性，但存在行业之间出现监管缝隙与重叠的风险。)
For education specifically, China‘s sector-specific approach has the advantage of allowing the Ministry of Education to issue directives tailored to educational contexts — distinguishing, for example, between AI use in research (broadly permitted) and AI use in examinations (broadly prohibited). The disadvantage is regulatory fragmentation: university administrators must navigate multiple overlapping regulations without a single authoritative framework.	(具体到教育领域，中国针对特定场景的规制路径具有一项优势，即允许教育部发布专门针对教育情境的指令——例如，区分人工智能在研究中的使用（广泛允许）与在考试中的使用（广泛禁止）。其劣势在于监管的碎片化：高校管理者必须在多重重叠的法规之间摸索前行，而缺乏一部统一且具有权威性的框架作为指引。)
'4.3 Comparative Assessment'	(4.3 比较评估)
A comprehensive comparative analysis of global AI regulation found that „many horizontal laws take a risk-based approach imposing stringent obligations on highest-risk systems, though definitions of ‘high-risk’ are inconsistent across jurisdictions“ (Royal Society Open Science 2025). The study confirmed that the EU favours a horizontal risk-based approach while China has favoured sector-specific laws tailored to specific use-cases, with neither approach clearly superior.	(一项关于全球人工智能监管的综合比较分析发现，“许多横向法律采取基于风险的路径，对最高风险系统施加严格的义务，但各司法管辖区对‘高风险’的定义并不一致”（《皇家学会开放科学》，2025年）。该研究证实，欧盟倾向于采用横向的基于风险的路径，而中国则倾向于针对特定使用场景制定的行业专项法律，两种路径均无明显优势。)
Chun, Schroeder de Witt, and Elkins (2024) characterize the fundamental difference as one of values: „The EU’s approach is characterised by commitment to fundamental rights, risk-based categorisation, and ethical oversight; China adopts a centralized, state-led approach integrating AI development with national objectives and social governance.“ For education, this means that EU regulation prioritizes protecting students’ rights (privacy, non-discrimination, human oversight), while Chinese regulation prioritizes both protecting students and advancing national AI competitiveness — goals that can converge but also conflict.	(Chun、Schroeder de Witt 与 Elkins（2024年）将两者之间的根本差异归结为价值观层面的不同：“欧盟的路径以对基本权利的承诺、基于风险的分类以及伦理监督为特征；中国则采取以国家为核心的集中式路径，将人工智能发展与国家目标及社会治理相结合。”就教育领域而言，这意味着欧盟的监管优先保护学生权利（隐私、不歧视、人工监督），而中国的监管则在保护学生的同时，优先推进国家人工智能竞争力——这两项目标既可能趋于一致，也可能产生冲突。)
'5. The Readiness Gap'	(5. 准备就绪差距)
'5.1 Faculty Awareness and Policy Implementation'	(5.1 教师认知与政策实施)
The most significant challenge facing both regulatory systems is not the adequacy of regulation itself but the gap between regulation and practice. Empirical data from multiple jurisdictions reveal a consistent pattern: faculty and students are using AI tools far more rapidly than institutions are developing policies to govern their use.	(两套监管体系所面临的最重大挑战，并非监管制度本身是否完善，而是制度与实践之间的差距。来自多个司法管辖区的实证数据揭示了一致的模式：教师与学生使用人工智能工具的速度，远远快于各机构制定相应使用规范的速度。)
The EDUCAUSE 2024 AI Landscape Study, based on a survey of over 900 higher education technology professionals, found that only 23 percent of institutions had AI-related acceptable use policies in place. Nearly half (48 percent) of respondents disagreed that their institution has appropriate AI policies for ethical decision-making, and 54 percent disagreed that their institution has an effective AI governance mechanism (EDUCAUSE 2024). Yet 80 percent of faculty and staff reported using AI tools, and fewer than one in four were aware of a formal institutional AI policy (EDUCAUSE 2024 Action Plan).	(EDUCAUSE《2024年人工智能格局研究》基于对900余名高等教育技术专业人员的调查发现，仅有23%的机构制定了与人工智能相关的可接受使用政策。近半数（48%）的受访者不认同其所在机构拥有用于伦理决策的适当人工智能政策，54%的受访者不认同其所在机构具备有效的人工智能治理机制（EDUCAUSE，2024年）。然而，80%的教职员工报告称使用了人工智能工具，而了解所在机构正式人工智能政策的人员比例不足四分之一（EDUCAUSE《2024年行动计划》）。)
This gap is not unique to the United States. In the UK, the Higher Education Policy Institute (HEPI) 2025 survey found that 92 percent of undergraduate students now use generative AI in some form, up from 66 percent in 2024 — a dramatic year-on-year increase demonstrating the rapid normalization of AI tool usage (HEPI 2025). Yet institutional policies have not kept pace with this adoption.	(这一差距并非美国所独有。在英国，高等教育政策研究所（HEPI）2025年的调查发现，目前已有92%的本科生以某种形式使用生成式人工智能，较2024年的66%大幅上升——这一显著的同比增长表明人工智能工具的使用正迅速走向常态化（HEPI，2025年）。然而，各机构的政策制定并未跟上这一应用普及的速度。)
'5.2 The Chinese Readiness Landscape'	(5.2 中国的准备就绪状况)
In China, Liu et al. (2025) investigated regulations, technology policies, and university attitudes to AI based on policy document analysis and 33 faculty interviews at Chinese research universities. They found that while faculty generally view AI as enhancing personalization, research productivity, and administrative efficiency, they raise concerns about academic integrity, algorithmic bias, and overreliance on technology. Crucially, China’s state-led governance model „is interpreted and enacted unevenly on university campuses“ — even centralized directives are implemented inconsistently at the institutional level (Liu et al. 2025).	(在中国，Liu等人（2025年）基于政策文件分析及对中国研究型大学33名教师的访谈，对相关法规、技术政策以及高校对人工智能的态度进行了调查。他们发现，虽然教师普遍认为人工智能能够提升个性化教学、科研效率与行政效能，但也对学术诚信、算法偏见及技术过度依赖等问题表示担忧。关键在于，中国以国家为主导的治理模式“在大学校园中的解读与执行并不均衡”——即使是集中式的指令，在机构层面的实施也不一致（Liu等人，2025年）。)
A comparative analysis of AI regulation across mainland China, Hong Kong, and Macau found that even within a single country, „varied governance structures produce divergent approaches to AI in education„ — demonstrating that regulatory fragmentation exists not only between the EU and China but within China itself (Liu et al. 2025b).	(一项针对中国大陆、香港及澳门地区人工智能监管的比较分析发现，即便在同一国家内部，“不同的治理结构也会产生各异的面向教育的人工智能路径”——这表明监管碎片化不仅存在于欧盟与中国之间，也存在于中国自身内部（Liu等人，2025b）。)
'5.3 Emerging Institutional Responses'	(5.3 新兴的机构层面应对措施)
Despite the readiness gap, some institutions are developing innovative responses. In Austria, the HEAT-AI (Higher Education Act for AI) framework, developed at a University of Applied Sciences, categorizes AI applications in higher education into four risk levels mirroring the EU AI Act — unacceptable, high, limited, and minimal risk — and provides institution-specific guidance for each category (Temper, Tjoa, and David 2025). The framework went live in September 2024 and represents an attempt to translate the abstract provisions of the AI Act into concrete institutional practice.	(尽管存在准备就绪差距，部分机构仍在制定创新性的应对措施。在奥地利，由一所应用科学大学开发的HEAT-AI（高等教育人工智能法案）框架，将高等教育中的人工智能应用划分为四个风险级别——不可接受风险、高风险、有限风险与最小风险——与欧盟《人工智能法案》的风险分级相对应，并为每个类别提供针对具体机构的指导（Temper、Tjoa 与 David，2025年）。该框架于2024年9月上线，代表着将《人工智能法案》的抽象条款转化为具体机构实践的一次尝试。)
A global study of emerging GenAI policies found that „only slightly more than half of examined institutions had publicly accessible GenAI guidelines, with most lacking formal policy at the institutional level“ (Aristombayeva et al. 2025). Policies addressing students and faculty are more prevalent than those addressing researchers — a gap that is particularly significant for research universities where AI is used extensively in the research process.	(一项针对新兴生成式人工智能政策的全球研究发现，“仅有略超过半数的受调查机构拥有可公开获取的生成式人工智能指导方针，且其中大多数缺乏机构层面的正式政策”（Aristombayeva 等人，2025年）。针对学生与教师的政策相较于针对研究人员的政策更为普遍——这一差距对于在研究过程中广泛使用人工智能的研究型大学而言尤为显著。)
'6. Surveillance vs. Support: The Proctoring Debate'	(监控与支持之辨：关于在线监考的争论)
'6.1 AI Proctoring in the EU Framework'	(6.1 欧盟框架下的人工智能监考)
The EU AI Act‘s classification of AI systems that monitor student behaviour during tests as „high-risk,“ combined with the emotion recognition ban, creates a regulatory environment that is significantly more restrictive than any other jurisdiction. AI proctoring systems that rely on facial expression analysis, gaze tracking, or keystroke dynamics to infer cheating must comply with the Act’s full set of high-risk obligations — or, if they involve emotion inference, may be prohibited entirely.	(欧盟《人工智能法案》将在考试期间监测学生行为的人工智能系统划定为“高风险”，加之情绪识别禁令，共同营造了一个比其他任何司法管辖区都更为严格的监管环境。依赖面部表情分析、视线追踪或击键动态来推断作弊行为的人工智能监考系统，必须遵守该法案全套的高风险合规义务——若涉及情绪推断，则可能被完全禁止。)
A systematic review of AI-based proctoring systems identified security and privacy concerns, ethical concerns, trust in AI-based technology, lack of training among users, and cost as the major issues (Nigam et al. 2021). The review noted that „security issues associated with AI-based proctoring systems are multiplying and are a cause of legitimate concern.“ These concerns align with the EU’s precautionary approach.	(一项针对基于人工智能的监考系统的系统综述，将安全与隐私问题、伦理关切、对人工智能技术的信任、用户缺乏培训以及成本列为主要问题（Nigam 等人，2021年）。该综述指出，“与基于人工智能的监考系统相关的安全问题正在成倍增加，构成了合理的关切。”这些关切与欧盟的预防性路径相一致。)
'6.2 AI Surveillance in Chinese Education'	(6.2 中国教育中的人工智能监控)
In China, the approach to AI-powered monitoring in education is markedly different. A comparative analysis of AI privacy concerns in higher education found that „Western outlets highlight individual privacy rights and controversies in remote exam monitoring, while Chinese coverage more frequently addresses AI-driven educational innovation“ (Xue et al. 2025). AI-powered surveillance systems in some Chinese universities — monitoring student behaviour, classroom engagement, and even facial expressions — have generated domestic debate, but the regulatory framework is more permissive than in the EU.	(在中国，对于教育中由人工智能驱动的监控所采取的方式，与欧盟截然不同。一项针对高等教育中人工智能隐私关切问题的比较分析发现，“西方媒体突出强调个人隐私权利以及远程考试监控中的争议，而中国的报道则更频繁地讨论人工智能驱动的教育创新”（Xue 等人，2025年）。部分中国高校中的人工智能监控系统——用于监测学生行为、课堂参与度乃至面部表情——已在国内引发了讨论，但相关的监管框架相较于欧盟更为宽松。)
The contrast reflects fundamentally different cultural and legal traditions regarding the relationship between individual privacy and collective benefit. In the EU framework, student monitoring is an intrusion that must be justified by compelling necessity and constrained by proportionality. In the Chinese framework, monitoring is more readily accepted as a component of educational management, provided it serves institutional and national objectives.	(这种对比反映了关于个人隐私与集体利益之间关系的、根本不同的文化与法律传统。在欧盟的框架中，对学生进行监控是一种需要以迫切的必要性加以证明、并受比例原则约束的侵扰。而在中国的框架中，只要监控服务于机构与国家目标，它便更容易被接受为教育管理的组成部分。)
'6.3 The Pedagogical Question'	(6.3 教学法层面的追问)
Beyond the legal and cultural dimensions, the proctoring debate raises a fundamental pedagogical question: does monitoring student behaviour during examinations improve educational outcomes, or does it merely optimize for compliance while undermining trust, intrinsic motivation, and the development of academic integrity as a personal value?	(除了法律与文化维度之外，关于在线监考的争论还引发了一个根本性的教学法问题：在考试期间监测学生行为，究竟是改善了教育成效，还是仅仅优化了合规性，同时却损害了信任、内在动机以及作为个人价值观的学术诚信的养成？)
The answer likely depends on context. In large-scale standardized assessments where the consequences of cheating are significant (professional licensing examinations, university entrance tests), some form of monitoring may be justifiable. In formative assessments designed to support learning rather than certify competence, monitoring may be counterproductive — replacing the educational relationship of trust between teacher and student with a surveillance relationship that undermines the very values it purports to protect.	(答案很可能取决于具体情境。在作弊后果较为严重的大规模标准化评估中（如职业资格考试、大学入学考试），某种形式的监控或许是合理的。而在旨在支持学习而非认证能力的发展性评估中，监控可能会适得其反——将师生之间基于信任的教育关系，替换为一种监控关系，从而损害其本欲保护的那些价值观。)
'7. The „Brussels Effect„ and Global AI Governance'	(7. “布鲁塞尔效应”与全球人工智能治理)
'7.1 The Brussels Effect in AI Regulation'	(7.1 人工智能监管中的“布鲁塞尔效应”)
The concept of the „Brussels Effect,“ theorized by Anu Bradford (2020), describes the EU’s capacity to unilaterally regulate global markets through stringent domestic regulation that multinational companies adopt worldwide for cost efficiency. The question of whether the AI Act will produce a Brussels Effect is actively debated.	(“布鲁塞尔效应”这一概念由安努·布拉德福德（2020年）提出理论化，描述了欧盟通过制定严格的国内法规，使得跨国公司出于成本效率考虑而在全球范围内采用这些标准，从而单方面规制全球市场的能力。《人工智能法案》是否会产生“布鲁塞尔效应”，目前正处在积极的讨论之中。)
Siegmann and Anderljung (2022) argue that „both de facto and de jure Brussels Effects are likely for parts of the EU AI regulatory regime,“ particularly for large US technology companies whose AI systems are classified as high-risk. For education, this means that AI tools developed by global companies (Microsoft, Google, OpenAI) for use in European universities will likely be designed to comply with the AI Act’s requirements — and that these compliance features may be exported to other markets, including China.	(Siegmann 与 Anderljung（2022年）认为，“无论是事实上的还是法律上的‘布鲁塞尔效应’，都有可能出现在欧盟人工智能监管体制的某些部分”，尤其是对于那些其人工智能系统被划定为高风险的美国大型科技公司而言。就教育领域而言，这意味着由全球性公司（微软、谷歌、OpenAI）开发、供欧洲高校使用的人工智能工具，很可能会按照《人工智能法案》的要求进行设计——而这些合规特性可能会被输出到包括中国在内的其他市场。)
However, Almada and Radu (2024) caution that the AI Act may also produce a „Brussels Side-Effect“: other jurisdictions may adopt the regulatory form (risk-based classification, documentation requirements) without the underlying rights-based substance that gives the EU framework its ethical force.	(然而，Almada 与 Radu（2024年）提醒指出，《人工智能法案》也可能产生一种“布鲁塞尔副作用”：其他司法管辖区可能采纳其监管形式（基于风险的分类、文档要求），却并不吸纳赋予欧盟框架以伦理力量的、作为根基的权利本位实质内容。)
'7.2 China‘s Global AI Governance Initiative'	(7.2 中国的全球人工智能治理倡议)
China has responded to the EU’s regulatory activism with its own vision for global AI governance. The Global AI Governance Initiative (全球人工智能治理倡议), announced by President Xi Jinping in October 2023, advocates a „people-centered approach“ to AI governance and calls for „mutual respect, equality, and mutual benefit“ among nations in AI development. As Racicot and Simpson (2024) note, the Initiative „should be seen in the context of China’s broader foreign policy objectives: its commitments to AI governance serve China’s geopolitical ambitions, including positioning itself as the standard-setter for developing nations.“	中国以其自身的全球人工智能治理愿景，回应了欧盟在监管领域的积极行动。由习近平主席于2023年10月提出的《全球人工智能治理倡议》，倡导在人工智能治理中采取“以人为本”的路径，并呼吁各国在人工智能发展过程中“相互尊重、平等互利”。正如 Racicot 与 Simpson（2024年）所指出的，该倡议“应置于中国更广泛外交政策目标的背景下加以理解：其对人工智能治理的承诺服务于中国的地缘政治抱负，包括将自身定位为发展中国家的标准制定者。”
For higher education, the competition between the EU’s Brussels Effect and China‘s Global AI Governance Initiative creates a complex regulatory landscape. International universities — particularly those with joint programmes spanning EU and Chinese institutions, such as those within the Jean Monnet Centre of Excellence network — must navigate both frameworks simultaneously. The companion chapter on data protection (Woesler, this volume) examines the practical challenges of dual compliance in detail.	(对于高等教育而言，欧盟的“布鲁塞尔效应”与中国的《全球人工智能治理倡议》之间的竞争，营造了一个复杂的监管格局。国际性大学——尤其是那些设有跨越欧盟与中国机构的联合培养项目的高校，例如让·莫内卓越中心网络内的项目——必须同时兼顾两套框架。本论文集中关于数据保护的专门章节（参见 Woesler，本卷）详细探讨了双重合规所带来的实际挑战。)
'8. Towards Responsible AI Integration: Recommendations'	(8. 迈向负责任的人工智能融合：建议)
Based on the comparative analysis presented in this article, we offer the following recommendations for universities navigating the emerging AI governance landscape.	(基于本文的比较分析，我们为正在应对新兴人工智能治理格局的高校提出以下建议。)
First, develop institution-specific AI policies that translate abstract regulatory requirements into concrete guidance for faculty and students. The HEAT-AI framework (Temper, Tjoa, and David 2025) provides a useful model for institutions seeking to operationalize risk-based AI governance at the institutional level.	(第一，制定校本人工智能政策，将抽象的监管要求转化为面向教师与学生的具体指导。HEAT-AI 框架（Temper、Tjoa 与 David，2025年）为寻求在机构层面落实基于风险的人工智能治理的高校，提供了一个有用的参考模型。)
Second, invest in AI literacy as a core institutional competency — not only for students but for faculty and administrative staff. The EU AI Act‘s literacy mandate (Article 4) provides a regulatory impetus, but the pedagogical imperative exists independent of regulation: faculty who do not understand the AI tools their students use cannot teach or assess effectively.	(第二，将人工智能素养作为机构核心能力进行投入——不仅面向学生，也面向教师与行政管理人员。欧盟《人工智能法案》的素养强制要求（第4条）提供了监管层面的推动力，但教学层面的必要性独立于监管而存在：不了解学生所用人工智能工具的教师，无法有效地进行教学或评估。)
Third, distinguish between AI governance for research and AI governance for assessment. The Chinese approach, which broadly permits AI use in research while restricting it in examinations and theses, provides a pragmatic distinction that European institutions would benefit from adopting. Research and assessment serve different purposes and face different risks; a single AI policy cannot adequately address both.	(第三，区分针对研究的治理与针对评估的人工智能治理。中国路径在广泛允许人工智能在研究中使用的同时，限制其在考试与论文写作中的应用，提供了一种务实的区分方式，欧洲各机构也将从中受益。研究与评估服务于不同的目的，面临不同的风险；单一的人工智能政策无法同时妥善应对这两者。)
Fourth, design assessment methods that evaluate distinctly human contributions — critical analysis, original thinking, creative synthesis, ethical reasoning — rather than relying on AI detection tools whose accuracy is contested and whose use may undermine trust between teachers and students.	(第四，设计能够评估人类独特贡献的考核方法——即批判性分析、原创性思维、创造性综合、伦理推理——而非依赖人工智能检测工具，后者的准确性存在争议，且其使用可能损害师生之间的信任关系。)
Fifth, engage in cross-border dialogue. The EU-China comparison reveals that each system has developed insights that the other lacks. European institutions can learn from China‘s speed of implementation and its integration of AI competencies across all disciplines. Chinese institutions can learn from the EU’s emphasis on ethical frameworks, faculty governance, and the protection of student rights. Joint programmes, exchange visits, and collaborative research — such as those facilitated by the Jean Monnet Centre of Excellence programme — provide concrete mechanisms for this dialogue.	(第五，开展跨境对话。欧盟与中国的比较揭示出，两套体系各自发展出了对方所不具备的洞见。欧洲机构可以从中国的实施速度及其将人工智能能力跨学科整合的做法中汲取经验。中国机构则可以从欧盟对伦理框架、教师治理以及学生权利保护的重视中学习。联合培养项目、互访交流与合作研究——例如由让·莫内卓越中心项目所促进的这类活动——为这一对话提供了具体的机制。)
'9. Conclusion'	(9. 结论)
The governance of AI in higher education is a challenge that no single regulatory framework has yet adequately addressed. The EU AI Act provides the most comprehensive legal framework, but its abstract provisions require institutional translation, and its implementation faces a significant readiness gap. China‘s sector-specific approach provides more targeted guidance for educational contexts, but its effectiveness depends on consistent institutional implementation and risks fragmentation across regulatory instruments.	(高等教育中的人工智能治理是一项尚未有任何单一监管框架能够充分应对的挑战。欧盟《人工智能法案》提供了最为全面的法律框架，但其条款具有抽象性，需要在机构层面加以转化落实，其实施还面临着显著的准备就绪差距。中国针对特定场景的路径为教育情境提供了更具针对性的指导，但其有效性取决于各机构能否一致地加以实施，且存在因监管工具多样而产生碎片化的风险。)
The most promising path forward combines elements of both approaches: the EU’s commitment to fundamental rights, risk-based classification, and ethical oversight; and China‘s willingness to mandate AI literacy at scale, its pragmatic distinction between research and assessment contexts, and its speed of institutional adaptation. Neither system can learn everything from the other — the cultural, political, and legal differences are too profound. But each can learn something, and the dialogue between them is itself a valuable contribution to the global challenge of governing AI in education responsibly.	(最具前景的前进路径，是将两种路径中的要素相结合：欧盟对基本权利的承诺、基于风险的分类以及伦理监督；以及中国大规模推行人工智能素养强制教育的意愿、其在研究与评估情境之间所作的务实区分，以及其机构层面的适应速度。两套体系都无法从对方那里学到一切——双方在文化、政治与法律层面的差异过于深刻。但每一方都能学到一些东西，而二者之间的对话本身，就是对负责任地治理教育领域人工智能这一全球挑战的宝贵贡献。)
As UNESCO’s Guidance for Generative AI in Education and Research (Miao and Holmes 2023) reminds us, „the absence of national GenAI regulations in most countries leaves user data privacy unprotected and educational institutions largely unprepared.“ The EU and China are among the few jurisdictions that have moved beyond this absence toward substantive governance frameworks. Their experiences — successes and failures alike — will shape how the rest of the world approaches the challenge of AI in higher education.	(正如联合国教科文组织《生成式人工智能教育与研究指南》（Miao 与 Holmes，2023年）所提醒我们的那样，“多数国家缺乏国家层面的生成式人工智能法规，这导致用户数据隐私不受保护，教育机构也普遍准备不足”。欧盟与中国是少数已经超越这种缺失状态、迈向实质性治理框架的司法管辖区。它们的经验——无论是成功还是失败——都将塑造世界其他地区应对高等教育中人工智能挑战的方式。)
'Acknowledgments'	(致谢)
This research was supported by the Jean Monnet Centre of Excellence „EU-Studies Centre: Digitalization in Europe and China„ (EUSC-DEC), funded by the European Union under Grant Agreement No. 101126782. Views and opinions expressed are those of the author only and do not necessarily reflect those of the European Union.	(本研究得到让·莫内卓越中心“欧盟研究中心：欧洲与中国的数字化”（EUSC-DEC）的支持，该中心由欧盟根据第101126782号资助协议提供资金。本文所表达的观点和意见仅代表作者本人，未必反映欧盟的立场。)
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