Rethinking Higher Education/Chapter 7/en-zh

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Beyond the Classroom: Alternative Learning Forms, Institutions, and Goals for the AI-Transformed Labour Market	超越课堂：人工智能转型劳动力市场下的替代性学习形态、教育建制与培养目标
Martin Woesler	吴漠汀
'Abstract'	摘要
The accelerating integration of artificial intelligence into economic production is rendering obsolete not merely specific job categories but the educational assumptions on which they rest. If machines can now perform most routine cognitive tasks — drafting legal documents, writing code, translating texts, analysing data — then the traditional educational model that trains students for precisely these tasks faces a legitimacy crisis. This article examines the emerging landscape of alternative learning forms, institutions, and educational goals that respond to this crisis, with systematic comparison between European and Chinese approaches. We analyse micro-credentials and nanodegrees, competency-based education, project-based and challenge-based learning, peer-to-peer learning, corporate universities, bootcamps, and open education platforms, assessing each for its potential contribution to workforce readiness in an AI-transformed economy. The European regulatory framework — including the ECTS micro-credential system, the Bologna Process adaptations, and the EU Digital Education Action Plan (2021–2027) — is compared with China‘s state-driven approach through „Education Modernization 2035“ and the „New Infrastructure“ strategy. We argue that neither the European preference for formal accreditation nor the Chinese emphasis on rapid scaling solves the fundamental problem: that education systems designed for stable knowledge domains cannot adequately prepare learners for a labour market characterised by radical uncertainty. We propose that the most promising developments are those that shift educational goals from knowledge transmission to adaptive capacity — the ability to learn, unlearn, and relearn in response to changing demands.	随着人工智能加速嵌入经济生产过程，其影响已不再局限于取代某些具体职业类别，而是进一步动摇了相关职业所依赖的教育假设。若机器已能够胜任起草法律文书、编写代码、翻译文本与分析数据等大多数常规认知任务，则传统以培养学生掌握此类任务为目标的教育模式，便面临深刻的合法性危机。本文聚焦于回应这一危机的新兴替代性学习形式、教育机构与教育目标，并对欧洲与中国的相关实践进行系统比较。文章考察了微证书与纳米学位、能力本位教育、项目式与挑战式学习、同伴互学、企业大学、训练营以及开放教育平台等模式，评估其在人工智能重塑的经济环境中对劳动力适应能力提升的潜在作用。与此同时，本文将欧洲的监管框架——包括 ECTS 微证书体系、博洛尼亚进程的调整以及欧盟《数字教育行动计划（2021—2027）》——与中国以国家主导为特征的发展路径进行比较，后者主要体现于《教育现代化2035》与“新基建”战略。本文认为，无论是欧洲对正式认证的偏重，还是中国对快速规模扩张的强调，均未能从根本上解决问题：即面向相对稳定知识领域所建构的教育体系，难以有效回应一个充满高度不确定性的劳动力市场。本文进而指出，最具前景的教育创新在于推动教育目标由知识传递转向适应能力培养，即使学习者具备在变化情境中不断学习、反思、遗忘并重构知识的能力。
Keywords: alternative education, AI labour market, micro-credentials, competency-based education, European-Chinese comparison, digital education, lifelong learning, workforce transformation	关键词： 替代性教育；人工智能；劳动力市场；微证书；能力本位教育；中欧比较；数字教育；终身学习；劳动力转型
'1. Introduction: The Obsolescence of the Curriculum'	'1. 引言：课程的过时性
In 2023, the World Economic Forum’s Future of Jobs Report estimated that 44% of workers’ core skills would need to change within the next five years — the largest anticipated skills disruption since the survey began (WEF, 2023). McKinsey Global Institute projected that by 2030, up to 375 million workers globally would need to switch occupational categories due to automation and AI (Manyika et al., 2017). In China, where the manufacturing sector employs over 100 million workers and the service sector is rapidly automating, the State Council’s 2017 „New Generation Artificial Intelligence Development Plan“ acknowledged that AI would create „severe challenges to employment structure“ (State Council, 2017).	2023年，世界经济论坛《未来就业报告》估计，未来五年内，44%的劳动者核心技能将发生变化，这是该项调查启动以来预期最大的技能冲击（WEF，2023）。麦肯锡全球研究院则预测，到2030年，全球可能有多达3.75亿劳动者因自动化和人工智能而需要转向其他职业类别（Manyika et al., 2017）。在中国，制造业雇佣劳动力超过1亿人，而服务业也正在快速实现自动化；国务院于2017年发布的《新一代人工智能发展规划》亦承认，人工智能将对“就业结构带来严峻挑战”（State Council, 2017）。
These projections have a common implication: the traditional educational model — four-year university degrees organised around disciplinary knowledge, delivered through lectures and seminars, assessed through examinations, and culminating in credentials that remain valid for an entire career — is increasingly misaligned with the demands of the contemporary labour market. A computer science graduate’s technical knowledge has a half-life of approximately five years; a graduate in digital marketing may find their skills obsolete within two. Even in traditionally stable professions — law, medicine, accounting — AI is automating tasks that were once the exclusive preserve of qualified professionals.	这些预测具有一个共同指向：传统教育模式——即以学科知识为核心、通过讲授与研讨实施、以考试作为评价方式，并最终授予可在整个职业生涯中持续有效的学位证书的四年制大学教育——正日益难以适应当代劳动力市场的需求。计算机专业毕业生所掌握的技术知识，其半衰期约为五年；而数字营销专业毕业生的技能甚至可能在两年内过时。即便在法律、医学、会计等传统上较为稳定的职业领域，人工智能也正在自动化那些曾长期由受过专业资质认证的从业者独占的任务。
This article examines the responses to this misalignment. We survey the alternative learning forms, institutions, and educational goals that are emerging in both Europe and China, and we assess their potential to prepare learners for a labour market characterised by radical uncertainty. The European Union and China represent two of the world’s largest and most ambitious educational reform efforts, yet they approach the challenge from fundamentally different institutional, cultural, and philosophical positions. Europe’s response is shaped by the Bologna Process, the ECTS framework, and a tradition of institutional autonomy and social partnership. China’s response is shaped by centralised educational planning, an examination-oriented culture undergoing deliberate reform, and a state that views education as a strategic instrument for national development.	本文考察了针对这一错位所作出的回应。我们梳理了欧洲与中国正在涌现的替代性学习形式、教育机构与教育目标，并评估其在培养学习者适应一个充满根本性不确定性的劳动力市场方面的潜力。欧盟与中国代表了全球规模最大、最具雄心的两类教育改革实践，但二者应对这一挑战所依托的制度、文化与理念基础却截然不同。欧洲的应对路径主要受博洛尼亚进程、欧洲学分转换与累积体系（ECTS）以及制度自治与社会伙伴关系传统的塑造；而中国的应对路径则建立在中央集权式教育规划、正在经历主动改革的应试文化，以及将教育视为国家发展战略工具的国家治理逻辑之上。
Our analysis is deliberately balanced: we do not advocate for the wholesale replacement of traditional education, which retains important functions that alternative models cannot replicate, but we argue that the traditional model must be supplemented — and in some cases fundamentally reconceived — if it is to remain relevant. The article draws on policy documents, institutional reports, and comparative educational research from both European and Chinese contexts, as well as on the author’s experience working in higher education in both Germany and China.	本研究秉持中立均衡的分析立场：我们并不主张全盘取代传统教育模式。传统教育仍具备各类新型教育范式无法复刻的重要功能；但本文认为，若想让传统教育模式持续保有现实价值与适配性，就必须对其进行补充完善，部分场景下更需予以根本性重构。本文研究素材源自中欧两地的政策文件、机构报告与比较教育研究文献，同时结合了笔者在德国与中国高等教育领域的从业研究经历。
'2. Alternative Learning Forms'	2. 替代性学习形式
'2.1 Micro-Credentials and Nanodegrees'	2.1 微证书与纳米学位
Micro-credentials — short, focused learning units that certify specific skills or competencies — represent perhaps the most significant structural innovation in recent educational history. The European Commission’s 2022 Recommendation on micro-credentials defined them as „records of the learning outcomes that a learner has acquired following a small volume of learning,“ typically expressed in ECTS credits (1–5 ECTS) and designed to be „stackable“ toward larger qualifications.	微证书——即用于认证特定技能或能力的、短小而聚焦的学习单元——或许代表了近代教育史上最重要的结构性创新之一。欧盟委员会于2022年发布的《微证书建议》将其定义为“学习者在完成小规模学习后所获得的学习成果记录”，通常以欧洲学分转换与累积体系（ECTS）学分来表示（1–5 ECTS），并被设计为可“叠加”起来，逐步获得更高级别的资格证书。
In Europe, the micro-credential ecosystem is developing within the Bologna Process framework. The European Approach to Micro-credentials, endorsed by the Council of the European Union in 2022, establishes common standards for quality assurance, recognition, and portability across member states. Platforms like MOOC providers (Coursera, edX, FutureLearn) and university consortia offer micro-credentials that carry ECTS credit, enabling learners to accumulate recognised qualifications through modular learning pathways.	在欧洲，微证书生态体系正在博洛尼亚进程框架内发展。欧盟理事会于2022年认可的《欧洲微证书方法》为成员国之间的质量保障、承认与可转移性建立了共同标准。Coursera、edX、FutureLearn 等 MOOC 平台以及大学联盟所提供的微证书，往往附带 ECTS 学分，使学习者能够通过模块化学习路径积累并获得受认可的资格证书。
China has developed a parallel but structurally different system. The 微证书 (wēi zhèngshū, micro-certificate) concept is less formalised than the European model but more widely practised. Chinese MOOC platforms — 学堂在线 (XuetangX, Tsinghua University), 中国大学MOOC (Chinese University MOOC, NetEase/Higher Education Press), and 智慧树 (Zhihuishu) — offer millions of courses, many carrying credits recognised within the Chinese higher education system. The Ministry of Education’s 2019 policy on „first-class online open courses“ (一流线上开放课程) formally integrated online course credits into university curricula.	中国则发展出一套与之平行但在结构上不同的体系。微证书（wēi zhèngshū）这一概念在中国的制度化程度不如欧洲模式那样完善，但实践范围却更为广泛。中国的 MOOC 平台——学堂在线（XuetangX，清华大学）、中国大学MOOC（网易/高等教育出版社）以及智慧树（Zhihuishu）——提供了数以百万计的课程，其中许多课程学分已被中国高等教育体系所认可。教育部2019年关于“一流线上开放课程”的政策，则正式将在线课程学分纳入大学课程体系之中。
The contrast is instructive: Europe emphasises standardisation, quality assurance, and cross-border recognition; China emphasises scale, speed, and integration with existing university structures. Both approaches have weaknesses. The European model risks over-regulation that stifles innovation. The Chinese model risks credential inflation — certificates that are technically „recognised“ but carry little market value because their quality varies enormously.	这种对比颇具启发性：欧洲强调标准化、质量保障以及跨境认可；中国则强调规模、速度，以及与既有大学体系的整合。两种路径各有其局限。欧洲模式可能因过度规制而抑制创新；中国模式则可能面临证书通胀——即这些证书在技术上“被承认”，但由于质量差异巨大，其市场价值却可能很低。
'2.2 Competency-Based Education'	2.2 基于能力的教育
Competency-based education (CBE) represents a more radical departure from the traditional model. Instead of measuring learning by time spent in instruction (credit hours, semesters), CBE measures learning by demonstrated mastery of defined competencies. Students advance when they can demonstrate what they know and can do, regardless of how long it took them to learn it.	基于能力本位的教育（competency-based education, CBE）代表了对传统教育模式更为激进的突破。它不再以学习者接受教学所花费的时间——如学分学时、学期等——来衡量学习，而是通过对既定能力的实际掌握程度来评估学习成果。学生只要能够证明自己“知道什么”以及“能够做什么”，即可继续推进学习，而不受其学习所耗费时间长短的限制。
In Europe, CBE has been adopted most enthusiastically in vocational education and training (VET) systems, particularly in Germany (dual education system), Switzerland, and the Netherlands. The European Qualifications Framework (EQF) is itself competency-based, defining eight levels of qualification in terms of knowledge, skills, and autonomy rather than years of study.	在欧洲，能力本位教育（CBE）最积极地被职业教育与培训（vocational education and training, VET）体系所采纳，尤其是在德国（双元制教育体系）、瑞士和荷兰。欧洲资格框架（European Qualifications Framework, EQF）本身就是以能力为基础的，它并不以学习年限来界定资格等级，而是从知识、技能以及自主性三个维度，划分出八个资格层级。
In China, competency-based approaches are emerging in vocational education reform. The 2019 „National Vocational Education Reform Implementation Plan“ (国家职业教育改革实施方案, known as „Zhiye 20“) introduced competency standards for over 500 occupational categories and established „1+X“ certificates — a diploma (1) supplemented by multiple vocational skill certificates (X) — as a bridge between academic and vocational qualifications.	在中国，基于能力的路径正在职业教育改革中逐步兴起。2019年发布的《国家职业教育改革实施方案》（简称“职教20条”）为500多个职业类别引入了能力标准，并建立了“1+X”证书制度——即以学历证书（1）为基础，叠加多个职业技能等级证书（X）——作为连接学术资格与职业资格之间的桥梁。
The advantage of CBE for the AI-transformed labour market is clear: if specific competencies become obsolete, learners can acquire new competencies without repeating an entire degree programme. The challenge is assessment: CBE requires robust, reliable methods for evaluating whether a learner has truly mastered a competency, not merely memorised its associated content. In an AI age, when machines can simulate many competencies convincingly, assessment design becomes a critical bottleneck.	基于能力的教育（CBE）对于人工智能重塑后的劳动力市场具有显而易见的优势：如果某些具体能力已经过时，学习者便可以直接获取新的能力，而无须重新修读整个学位课程。其挑战在于评估：CBE要求采用稳健、可靠的方法来判断学习者是否真正掌握了某项能力，而不仅仅是记住了与之相关的内容。在人工智能时代，机器能够相当逼真地模拟许多能力时，评估设计就成为一个关键瓶颈。
The European-Chinese comparison reveals an interesting convergence: both systems are moving toward competency-based approaches, but from opposite directions. Europe is adding competency frameworks to an already flexible, modular system. China is introducing flexibility and modularity into a traditionally rigid, examination-based system. The shared destination — an educational framework that certifies what learners can do rather than how long they studied — is significant, even if the journeys differ.	中欧教育体系对比呈现出一项颇具研究价值的趋同特征：两大教育体系均在向能力本位模式转型，但其变革路径恰好相反。欧洲是在本身已具备灵活性与模块化特征的现有教育体系之上，进一步纳入能力素养框架；中国则是向传统固化、以应试为核心的教育体系中，引入弹性机制与模块化架构。尽管二者改革演进路径截然不同，但其终极发展目标高度一致 —— 构建以学习者实际胜任能力而非学习时长为核心评价依据的教育认证体系，这一共性趋势具备重要的理论与现实意义。
'2.3 Project-Based and Challenge-Based Learning'	2.3 项目式学习与挑战式学习
Project-based learning (PBL) and its more ambitious variant, challenge-based learning (CBL), shift the educational focus from knowledge acquisition to problem-solving. Students work on real or realistic projects — developing a product, solving an engineering problem, creating a social enterprise — and learn disciplinary knowledge in the process of addressing the project’s demands.	项目式学习（PBL）及其立意更高的进阶形态 —— 挑战式学习（CBL），将教育重心从知识习得转向问题解决。学生立足于真实或贴近现实的项目开展实践，涵盖产品研发、工程难题攻关、社会企业创设等任务类型，并在响应项目需求、完成项目任务的过程中，习得专业学科知识。
Finland’s educational system, widely admired for its consistent high performance in international assessments, has moved aggressively toward phenomenon-based learning — a variant of PBL in which interdisciplinary projects replace subject-based instruction for portions of the curriculum. The European Commission’s Horizon Europe programme funds challenge-based education initiatives that connect university research with real-world problems.	芬兰教育体系因在各类国际学业测评中长期表现优异而广受推崇，目前正大力推行现象式学习。该模式属于项目式学习的衍生形态，以跨学科项目替代部分课程中的分科教学。欧盟委员会地平线欧洲计划专门资助各类挑战式教育创新项目，推动高校科研与现实社会问题深度对接。
In China, the „New Engineering“ (新工科) initiative, launched by the Ministry of Education in 2017, introduced project-based and interdisciplinary approaches into engineering education at hundreds of universities. Tsinghua University’s „challenge cup“ (挑战杯) competitions and Zhejiang University’s „entrepreneurial classroom“ programmes represent high-profile implementations of CBL philosophy.	CBE对于人工智能转型劳动力市场的优势是明确的：如果特定能力变得过时，学习者可以获取新能力而无需重修整个学位课程。挑战在于评估：CBE需要稳健、可靠的方法来评价学习者是否真正掌握了某项能力，而非仅仅记住了相关内容。在人工智能时代，当机器能够令人信服地模拟许多能力时，评估设计成为关键瓶颈。
PBL and CBL are particularly well-suited to the AI-transformed labour market because they develop precisely the skills that AI cannot easily replicate: creative problem-solving, collaborative negotiation, ethical judgment under uncertainty, and the ability to integrate knowledge across domains.	项目式学习（PBL）与挑战式学习（CBL）尤为适配人工智能重塑下的劳动力市场。两类学习模式所培育的能力，恰恰是人工智能难以复刻的核心素养：创造性问题解决能力、协作协商能力、不确定情境下的伦理判断能力，以及跨领域知识整合能力。
Interestingly, AI itself may facilitate PBL at scale. AI tutoring systems can provide the personalised guidance that project-based learning requires — answering technical questions, suggesting resources, providing feedback on drafts — while human instructors focus on the higher-order mentoring that AI cannot yet provide: asking challenging questions, providing emotional support, and modelling the habits of mind that characterise expert practitioners. This human-AI complementarity in pedagogical design mirrors the complementarity documented in our empirical study (Woesler, this volume) at the level of language learning.	值得关注的是，人工智能本身或将助力项目式学习实现规模化落地。人工智能导学系统能够提供项目式学习所需的个性化指导：解答专业技术问题、推荐学习资源、对初稿成果给出修改反馈；而人类教师则可聚焦于人工智能现阶段尚无法胜任的高阶育人引导，包括提出启发性思辨问题、给予情感支持，以及示范专业从业者所特有的专业思维范式。这种教学设计层面的人机协同互补模式，与本论文集沃斯勒（Woesler）在语言学习实证研究中所论证的互补特征形成了内在呼应。
The limitation is scalability: PBL requires intensive mentoring, flexible assessment, and institutional structures that most universities are not yet designed to provide. Chinese universities face an additional challenge: the examination-oriented culture creates strong pressure toward standardised, objectively assessable tasks, which PBL’s open-ended, process-oriented approach resists. European universities face a different challenge: faculty workload models and promotion criteria that reward research productivity over innovative teaching make it difficult to incentivise the intensive mentoring that PBL requires.	项目式学习的短板在于规模化落地受限：该教学模式需要精细化导学指导、弹性化考核方式以及配套制度架构，而大多数高校现行办学体制尚未形成相应支撑条件。中国高校还面临额外挑战：根深蒂固的应试本位文化倒逼教学倾向于设置标准化、可客观量化考评的学习任务，而项目式学习秉持开放式、过程导向的理念，与这种倾向存在内在冲突。欧洲高校则面临另一重困境：现行教师工作量核算机制与职称晋升评价标准重科研产出、轻教学创新，难以有效激励教师投入项目式学习所必需的高强度导学工作。
'2.4 Peer-to-Peer and Community Learning'	2.4 同伴学习与社群式学习
The internet has enabled learning forms that bypass institutional structures entirely. Peer-to-peer learning — in which individuals with complementary knowledge teach each other — flourishes on platforms like GitHub (for programming), Stack Overflow (for technical problem-solving), and Zhihu (知乎, China‘s equivalent of Quora) for general knowledge exchange. Community learning takes this further: groups of learners form self-organised communities around shared learning goals, often using open educational resources and free tools.	互联网催生了完全脱离正规教育体制架构的新型学习形态。同伴学习是指知识储备与能力结构互补的学习者之间互为师友、互教互学，该模式已在各类网络平台蓬勃发展：GitHub 平台服务于编程学习、Stack Overflow 聚焦技术问题求解，知乎（中国版 Quora）则主要用于通识知识交流。社群式学习是同伴学习的进阶形态：学习者围绕共同的学习目标自发组建学习共同体，通常依托开放教育资源与免费数字化工具开展学习活动。
The „community of practice“ model described by Etienne Wenger — in which learning occurs through participation in a community defined by shared domain, practice, and identity — has been amplified by digital connectivity. In China, WeChat learning groups (微信学习群) and Douyin (抖音, TikTok) educational channels have become significant informal learning environments, reaching audiences that formal education cannot.	'艾蒂安・温格所提出的实践共同体理论模式，强调学习依托成员参与共同体活动而发生，而该共同体以共同知识领域、实践范式与身份认同为核心特质；数字互联技术进一步拓展并强化了这一学习模式的影响力。在国内，微信学习群与抖音教育类账号已发展为重要的非正式学习场域，覆盖了正规教育体系难以触达的广大学习群体。'
The advantage of peer-to-peer learning is its adaptability: communities can form around emerging skills — prompt engineering, for example, or AI-assisted design — long before formal educational institutions develop curricula for them. The disadvantage is quality assurance: without institutional oversight, peer-to-peer learning is susceptible to misinformation, echo chambers, and the Dunning-Kruger effect.	同伴学习的核心优势在于灵活适配性：针对提示词工程、人工智能辅助设计等新兴技能，学习社群可先行自发组建，远早于正规教育机构完成相关课程体系的搭建。其弊端则体现在学习质量难以保障：由于缺乏制度化监管，同伴学习极易滋生虚假信息与信息茧房现象，同时还易受邓宁 - 克鲁格效应的影响。
'3. Alternative Institutions'	3 替代性教育机构
'3.1 MOOCs and Online Platforms'	3.1 慕课与在线学习平台
Massive Open Online Courses were heralded as a revolution in 2012 when Coursera, edX, and Udacity launched with content from elite universities. The revolution was overpromised: completion rates averaged below 10%, and MOOCs reached predominantly already-educated learners in developed countries. But a decade later, the MOOC ecosystem has matured into a significant component of the global education infrastructure.	2012 年，Coursera、edX、优达学城（Udacity）三大平台依托顶尖高校资源正式上线，大规模开放在线课程（慕课） 一度被誉为一场教育变革。但这场变革最初被过度理想化：慕课课程平均完成率不足 10%，且受众大多为发达国家已具备良好教育基础的学习者。历经十年发展，慕课生态体系已日趋成熟，成为全球教育基础设施的重要组成部分。
In Europe, the MOOC landscape includes Coursera and edX partnerships with European universities, the EU-funded European MOOC Consortium, and national platforms like France Université Numérique (FUN) and Germany’s openHPI (Hasso Plattner Institute). The EU’s commitment to digital skills — articulated in the Digital Education Action Plan (2021–2027) — includes support for online learning platforms as tools for lifelong learning and reskilling.	欧洲慕课发展格局涵盖：Coursera 与 edX 两大平台同欧洲高校的合作项目、欧盟资助的欧洲慕课联盟，以及法国数字大学平台（FUN）、德国哈索・普拉特纳研究院旗下 openHPI 等国家级在线教育平台。欧盟在《数字教育行动计划（2021–2027）》中明确提出发展数字素养的战略部署，将扶持在线学习平台作为推进终身学习与职业技能重塑的重要载体。
China‘s MOOC ecosystem is larger by enrollment numbers. XuetangX, developed by Tsinghua University, serves over 100 million registered users. Chinese University MOOC, co-developed by NetEase and the Higher Education Press, offers courses from over 900 Chinese universities. The Ministry of Education’s recognition of online course credits has given Chinese MOOCs an institutional legitimacy that their Western counterparts often lack.	从注册用户规模来看，中国慕课生态体系体量更为庞大。由清华大学研发的学堂在线，注册用户已突破 1 亿人次；由网易与高等教育出版社联合共建的中国大学 MOOC，汇聚了国内 900 余所高校的课程资源。我国教育部对在线课程学分予以官方认可，这赋予了中国慕课制度层面的合规性与权威性，而这恰恰是西方同类慕课平台普遍欠缺的制度优势。
The challenge for MOOCs in the AI age is paradoxical: the same AI technologies that create the demand for reskilling also threaten to automate the assessment, feedback, and personalisation functions that make MOOCs effective. AI-powered tutoring systems may ultimately replace the MOOC format itself, offering fully personalised learning pathways rather than standardised course sequences.	人工智能时代的慕课面临悖论式困境：人工智能技术一方面催生了社会技能重塑的巨大需求，另一方面又可将慕课赖以发挥效用的学业考评、学习反馈与个性化适配等功能实现自动化，进而对慕课形成冲击。人工智能导学系统或将最终取代慕课现有形态，不再提供标准化课程流程，而是为学习者量身打造完全个性化的学习路径。
'3.2 Corporate Universities and Employer-Led Training'	3.2 企业大学与雇主主导型培训
Major technology companies have developed educational programmes that rival university offerings in depth and market value. Google’s Career Certificates, Amazon’s AWS Training, and Microsoft’s Learn platform offer industry-recognised credentials that can be completed in weeks rather than years. In China, Huawei’s ICT Academy, Alibaba’s DAMO Academy training programmes, and ByteDance’s internal university serve similar functions.	大型科技企业已构建起成熟的教育培养项目，其知识体系深度与市场价值足以与高校课程体系相抗衡。谷歌职业证书项目、亚马逊 AWS 培训体系及微软学习平台均颁发行业认可职业资质，修读周期仅为数周，无需像传统高等教育那样耗时数年。在中国，华为 ICT 学院、阿里巴巴达摩院培训项目以及字节跳动企业内部大学，均承担着与之相仿的人才培养职能。
The rise of corporate education raises fundamental questions about the purpose of universities. If employers can train workers more efficiently and relevantly than universities, what remains for higher education? The answer, we suggest, lies in what corporate training cannot and does not intend to provide: breadth of knowledge, critical thinking, ethical reasoning, cultural understanding, and the ability to question — not merely execute — institutional purposes. A Google certificate teaches cloud computing; it does not teach a student to ask whether cloud computing serves human flourishing. A Huawei ICT certification teaches network engineering; it does not teach a student to consider the surveillance implications of the infrastructure they build.	企业办学的兴起，对大学的存在价值与办学宗旨提出了根本性拷问。倘若企业雇主能够比高等院校更高效、更贴合行业实际需求地开展人才培养，那么高等教育的独特定位与核心使命又将何以立足？ 本文认为，答案在于企业培训既无力供给、也无意承载的素养维度：广博的知识视野、批判性思维、伦理推理能力、文化理解力，以及敢于对制度目标进行反思诘问、而非仅被动执行任务的思辨能力。谷歌职业证书仅教授云计算专业技能，却无法引导学习者思考云计算技术是否真正服务于人类福祉；华为 ICT 认证只传授网络工程技术，却无法培养学习者审视其搭建的信息基础设施所暗藏的监控伦理隐患。
This distinction is not a snobbish defence of academic privilege. It reflects a genuine difference in institutional purpose: corporations train workers to serve the corporation’s interests; universities (at their best) educate citizens to serve society’s interests. Both functions are necessary, but they are not identical, and conflating them — as some advocates of „disruptive“ education propose — risks producing a workforce that is technically competent but ethically uncritical. The philosopher Hans Jonas’ imperative of responsibility — that we must ensure our actions do not destroy the conditions for future human existence — applies to educational design as much as to environmental policy.	这种区分并非以精英姿态狭隘维护学术特权，而是反映了两类机构办学使命的本质差异：企业培养服务自身利益的从业者；而大学在理想状态下，培育服务社会公共利益的公民。两种培养功能均不可或缺，但本质并不等同。部分倡导 “颠覆性教育” 的观点试图将二者混为一谈，这极易造就一批技术素养过硬、伦理思辨缺失的劳动力群体。哲学家汉斯・约纳斯提出的责任律令—— 人类行事须确保自身行为不会破坏人类未来的生存根基 —— 不仅适用于环境政策制定，同样也深刻适用于教育体系的顶层设计。
'3.3 Bootcamps and Intensive Vocational Training'	3.3 技能训练营与密集型职业培训
Coding bootcamps — intensive, short-term (typically 12–24 weeks) training programmes — emerged in the 2010s as an alternative pathway into the technology sector. The model has expanded beyond coding to include data science, UX design, digital marketing, and increasingly, AI and machine learning. In Europe, bootcamps like Le Wagon (founded in Paris, now operating in 40+ cities), Ironhack, and Northcoders operate across multiple countries. In China, platforms like 拉勾教育, 开课吧 (Kaikeba), and numerous smaller operations offer intensive technical training to hundreds of thousands of learners annually.	编程技能训练营是一类高强度短期培训项目，学制通常为 12–24 周，于 21 世纪 10 年代兴起，成为进入科技行业的新型入行路径。该培养模式现已从编程领域向外拓展，覆盖数据科学、用户体验设计、数字营销等方向，并逐步延伸至人工智能与机器学习领域。在欧洲，勒瓦贡（Le Wagon，创立于巴黎，现已布局全球 40 余座城市）、艾隆哈克（Ironhack）、诺斯考德斯（Northcoders）等技能训练营均实现了跨国运营。在中国，拉勾教育、开课吧以及大量中小型培训机构，每年为数十万学习者提供系统化的高强度技术实训。
Bootcamps’ strength is job relevance: curricula are designed in consultation with employers and updated continuously. Their weakness is narrowness: graduates acquire specific technical skills but may lack the broader understanding needed to adapt when those skills become obsolete — which, in the AI field, can happen within months. The bootcamp model also raises equity concerns: tuition fees of €5,000–15,000 in Europe and ¥10,000–50,000 in China restrict access to those who can afford to invest in uncertain outcomes.	技能训练营的核心优势在于高度贴合职场实际需求：其课程体系均联动用人单位共同设计，并进行持续动态更新。但其短板在于培养维度过于狭隘：受训学员仅习得专项技术技能，却缺乏必备的综合知识视野；一旦相关技能被行业淘汰，便难以完成职业适配转型 —— 而在人工智能领域，技能迭代淘汰周期往往仅需数月。同时，技能训练营模式还存在教育公平隐忧：欧洲此类培训学费区间为 5000–15000 欧元，中国收费则达 10000–50000 元人民币。高昂的参训门槛，仅面向具备经济支付能力、且愿意为不确定的就业回报投入成本的群体，限制了普通学习者的参与机会。
'3.4 Open Universities and Lifelong Learning Centres'	3.4 开放大学与终身学习中心
Europe’s open university tradition — exemplified by the UK’s Open University (founded 1969), Germany’s FernUniversität in Hagen, and Spain’s UNED — provides a model for lifelong learning that pre-dates the digital revolution. These institutions were designed to serve working adults, part-time learners, and those excluded from traditional higher education, and their experience with flexible learning formats, distance education, and non-traditional assessment is directly relevant to the AI-era challenge.	欧洲拥有悠久的开放大学办学传统，以英国开放大学（1969 年建校）、德国哈根函授大学、西班牙国立远程教育大学（UNED）为典型代表，其构建的终身学习范式早在数字革命之前便已形成。这类教育机构创办初衷是服务在职成人、非全日制学习者，以及无缘接受传统高等教育的群体。它们在弹性学习形式、远程教育模式与非传统学业评价方面积累了丰富实践经验，对破解人工智能时代的教育困境具有直接的参考价值。
China‘s equivalent — the Open University of China (国家开放大学, formerly China Central Radio and TV University) with its network of over 3,000 learning centres — serves approximately 4 million enrolled students, making it one of the largest educational institutions in the world. The system has been criticised for low graduation rates and variable quality, but its infrastructure for reaching geographically dispersed learners provides a foundation for the kind of mass reskilling that the AI transformation demands.	中国同类代表性机构为国家开放大学，其前身为中央广播电视大学，拥有覆盖全国的 3000 余个学习中心办学网络，在籍学生规模约 400 万人，是全球体量最大的教育机构之一。该办学体系虽因毕业率偏低、办学质量参差不齐而备受诟病，但其面向地域分散学习者构建的服务基础设施，能够为人工智能转型时代所需的大规模全民技能重塑筑牢重要支撑基础。
The open university model’s key insight — that education must meet learners where they are, not where the institution wants them to be — is increasingly relevant as AI-driven labour market disruption affects workers at all career stages, not just recent graduates.	开放大学模式蕴含一条核心理念：教育应当适配学习者的现实基础与实际处境，而非要求学习者迁就院校的固有办学范式。随着人工智能引发劳动力市场结构性变革，受冲击的群体不再局限于应届毕业生，而是覆盖职业生涯各个阶段的从业者，这一理念也愈发具有现实指导意义。
'4. Alternative Goals: From Knowledge to Capacity'	4 替代性教育目标：从知识本位走向能力素养
'4.1 The Shift from Knowledge Transmission to Adaptive Capacity'	4.1 从知识传授到适应能力的转向
The most fundamental change that the AI-transformed labour market demands is not in learning forms or institutions but in educational goals. Traditional education aims to transmit knowledge: a body of established facts, theories, methods, and skills that the student is expected to master. This model presupposes that the knowledge being transmitted will remain relevant for a significant portion of the student’s career. AI disrupts this presupposition.	人工智能重塑的劳动力市场所要求的最根本变革，并不在于学习形式或办学机构，而在于教育目标的革新。传统教育以知识传授为核心：向学习者传授一套既定的事实、理论、方法与技能体系，并要求学生熟练掌握。这种模式隐含一个前提假设：所传授的知识在学习者职业生涯的相当长时期内，仍能保持实用价值。而人工智能的出现，彻底颠覆了这一预设前提。
If AI can access, organise, and apply factual knowledge faster and more accurately than any human, then the educational premium shifts from knowing to meta-knowing: the ability to learn new domains rapidly, to evaluate information critically, to synthesise perspectives from different fields, to ask the right questions, and to exercise judgment in situations where data is ambiguous or incomplete.	倘若人工智能在检索、梳理与运用事实性知识上，比人类更迅速、更精准，那么教育的核心价值便从掌握既有知识转向元认知能力：也就是快速研习全新知识领域、批判性甄别信息、融会贯通跨学科视角、提出关键性问题，以及在数据模糊或残缺不全的情境下做出独立研判的能力。
Alvin Toffler’s often-quoted prediction — „The illiterate of the 21st century will not be those who cannot read and write, but those who cannot learn, unlearn, and relearn“ — has become operational reality. The educational challenge is to develop curricula that cultivate this adaptive capacity without abandoning the foundational knowledge that makes adaptation possible.	未来学家阿尔文・托夫勒有一句广为引用的预言：“21 世纪的文盲，不再是那些不会读写的人，而是不会学习、不会扬弃旧知、不会重新学习的人。” 如今这一论断已然成为现实常态。 当下教育面临的核心难题是：构建能够培育这种适应能力的课程体系，同时又不舍弃支撑个体实现适应发展所必需的基础知识。
'4.2 AI Literacy as Core Competency'	4.2 人工智能素养作为核心素养
AI literacy — the ability to understand, evaluate, and appropriately use AI tools — is rapidly becoming as fundamental as numerical and textual literacy. We distinguish three dimensions of AI literacy:	人工智能素养，即理解、评估并合理运用人工智能工具的综合能力，正迅速成为与数理素养、文本读写素养同等重要的基础素养。本文将人工智能素养划分为三个维度：
Technical literacy: the ability to use AI tools effectively — formulating prompts, evaluating outputs, understanding the basic principles of machine learning. This is the most immediately practical dimension and the one most commonly addressed in current educational offerings.	技术素养：指高效运用人工智能工具的能力，包括精准设计提示词、甄别评估生成结果、理解机器学习的基本原理。这一维度最具即时实用价值，也是当前各类教育教学内容中覆盖最为普遍的部分。
Conceptual literacy: understanding what AI can and cannot do, recognising AI-generated content, evaluating AI outputs critically, and understanding the difference between pattern recognition and genuine understanding. This dimension is essential for preventing the uncritical over-reliance on AI that Fang Lu (this volume) documents in her case studies.	概念素养：明晰人工智能的能力边界与局限，能够识别 AI 生成内容、批判性研判人工智能输出结果，并分清模式识别与真正意义上认知理解的本质区别。陆芳在本卷的案例研究中，论证了大众对人工智能不加思辨的过度依赖问题，而这一素养维度正是规避该现象的关键。
Ethical literacy: understanding the societal implications of AI deployment, including algorithmic bias, privacy erosion, environmental costs of large model training, labour displacement, and the concentration of power in the hands of a few technology companies. This dimension connects AI literacy to broader questions of citizenship and social responsibility.	伦理素养：理解人工智能应用落地所带来的各类社会影响，涵盖算法偏见、隐私侵蚀、大模型训练的环境成本、劳动力替代，以及少数科技企业形成的权力垄断等问题。这一维度将人工智能素养与公民素养、社会责任等更深层次的议题紧密联结。
The EU’s AI Act (2024) — the world’s first comprehensive AI regulation — implicitly creates a demand for AI literacy across all professional domains: compliance requires understanding what AI systems do, how they make decisions, and what risks they pose. The Act’s risk-based classification system (unacceptable risk, high risk, limited risk, minimal risk) provides a conceptual framework that could inform AI literacy curricula throughout Europe.	欧盟 2024 年《人工智能法案》作为全球首部综合性人工智能监管法规，无形中催生了各行各业对人工智能素养的普遍需求：合规履职要求人们理解人工智能系统的运行逻辑、决策方式及其潜在风险。该法案采用风险分级分类体系，将人工智能划分为不可接受风险、高风险、有限风险与最小风险四个等级，可为欧洲各地人工智能素养课程的设计与开设提供成熟的理论参照框架。
China‘s approach has been more enabling than restrictive: the 2023 „Interim Administrative Measures for Generative AI Services“ (生成式人工智能服务管理暂行办法) focus on registration and content moderation rather than comprehensive regulation, but the underlying demand for AI literacy is equally urgent. China’s Ministry of Education has begun integrating AI content into primary and secondary curricula, and several universities have established dedicated AI education departments — a development that Europe, despite its regulatory leadership, has been slower to achieve at scale.	中国对人工智能的治理思路更侧重赋能引导而非严格管控。2023 年出台的《生成式人工智能服务管理暂行办法》，重点聚焦备案登记与内容合规审核，并未采取全域式监管规制，但社会层面对人工智能素养的培育需求同样十分迫切。我国教育部已着手将人工智能相关内容融入中小学课程体系，多所高校也相继设立专门的人工智能教育院系。欧洲虽在人工智能规则制定上占据先导地位，但在规模化落地普及人工智能教育这一进程上，推进速度反而相对迟缓。
'4.3 Creativity, Critical Thinking, and Emotional Intelligence'	4.3 创造力、批判性思维与情绪智力
If AI handles routine cognitive tasks, then uniquely human capacities — creativity, critical thinking, emotional intelligence, ethical reasoning, and cross-cultural communication — become the core of economic value. The World Economic Forum’s Future of Jobs Report consistently identifies analytical thinking, creative thinking, resilience, flexibility, and curiosity as the top skills valued by employers — none of which are domain-specific knowledge. The challenge is that these capacities are precisely what traditional education, with its emphasis on standardised assessment and disciplinary knowledge, is least well-designed to cultivate.	当人工智能能够承接各类常规认知工作时，人类独有的综合能力—— 创造力、批判性思维、情绪智力、伦理推理与跨文化沟通能力，将成为经济价值创造的核心支撑。世界经济论坛发布的《未来就业报告》持续指出：分析思维、创新思维、心理韧性、应变灵活性与求知好奇心，是雇主最为看重的顶尖能力；而这些能力均不属于局限于单一领域的专业知识。当下的教育困境在于：传统教育以标准化考核和分科知识传授为核心导向，恰恰在培育上述综合素养方面存在天然短板。
European educational philosophy, particularly the Nordic model, has long emphasised creativity and critical thinking alongside academic achievement. Finland’s national curriculum reform (2014) made „transversal competences“ — including thinking and learning to learn, cultural competence, multiliteracy, and participation — a mandatory component of all instruction from primary school onward. The EU’s Key Competences for Lifelong Learning framework identifies eight transversal competences — including „learning to learn,“ „social and civic competences,“ and „cultural awareness and expression“ — that go beyond subject knowledge.	欧洲教育理念，尤其是北欧教育模式，历来在重视学业成绩的同时，兼顾创造力与批判性思维的培育。芬兰 2014 年国家课程改革，将横向核心素养列为小学起所有教学活动的必修内容，涵盖思辨与学会学习、文化素养、多元读写能力及社会参与等维度。欧盟《终身学习核心素养框架》明确了八项横向通用素养，包括学会学习、社会与公民素养、文化认知与表达能力等，这类素养均突破了单一学科知识的范畴。
China‘s educational reform efforts, particularly the 素质教育 (sùzhì jiàoyù, quality education) movement that has been official policy since the 1990s, aim to counterbalance the examination-oriented culture (应试教育, yìngshì jiàoyù) with broader competency development. The „double reduction“ (双减) policy of 2021, which restricted after-school tutoring and homework loads, was explicitly motivated by the desire to create space for creativity, physical activity, and non-academic development. However, implementation remains uneven, and the gaokao (高考, national college entrance examination) continues to exert powerful centripetal force toward examination preparation at the expense of creative and critical thinking development.	中国的教育改革探索，尤其是自 20 世纪 90 年代起正式纳入国策的素质教育改革，意在以综合素养培育平衡应试教育的弊端。2021 年实施的双减政策，通过规范校外培训、压降作业负担，其核心初衷正是为学生腾出空间，助力创造力发展、体育锻炼与非学业素养的全面成长。但政策落地成效仍存在明显不均衡，高考依旧形成强大的导向惯性，使基础教育始终围绕应试备考展开，进而挤占了创造力与批判性思维素养的培育空间。
'4.4 Cross-Cultural Competence as Competitive Advantage'	4.4 跨文化素养作为竞争优势
In a globalised economy where AI handles routine tasks across language and cultural boundaries, the ability to navigate cultural differences — to understand different value systems, communication styles, negotiation practices, and institutional norms — becomes a distinctive human competence with significant economic value. This is particularly relevant for the European-Chinese educational dialogue that this volume represents: professionals who can work effectively across the European-Chinese divide possess a rare and valuable capability that AI cannot replicate.	在全球化经济格局下，人工智能已然能够跨越语言与文化边界处理各类常规事务。在此背景下，驾驭文化差异的能力 —— 理解多元价值体系、沟通风格、谈判惯例与制度规范 —— 成为人类独有且具备极高经济价值的核心素养。这一点对本卷所承载的中欧教育对话尤为关键：能够在中欧两大语境间高效开展工作的专业人才，拥有人工智能无法复刻的稀缺核心能力。
Cross-cultural competence is not merely language proficiency (which AI can increasingly simulate) but cultural intelligence: the ability to interpret ambiguous social signals, to adapt behaviour to unfamiliar cultural contexts, and to build trust across cultural boundaries. Educational programmes that develop this competence — through exchange programmes, immersive experiences, intercultural teamwork, and sustained engagement with another culture’s intellectual traditions — represent a form of alternative education that is immune to AI displacement precisely because it depends on embodied human experience.	跨文化素养绝非单纯的语言熟练度（人工智能对此类能力的模拟已愈发成熟），而是文化智能：能够解读模糊的社会人际信号、在陌生文化情境中调整行为方式，并跨越文化隔阂建立信任联结。依托国际交流项目、沉浸式文化体验、跨文化团队协作，以及长期研习异质文化思想传统等路径培育该素养的教育模式，构成了一种差异化育人范式。这类教育完全依托人类具身实践体验形成，正因如此，具备不受人工智能替代冲击的独特价值。
'5. European Regulatory Frameworks vs. Chinese State-Driven Approaches'	5 欧洲监管框架与中国政府主导模式对比
The contrast between European and Chinese educational governance illuminates the strengths and limitations of each system’s response to AI-driven disruption.	中欧教育治理模式的差异，清晰揭示了两大体系在应对人工智能颠覆性变革时各自的优势与局限。
'5.1 The European Model: Regulation and Standardisation'	5.1 欧洲模式：监管与标准化
The European Union‘s approach to educational reform operates through a complex multi-level governance structure. The EU has limited direct competence over education policy (education remains primarily a member state responsibility under the subsidiarity principle), but it exerts significant influence through frameworks, recommendations, and funding.	欧盟推进教育改革，依托一套复杂的多层级治理架构运作。依据辅助性原则，欧盟对教育政策并无过多直接管辖权限，教育事务仍主要由各成员国自行负责；但欧盟可通过制定框架标准、发布政策建议以及提供资金支持，形成显著的引导影响力。
The Digital Education Action Plan (2021–2027) establishes two strategic priorities: fostering the development of a high-performing digital education ecosystem, and enhancing digital skills and competences for the digital transformation. It funds initiatives in digital infrastructure, teacher training, and online learning, but implementation depends on member state adoption.	欧盟《数字教育行动计划（2021–2027 年）》确立了两大战略重点：一是构建高效能数字教育生态体系，二是提升适配数字化转型的数字技能与综合素养。该计划为数字基础设施建设、教师培训、在线学习等专项行动提供资金支持，但其落地推行仍有赖于各成员国的采纳落实。
The European Skills Agenda (2020) sets targets including 60% of adults participating in learning annually by 2030, and the creation of individual learning accounts to finance training. The Recommendation on Micro-Credentials (2022) provides a common definition and quality standards. The European Qualifications Framework (EQF) enables cross-border recognition of qualifications.	欧盟《技能议程（2020 年）》制定了多项发展目标，包括到 2030 年实现60% 成年人每年参与继续教育学习，并设立个人学习账户，为职业培训提供资金保障。《微证书政策建议（2022 年）》确立了微证书的统一定义与质量标准。欧洲资格框架（EQF）则实现了各类学历资质的跨国互认。
The strengths of this approach are quality assurance, portability, and trust: a European micro-credential issued under common standards carries recognised value across 27 member states. The weakness is speed: the time required to negotiate, adopt, and implement EU-wide frameworks means that educational policy consistently lags behind technological change. By the time a European micro-credential framework for „AI prompt engineering“ is formally established, the skill itself may have been automated.	这套治理模式的优势体现在质量保障、资质通用与公信力背书：依照统一标准颁发的欧洲微证书，在欧盟 27 个成员国境内均具备公认效力与通行价值。其短板则在于推进节奏迟缓：欧盟层面的政策框架需历经磋商、表决采纳再到落地实施，耗时冗长，导致教育政策始终滞后于技术迭代的速度。待到欧盟正式出台 “人工智能提示工程” 微证书规范体系时，这项技能本身或许早已被技术自动化所代。
'5.2 The Chinese Model: State Direction and Rapid Scaling'	5.2 中国模式：国家引领与快速规模化发展
China‘s educational governance is centralised. The Ministry of Education (教育部) sets national policy, and provincial education bureaus implement it. This structure enables rapid, large-scale action: when the State Council issues a national strategy — such as Education Modernization 2035 or the Double First-Class University initiative — resources can be mobilised and curricula reformed across thousands of institutions within months, not years.	中国教育治理实行集中统筹体制。教育部制定全国教育大政方针，各省级教育主管部门负责贯彻落实。这一治理架构具备快速动员、大规模落地的突出优势：国务院发布国家级战略规划后，例如《教育现代化 2035》与双一流高校建设计划，只需数月而非数年时间，就能在全国数千所教育机构中完成资源调配与课程体系改革。
The „New Infrastructure“ strategy (新基建), announced in 2020, designated AI as one of seven strategic infrastructure categories alongside 5G, data centres, and industrial internet. Education was explicitly identified as a beneficiary: AI-powered adaptive learning platforms, intelligent tutoring systems, and automated assessment tools were to be developed and deployed at scale.	2020 年出台的新基建战略，将人工智能与 5G、数据中心、工业互联网等并列，划定为七大战略性基础设施建设领域之一。教育领域被明确纳入受益范畴：基于人工智能的自适应学习平台、智能辅导系统以及智能测评工具，得以实现规模化研发与落地推广。
The strength of this approach is execution speed and scale: China can build educational infrastructure — digital platforms, training programmes, institutional reforms — faster than any other country. The weakness is flexibility: centralised planning tends toward uniformity, and the emphasis on national strategies and quantitative targets (number of courses, number of credentials, number of participants) can obscure qualitative concerns about learning depth, critical thinking, and intellectual autonomy.	该模式的优势在于执行效率与规模化落地能力：中国搭建教育基础设施 —— 包括数字平台、培训项目与制度改革 —— 的速度位居全球前列。其短板则体现在灵活适配性不足：集中统筹规划易走向同质化发展；同时过度侧重国家战略部署与量化指标（课程数量、证书数量、参与人数等），往往会忽视学习深度、批判性思维及学术自主能力等质量层面的核心诉求。
'5.3 Toward Mutual Learning'	5.3 走向互学互鉴
The European and Chinese models are not merely different but complementary. Europe needs more of China‘s willingness to scale quickly and to integrate alternative credentials into formal education systems without decades of deliberation. China needs more of Europe’s emphasis on quality assurance, learner autonomy, and the philosophical foundations of education — what Döring (this volume) calls the distinction between Verstand and Vernunft.	欧洲与中国的发展模式不只是存在差异，更具备互补性。欧洲需要借鉴中国快速规模化推进的魄力，以及不必经过数十年研讨斟酌，就能将新型非传统证书纳入正规教育体系的实践思路。中国则需要吸收欧洲对质量保障、学习者自主以及教育哲学根基的重视 —— 正如多林（本卷收录文章）所提出的知性（Verstand）与理性（Vernunft）之分。
A productive dialogue between the two systems — the kind of dialogue that this volume and the Jean Monnet Centre of Excellence that supports it are designed to foster — would focus not on which model is „better“ but on what each can learn from the other. The AI transformation of the labour market is a shared challenge; the educational responses should be shared as well.	两大体系间富有建设性的对话 —— 也正是本论文集及其依托的让・莫内卓越中心着力推动的交流范式 —— 不应纠结评判哪种模式更为 “优越”，而应聚焦彼此能够取长补短、互学互鉴之处。人工智能重塑劳动力市场是双方共同面临的时代挑战，针对这一变革的教育应对路径，同样值得双方交流共享、协同探索。
'6. Risks and Critiques'	6 风险与批判性反思
'6.1 The Credentialism Trap'	6.1 文凭主义陷阱
As alternative credentials proliferate, the risk of credential inflation grows. If micro-credentials, nanodegrees, bootcamp certificates, and corporate certifications multiply without robust quality assurance, the labour market may face a situation in which credentials signal nothing reliable about competence. Employers, unable to evaluate the quality of hundreds of competing credentials, may retreat to the simplest available heuristic: the traditional university degree — precisely the credential that alternative learning seeks to supplement or replace.	随着各类替代性证书大量涌现，文凭通胀的风险持续攀升。若微证书、纳米学位、技能训练营结业证及企业认证肆意泛滥，又缺乏完善的质量管控体系，劳动力市场或将陷入困局：各类证书再也无法真实可靠地佐证从业者的实际能力。面对名目繁多、鱼龙混杂的各类证书，用人单位难以甄别其含金量，便会退守至最简便的评判惯性：依旧以传统大学学位作为选人标准 —— 而这恰恰是替代性学习本想补充、乃至取代的学历凭证。
This paradox is already visible in China, where the proliferation of online certificates (网络证书) has led many employers to discount them entirely, while simultaneously the traditional university degree retains (and even increases) its gatekeeping function for desirable employment. In Europe, the risk is institutional: as universities themselves begin offering micro-credentials, the boundary between „traditional“ and „alternative“ credentials blurs, potentially undermining the signalling value of both.	'这种悖论在中国已然显现：网络证书的泛滥，使得不少用人单位对其完全不予认可；而与此同时，传统大学文凭在优质就业求职中的准入门槛功能不仅依旧稳固，甚至还进一步强化。欧洲则面临体制性风险：随着高校自身也开始开设颁发微证书，传统学历与替代性证书之间的边界日渐模糊，有可能同时削弱两者的信号价值。'
'6.2 The Digital Divide'	6.2 数字鸿沟
Alternative learning forms overwhelmingly require digital access, digital literacy, and self-directed learning capacity. These prerequisites are unevenly distributed both between and within countries. In China, the gap between urban and rural educational infrastructure remains substantial: while students in Beijing and Shanghai have access to world-class digital learning environments, students in rural Guizhou or Gansu may lack reliable internet connectivity. According to the China Internet Network Information Center (CNNIC), internet penetration in urban areas exceeds 80% but falls below 60% in rural areas — a gap that directly limits access to online learning platforms. In Europe, the digital divide between Northern and Southern, Eastern and Western member states creates similar inequities: Eurostat data show that the percentage of individuals with above-basic digital skills ranges from over 70% in the Netherlands and Finland to below 30% in Romania and Bulgaria.	各类替代性学习模式高度依赖数字接入条件、数字素养与自主学习能力。这些基础前置条件在国与国之间、一国内部均分布不均。在中国，城乡教育基础设施差距依旧悬殊：北京、上海等地学生可享用世界一流的数字化学习环境，而贵州、甘肃等乡村地区的学生甚至难以获得稳定的网络接入。中国互联网络信息中心（CNNIC）数据显示，我国城镇互联网普及率超 80%，农村地区则不足 60%，这一差距直接制约了乡村群体使用在线学习平台的渠道。在欧洲，成员国南北、东西之间的数字鸿沟也催生了同类教育不公现象。欧盟统计局数据显示，拥有中高阶数字技能的民众占比差异显著：荷兰、芬兰等国超过 70%，而罗马尼亚、保加利亚等国不足 30%。
The risk is that alternative learning forms, rather than democratising education, may reinforce existing inequalities — providing additional opportunities to the already advantaged while leaving behind those who most need reskilling. A coal miner in Shanxi or a textile worker in Portugal, displaced by automation, is unlikely to reskill through a Coursera nanodegree without substantial institutional support, digital infrastructure, and financial assistance. Any serious policy framework for alternative education must address access equity as a first-order concern, not an afterthought.	其中潜藏的风险在于：替代性学习模式非但没能推动教育普惠，反而可能固化既有社会不平等—— 只为原本就具备优势的群体提供更多发展机会，反而把最需要技能再培训的弱势群体甩在身后。因自动化浪潮而失业的山西煤矿工人、葡萄牙纺织工人，若缺少完备的制度支撑、数字基础设施与资金帮扶，很难仅凭 Coursera 平台的纳米学位完成职业技能转型。任何一套成熟的替代性教育政策框架，都必须把准入公平当作首要核心议题，而非事后补位的次要考量。
'6.3 The Loss of Humanistic Education'	6.3 人文教育的式微
The pressure to align education with labour market demands risks marginalising precisely the dimensions of education that are most resistant to AI disruption: humanistic inquiry, philosophical reflection, aesthetic appreciation, and civic formation. A workforce trained exclusively in „job-relevant“ skills may be technically competent but culturally impoverished — unable to ask the ethical, political, and existential questions that human societies need their members to ask. Ole Döring’s analysis in this volume, distinguishing between Verstand (technical intelligence) and Vernunft (practical wisdom), is directly relevant: alternative education must cultivate both if it is to serve human flourishing and not merely economic productivity.	迫于教育适配劳动力市场需求的现实压力，那些最不易被人工智能冲击替代的教育内核正面临边缘化风险：人文探究、哲学反思、审美涵养与公民人格塑造皆是如此。若人才培养只局限于 “职业相关” 技能，劳动者即便具备专业技术能力，也会陷入人文精神匮乏的困境，无力去思考人类社会必须直面的伦理、政治与终极存在等深层命题。奥莱・多林在本卷中的分析，对 ** 知性（Verstand，工具性智能）与理性（Vernunft，实践智慧）** 作出明确区分，极具现实参照意义：替代性教育若旨在实现人的全面发展，而非仅仅服务于经济产能提升，就必须兼顾二者的涵养培育。
'7. Conclusion: Toward a Balanced Model'	7 结语：走向均衡发展模式
The landscape of alternative education in Europe and China reveals a shared diagnosis — traditional educational models are increasingly misaligned with AI-era labour market demands — and divergent responses. Europe emphasises regulation, standardisation, and quality assurance. China emphasises scale, speed, and state-directed integration. Neither approach alone is sufficient.	中欧替代性教育的发展格局，呈现出共识与路径分野：双方均形成了一致判断 —— 传统教育模式已愈发难以适配人工智能时代劳动力市场的需求，但采取的应对路径却截然不同。欧洲侧重制度规制、标准统一与质量保障；中国侧重规模扩容、落地效率以及国家主导下的体系融合。两种模式若单独推行，都难以周全应对时代诉求。
We propose that the most productive path forward combines elements from both traditions:	我们认为，未来最具实效的发展路径，应当融合两种模式的有益特质：
From the European model: robust quality assurance frameworks that prevent credential inflation; recognition mechanisms that enable lifelong learning across institutional boundaries; and a commitment to humanistic education as an irreducible component of any educational programme, not an optional supplement.	借鉴欧洲模式的优势：建立完备的质量保障体系，遏制文凭通胀；搭建跨教育机构互通的资历认证机制，为终身学习打通壁垒；坚守人文教育内核，将其视作所有教育课程不可或缺的核心构成，而非可有可无的附加模块。
From the Chinese model: the willingness to experiment at scale; the integration of online and offline learning within formal university structures; and the pragmatic recognition that education must serve both individual development and collective economic needs.	借鉴中国模式的优势：敢于开展大规模试点探索；在正规高校体系内部推进线上线下融合教学；秉持务实共识，承认教育必须同时服务于个人发展与社会整体经济发展需求。
From neither model but urgently needed: a fundamental reconception of educational goals that shifts the centre of gravity from knowledge transmission to adaptive capacity. The most valuable education in the AI age is not the one that teaches students what to do — AI can do most specific tasks faster and more cheaply — but the one that teaches students how to think, how to learn, how to judge, and how to care.	超脱两种既有模式之外、却又亟需落地践行的核心变革：从根本上重塑教育目标，将教育重心从知识传授转向适应应变能力的培育。人工智能时代最有价值的教育，不在于教会学生做什么 —— 绝大多数具体事务，人工智能都能做得更快、成本更低；而在于教会学生如何思考、如何自主学习、如何理性判断、如何心怀人文关怀。
This does not mean that traditional universities should become skill-training centres, or that the humanities should be abandoned in favour of STEM education, or that every student should learn to code. It means that every educational institution — from primary school to postgraduate research — should ask itself: are we preparing learners for a world that no longer exists, or for the world that is emerging? The question is not rhetorical. The AI revolution is not coming; it has arrived. The educational revolution, in both Europe and China, has barely begun.	这并不意味着传统大学应当沦为技能培训机构，也不意味着要舍弃人文教育、一味偏向理工科教育，更不意味着所有学生都必须学习编程。真正的核心要义在于：从小学基础教育到研究生科研阶段，每一所教育机构都应当自我叩问 —— 我们培养学习者，是在为已然逝去的旧时代做准备，还是为正在成型的新时代赋能？这绝非空洞的修辞设问。人工智能革命并非即将到来，而是已然降临。而无论是欧洲还是中国，教育变革的进程才刚刚起步。
The alternative learning forms, institutions, and goals surveyed in this article represent the early experiments of that revolution. Some will prove ephemeral; others will reshape education for generations. The task for educational policymakers, researchers, and practitioners — in both Europe and China — is to identify which innovations serve human flourishing and which merely serve market convenience, and to build educational systems that do both without sacrificing either. These capacities are not alternative to traditional education but its deepest purpose, rediscovered under the pressure of technological change.	本文所探讨的各类替代性学习形态、办学机构与育人目标，正是这场教育变革的早期探索实践。其中部分尝试终将昙花一现，另一些则将重塑未来世代的教育格局。中欧两地的教育政策制定者、学术研究者与教育从业者，肩负着共同使命：甄别哪些教育创新真正服务于人的全面发展，哪些只是单纯迎合市场功利需求；进而构建兼顾双重价值、互不偏废的教育体系。这类核心素养培育并非传统教育的另类补充，而是其本源使命，只是在技术变革的时代驱动下，重新被世人重拾与回归。
'Acknowledgments'	致谢
Co-funded by the European Union. Views and opinions expressed are however those of the author only and do not necessarily reflect those of the European Union [101126782].	本项目由欧盟共同资助。文中所表达的观点仅代表作者个人立场，并不一定反映欧盟的官方观点（项目编号：101126782）。
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'Part III: Technology and Innovation'	第三部分：技术与创新
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