Rethinking Higher Education/Chapter 7/en-zh

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Chapter 7: Beyond the Classroom

Martin Woesler

English (Source)	中文 (Target)
== Beyond the Classroom: Alternative Learning Forms, Institutions, and Goals for the AI-Transformed Labour Market ==	== 超越课堂：面向人工智能转型劳动力市场的替代学习形式、机构与目标 ==
Martin Woesler	Martin Woesler
Hunan Normal University	湖南师范大学
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.	人工智能在经济生产中的加速融合不仅正在淘汰特定的职业类别，还正在颠覆这些职业所依据的教育假设。如果机器现在能够执行大多数常规认知任务——起草法律文件、编写代码、翻译文本、分析数据——那么以培养学生从事这些任务为目标的传统教育模式便面临合法性危机。本文考察了应对这一危机而兴起的替代学习形式、机构与教育目标，并系统比较了欧洲与中国的应对路径。我们分析了微证书与纳米学位、基于能力的教育、基于项目与挑战的学习、同伴学习、企业大学、编程训练营和开放教育平台，评估每种形式对人工智能转型经济中劳动力准备度的潜在贡献。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年）——与中国通过"教育现代化2035"和"新型基础设施"战略推行的国家驱动方法进行了比较。我们认为，无论是欧洲对正式认证的偏好还是中国对快速规模化的强调，都未能解决根本问题：为稳定知识领域设计的教育体系无法充分帮助学习者应对以根本性不确定性为特征的劳动力市场。我们提出，最具前景的发展方向是将教育目标从知识传递转向适应能力——即根据变化的需求进行学习、去学习和重新学习的能力。
Keywords: alternative education, AI labour market, micro-credentials, competency-based education, European-Chinese comparison, digital education, lifelong learning, workforce transformation	关键词：替代教育、人工智能劳动力市场、微证书、基于能力的教育、欧中比较、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
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需要改变——这是该调查开始以来预期的最大技能颠覆（WEF 2023）。麦肯锡全球研究院预测，到2030年，全球将有多达3.75亿工人需要因自动化和人工智能而转换职业类别（Manyika等人 2017）。在中国，制造业雇用超过1亿工人且服务业正在快速自动化的背景下，国务院2017年"新一代人工智能发展规划"承认人工智能将对"就业结构"带来"严峻挑战"（国务院 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.	这些预测有一个共同含义：传统教育模式——围绕学科知识组织的四年制大学学位、通过讲座和研讨班提供、通过考试评估、最终授予在整个职业生涯中有效的证书——与当代劳动力市场的需求之间的错位日益加剧。计算机科学毕业生的技术知识半衰期约为五年；数字营销专业毕业生可能在两年内发现其技能已过时。即使在传统上较为稳定的职业——法律、医学、会计——人工智能也在自动化那些曾经是合格专业人员专属领域的任务。
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.	本文考察了对这种错位的应对。我们调查了正在欧洲和中国涌现的替代学习形式、机构和教育目标，并评估它们为学习者应对以根本性不确定性为特征的劳动力市场所做准备的潜力。欧盟和中国代表着世界上两个最大、最具雄心的教育改革努力，但它们从根本不同的制度、文化和哲学立场出发应对这一挑战。欧洲的应对受博洛尼亚进程、ECTS框架以及制度自主和社会伙伴关系传统的塑造。中国的应对受集中化教育规划、正在进行刻意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意志的塑造。
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.	我们的分析力求保持平衡：我们不主张全面取代传统教育——传统教育保留着替代模式无法复制的重要功能——但我们认为，如果传统模式要保持其相关性，就必须加以补充，在某些情况下甚至需要从根本上重新构想。本文借鉴了来自欧洲和中国的政策文件、机构报告和比较教育研究，以及作者本人在德国和中国高等教育领域的工作经验。
2. Alternative Learning Forms	2. 替代学习形式
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——认证特定技能或能力的短期、聚焦学习单元——可能代表了近代教育史上最重要的结构性创新。欧盟委员会2022年关于微证书的建议将其定义为"学习者在少量学习后所获学习成果的记录"，通常以ECTS学分（1–5个ECTS）表示，并设计为可"堆叠"以获得更大的资格认证。
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，微证书生态系统在博洛尼亚进程框架内发展。欧盟理事会于2022年批准的《欧洲微证书方法》为成员国之间的质量保证、认可和可携性建立了共同标准。Coursera、edX、FutureLearn等MOOC平台以及大学联盟提供的微证书可获得ECTS学分，使学习者能够通过模块化学习路径积累被认可的资格。
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.	中国发展了一个平行但结构不同的体系。微证书（wēi zhèngshū）概念不如欧洲模式正式化但实践更为广泛。中国MOOC平台——学堂在线（清华大学）、中国大学MOOC（网易/高等教育出版社）和智慧树——提供数百万门课程，其中许多课程获得了中国高等教育体系内认可的学分。教育部2019年关于"一流线上开放课程"（一流线上开放课程）的政策正式将在线课程学分纳入大学课程体系。
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.	对比具有启发意义：欧洲强调标准化、质量保证和跨境认可；中国强调规模、速度和与现有大学结构的整合。两种方法都有其弱点。欧洲模式存在过度监管扼杀创新的风险。中国模式存在证书通胀的风险——技术上被"认可"但市场价值甚微的证书，原因在于其质量参差不齐。
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.	基于能力的教育（CBE）代表了对传统模式更为根本性的偏离。CBE不是通过在教学中花费的时间（学时、学期）来衡量学习，而是通过对既定能力的掌握展示来衡量学习。无论学习者花费多长时间，只要能够展示其所知所能，即可进阶。
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，CBE在职业教育与培训（VET）体系中被最热切地采用，尤其是在德国（双元制教育）、瑞士和荷兰。欧洲资格框架（EQF）本身就是基于能力的，以知识、技能和自主性而非学习年限来定义八个资格级别。
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，基于能力的方法正在职业教育改革中兴起。2019年"国家职业教育改革实施方案"（又称"职教20条"）引入了涵盖500余个职业类别的能力标准，并建立了"1+X"证书制度——一个学历证书（1）辅以多个职业技能等级证书（X）——作为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.	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。
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.	欧中比较揭示了一种有趣的趋同：两个体系都在向基于能力的方法迈进，但方向相反。欧洲正在已经灵活的模块化体系中添加能力框架。中国正在传统上僵化的、以考试为基础的体系中引入灵活性和模块化。共同的目标——一个认证学习者能做什么而非学了多久的教育框架——意义重大，即便通往这一目标的路径各有不同。
2.3 Project-Based and Challenge-Based Learning	2.3 基于项目与挑战的学习
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.	基于项目的学习（PBL）及其更具雄心的变体——基于挑战的学习（CBL）——将教育重心从知识获取转向问题解决。学生围绕真实或拟真的项目开展工作——开发一项产品、解决一个工程问题、创建一个社会企业——并在应对项目需求的过程中习得学科知识。
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.	芬兰的教育体系在国际评估中一贯表现出色、广受赞誉，已积极推进现象式学习——一种PBL变体，以跨学科项目替代部分课程中基于学科的教学。欧盟委员会的"地平线欧洲"计划资助将大学研究与现实问题相结合的基于挑战的教育项目。
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.	在中国，教育部2017年启动的"新工科"倡议在数百所大学的工程教育中引入了基于项目和跨学科的方法。清华大学的"挑战杯"竞赛和浙江大学的"创业课堂"项目是CBL理念的高知名度实践案例。
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.	PBL和CBL特别适合人工智能转型的劳动力市场，因为它们培养的恰恰是人工智能无法轻易复制的技能：创造性问题解决、协作谈判、不确定性下的伦理判断以及跨领域整合知识的能力。
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.	有趣的是，人工智能本身可能促进PBL的规模化。AI辅导系统可以提供基于项目的学习所需的个性化指导——回答技术问题、建议资源、对初稿提供反馈——而人类教师则专注于AI尚无法提供的高阶指导：提出具有挑战性的问题、提供情感支持以及示范专家从业者的思维习惯。这种教学设计中的人机互补呼应了我们在实证研究（Woesler，本卷）中在语言学习层面所记录的互补性。
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.	其局限性在于可扩展性：PBL需要密集的指导、灵活的评估和大多数大学尚未具备的制度结构。中国大学面临额外的挑战：应试文化产生了偏向标准化、客观可评估任务的强大压力，而PBL开放式、过程导向的方法恰与此相悖。欧洲大学面临不同的挑战：教师工作量模式和晋升标准奖励的是研究产出而非教学创新，这使得激励PBL所需的密集指导变得困难。
2.4 Peer-to-Peer and Community Learning	2.4 同伴学习与社区学习
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.	互联网使绕过机构结构的学习形式成为可能。同伴学习——具有互补知识的个体相互教学——在GitHub（编程）、Stack Overflow（技术问题解决）和知乎（中国的Quora对应平台，用于一般知识交流）等平台上蓬勃发展。社区学习更进一步：学习者围绕共同的学习目标自发组建社区，通常利用开放教育资源和免费工具。
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.	Etienne Wenger描述的"实践社区"模型——学习通过参与一个以共同领域、实践和身份认同为特征的社区而发生——已被数字连接所放大。在中国，微信学习群和抖音（TikTok）教育频道已成为重要的非正式学习环境，触及了正规教育所无法企及的受众。
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.	同伴学习的优势在于其适应性：社区可以围绕新兴技能——例如提示词工程或AI辅助设计——形成，远早于正规教育机构为其开发课程之前。其劣势在于质量保证：缺乏机构监督的同伴学习容易受到错误信息、信息茧房和达克效应的影响。
3. Alternative Institutions	3. 替代机构
3.1 MOOCs and Online Platforms	3.1 MOOC与在线平台
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.	大规模在线开放课程在2012年Coursera、edX和Udacity携精英大学内容上线时被誉为一场革命。这场革命被过度承诺了：完成率平均低于10%，且MOOC主要触及的是发达国家已受过教育的学习者。然而十年后，MOOC生态系统已发展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.	在欧洲，MOOC版图包括Coursera和edX与欧洲大学的合作、欧盟资助的欧洲MOOC联盟，以及法国的France Université Numérique（FUN）和德国的openHPI（哈索·普拉特纳研究所）等国家平台。欧盟在数字教育行动计划（2021–2027年）中阐述的数字技能承诺，包括支持在线学习平台作为终身学习和技能更新的工具。
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.	中国的MOOC生态系统按注册用户数更为庞大。清华大学开发的学堂在线服务超过1亿注册用户。由网易和高等教育出版社共同开发的中国大学MOOC提供超过900所中国大学的课程。教育部对在线课程学分的认可赋予了中国MOOC一种其西方同行往往缺乏的制度合法性。
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.	MOOC在人工智能时代面临的挑战颇具悖论意味：创造技能更新需求的同一AI技术也有可能自动化使MOOC有效运作的评估、反馈和个性化功能。AI驱动的辅导系统最终可能取代MOOC格式本身，提供完全个性化的学习路径而非标准化的课程序列。
3.2 Corporate Universities and Employer-Led Training	3.2 企业大学与雇主主导的培训
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.	主要科技公司已开发出在深度和市场价值上可与大学相媲美的教育项目。谷歌职业证书、亚马逊AWS培训和微软学习平台提供行业认可的证书，可在数周而非数年内完成。在中国，华为ICT学院、阿里巴巴达摩院培训项目和字节跳动内部大学发挥着类似功能。
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.	企业教育的兴起对大学的使命提出了根本性问题。如果雇主能够比大学更高效、更切合实际地培训工人，那么高等教育还剩下什么？我们认为，答案在于企业培训无法也无意提供的内容：知识的广度、批判性思维、伦理推理、文化理解，以及质疑——而非仅仅执行——机构目的的能力。谷歌证书教授云计算；它不教学生追问云计算是否有益于人类福祉。华为ICT认证教授网络工程；它不教学生思考其所构建的基础设施可能带来的监控隐患。
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.	这种区分并非对学术特权的势利辩护，而是反映了制度使命的真正差异：企业培训工人是为了服务企业利益；大学（在最佳状态下）教育公民是为了服务社会利益。两种功能都是必要的，但它们并不相同，将二者混为一谈——如某些"颠覆性"教育倡导者所主张的那样——有可能培养出技术上胜任但在伦理上缺乏批判力的劳动力。哲学家Hans Jonas的责任命令——我们必须确保自己的行为不会摧毁未来人类生存的条件——同样适用于教育设计，正如它适用于环境政策一样。
3.3 Bootcamps and Intensive Vocational Training	3.3 编程训练营与密集职业培训
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.	编程训练营——密集的短期（通常12–24周）培训项目——在2010年代作为进入技术领域的替代途径而兴起。这一模式已扩展到编程之外，涵盖数据科学、用户体验设计、数字营销，以及日益增长的人工智能和机器学习。在欧洲，Le Wagon（创立于巴黎，现在40多个城市运营）、Ironhack和Northcoders等训练营跨多个国家运营。在中国，拉勾教育、开课吧等平台以及众多小型机构每年为数十万学习者提供密集技术培训。
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.	训练营的优势在于与工作的相关性：课程在与雇主协商后设计并持续更新。其弱点在于狭隘性：毕业生获得了特定的技术技能，但可能缺乏在这些技能过时时——在人工智能领域，这可能在数月内发生——所需的更广泛理解和适应能力。训练营模式还引发公平性问题：欧洲5,000–15,000欧元和中国10,000–50,000元人民币的学费将接入限制在那些有能力投资于不确定结果的人群中。
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.	欧洲的开放大学传统——以英国开放大学（1969年成立）、德国哈根远程大学和西班牙UNED为代表——提供了先于数字革命的终身学习模式。这些机构的设计初衷是服务在职成人、非全日制学习者和被传统高等教育排斥在外的群体，其在灵活学习形式、远程教育和非传统评估方面的经验与AI时代的挑战直接相关。
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.	中国的对应机构——国家开放大学（原中央广播电视大学）及其3,000多个学习中心网络——服务约400万在册学生，是世界上最大的教育机构之一。该体系因毕业率偏低和质量参差而受到批评，但其覆盖地理分散学习者的基础设施为人工智能转型所需的大规模技能更新提供了基础。
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.	开放大学模式的核心洞见——教育必须在学习者所在之处与其相遇，而非要求他们去机构期望的地方——在当下愈发切合：AI驱动的劳动力市场颠覆影响的是所有职业阶段的工人，而不仅仅是应届毕业生。
4. Alternative Goals: From Knowledge to Capacity	4. 替代目标：从知识到能力
4.1 The Shift from Knowledge Transmission to Adaptive Capacity	4.1 从知识传递到适应能力的转变
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.	人工智能转型劳动力市场所要求的最根本变化不在于学习形式或机构，而在于教育目标。传统教育旨在传递知识：一套既定的事实、理论、方法和技能，期望学生加以掌握。这一模式的前提是所传递的知识在学生职业生涯的相当一段时间内仍保持其相关性。人工智能打破了这一前提。
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.	如果人工智能可以比任何人类更快、更准确地获取、组织和应用事实知识，那么教育的溢价就从"知道"转向"元知道"：快速学习新领域、批判性评估信息、综合不同领域的视角、提出正确的问题以及在数据模糊或不完整的情况下行使判断的能力。
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.	Alvin Toffler常被引用的预言——"21世纪的文盲不是那些不会读写的人，而是那些不会学习、去学习和重新学习的人"——已成为现实。教育面临的挑战是如何在不放弃使适应成为可能的基础知识的前提下，开发培养这种适应能力的课程。
4.2 AI Literacy as Core Competency	4.2 人工智能素养作为核心能力
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——理解、评估和适当使用人工智能工具的能力——正在迅速成为与数学素养和文字素养同等基本的能力。我们区分了人工智能素养的三个维度：
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：有效使用AI工具的能力——构造提示词、评估输出、理解机器学习的基本原理。这是最具直接实用性的维度，也是当前教育供给中最常涉及的维度。
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：理解AI能做什么和不能做什么，识别AI生成的内容，批判性地评估AI输出，理解模式识别与真正理解之间的区别。这一维度对于防止Fang Lu（本卷）在其案例研究中记录的那种对AI的非批判性过度依赖至关重要。
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AI部署的社会影响，包括算法偏见、隐私侵蚀、大型模型训练的环境成本、劳动力替代以及权力向少数科技公司集中等问题。这一维度将AI素养与更广泛的公民意识和社会责任问题联系起来。
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《人工智能法》（2024年）——全球首部全面的人工智能法规——隐含地在所有专业领域创造了对AI素养的需求：合规要求理解AI系统的功能、决策方式和潜在风险。该法基于风险的分类体系（不可接受风险、高风险、有限风险、最低风险）提供了可为全欧洲AI素养课程提供参考的概念框架。
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.	中国的方法更侧重于赋能而非限制：2023年"生成式人工智能服务管理暂行办法"侧重于登记和内容审核而非全面监管，但对AI素养的底层需求同样迫切。中国教育部已开始将AI内容纳入中小学课程，数所大学已设立专门的AI教育院系——尽管欧洲在监管方面处于领先地位，但在大规模推进这一发展方面相对迟缓。
4.3 Creativity, Critical Thinking, and Emotional Intelligence	4.3 创造力、批判性思维与情商
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.	如果AI处理常规认知任务，那么独特的人类能力——创造力、批判性思维、情商、伦理推理和跨文化交流——就成为经济价值的核心。世界经济论坛《未来就业报告》一贯将分析思维、创造性思维、韧性、灵活性和好奇心确定为雇主最看重的技能——这些能力无一属于特定领域的知识。挑战在于，这些能力恰恰是传统教育——以标准化评估和学科知识为重心——最不擅长培养的。
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.	欧洲的教育哲学，尤其是北欧模式，长期以来在学术成就之外强调创造力和批判性思维。芬兰2014年国家课程改革将"横向能力"——包括思维与学习能力、文化能力、多元素养和参与能力——列为从小学起所有教学的必修组成部分。欧盟终身学习关键能力框架确定了八项横向能力——包括"学会学习"、"社会与公民能力"和"文化意识与表达"——超越了学科知识的范畴。
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.	中国的教育改革努力，特别是自1990年代以来作为官方政策的素质教育运动，旨在以更广泛的能力发展来平衡应试教育文化。2021年的"双减"政策限制了课后辅导和作业负担，其明确动机是为创造力、体育活动和非学术发展腾出空间。然而，政策实施仍不均衡，高考继续对考试准备产生强大的向心力，挤压了创造性和批判性思维发展的空间。
4.4 Cross-Cultural Competence as Competitive Advantage	4.4 跨文化能力作为竞争优势
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.	在人工智能处理跨越语言和文化界限的常规任务的全球化经济中，驾驭文化差异的能力——理解不同的价值体系、沟通方式、谈判惯例和制度规范——成为具有重大经济价值的独特人类能力。这对本卷所代表的欧中教育对话尤为切要：能够有效跨越欧中鸿沟工作的专业人才拥有一种稀有且珍贵的能力，这是人工智能无法复制的。
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.	跨文化能力不仅仅是语言能力（人工智能可以越来越多地模拟），而是文化智力：解读模糊的社会信号、使行为适应不熟悉的文化语境以及跨文化界限建立信任的能力。培养这种能力的教育项目——通过交换项目、沉浸式体验、跨文化团队合作以及对另一种文化的知识传统的持续参与——代表了一种天然免于AI替代的替代教育形式，正因其依赖于具身的人类经验。
5. European Regulatory Frameworks vs. Chinese State-Driven Approaches	5. 欧洲监管框架与中国国家驱动方法
The contrast between European and Chinese educational governance illuminates the strengths and limitations of each system’s response to AI-driven disruption.	欧洲和中国教育治理之间的对比揭示了各自体系在应对AI驱动颠覆方面的优势与局限。
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.	欧盟的教育改革方法通过复杂的多层次治理结构运作。欧盟在教育政策上的直接权限有限（教育根据辅助性原则主要属于成员国的职责），但通过框架、建议和资助发挥着重要影响。
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.	数字教育行动计划（2021–2027年）确立了两个战略优先事项：促进高性能数字教育生态系统的发展，以及提升数字技能和能力以推动数字化转型。该计划资助数字基础设施、教师培训和在线学习方面的举措，但实施取决于成员国的采纳。
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.	欧洲技能议程（2020年）设定了多项目标，包括到2030年60%的成年人每年参与学习，以及创建个人学习账户以资助培训。微证书建议（2022年）提供了共同的定义和质量标准。欧洲资格框架（EQF）使跨境资格认可成为可能。
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.	这种方法的优势在于质量保证、可携性和信任：在共同标准下颁发的欧洲微证书在27个成员国内具有被认可的价值。弱点在于速度：谈判、通过和实施欧盟层面框架所需的时间意味着教育政策始终落后于技术变革。当一个用于"AI提示词工程"的欧洲微证书框架正式建立时，该技能本身可能已被自动化。
5.2 The Chinese Model: State Direction and Rapid Scaling	5.2 中国模式：国家指导与快速规模化
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.	中国的教育治理是集中化的。教育部制定国家政策，省级教育厅执行。这种结构使得快速、大规模的行动成为可能：当国务院发布国家战略——如"教育现代化2035"或"双一流"大学建设——资源可在数月而非数年内在数千所机构中调配，课程可迅速改革。
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.	"新基建"战略（2020年宣布）将人工智能列为七个战略基础设施类别之一，与5G、数据中心和工业互联网并列。教育被明确列为受益领域：AI驱动的自适应学习平台、智能辅导系统和自动化评估工具将被大规模开发和部署。
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.	这种方法的优势是执行速度和规模：中国建设教育基础设施——数字平台、培训项目、制度改革——的速度超过任何其他国家。弱点在于灵活性不足：集中化规划倾向于统一化，对国家战略和量化指标（课程数量、证书数量、参与者数量）的强调可能掩盖对学习深度、批判性思维和智识自主等质量关切的忽视。
5.3 Toward Mutual Learning	5.3 走向相互学习
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.	欧洲和中国的模式不仅不同而且互补。欧洲需要更多中国快速规模化和将替代证书整合到正式教育体系中的意愿，而无需数十年的审议。中国需要更多欧洲对质量保证、学习者自主性和教育哲学基础的强调——Döring（本卷）所称的知性（Verstand）与理性（Vernunft）之间的区分。
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.	两个体系之间的富有成效的对话——正如本卷以及支持它的让·莫内卓越中心所旨在促进的——不应聚焦于哪个模式"更好"，而应关注各自能从对方学到什么。人工智能对劳动力市场的变革是一个共同挑战；教育的应对也应当是共享的。
6. Risks and Critiques	6. 风险与批评
6.1 The Credentialism Trap	6.1 证书主义陷阱
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.	随着替代证书的增多，证书通胀的风险也在增长。如果微证书、纳米学位、训练营证书和企业认证在没有健全质量保证的情况下大量涌现，劳动力市场可能面临证书无法可靠反映能力的局面。无法评估数百种竞争证书质量的雇主可能退回到最简单的启发式标准：传统大学学位——恰恰是替代学习所要补充或取代的证书。
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.	这一悖论在中国已初露端倪：在线证书（网络证书）的泛滥导致许多雇主完全忽视它们，同时传统大学学位仍然保持（甚至加强了）其作为理想就业门槛的把关功能。在欧洲，风险是制度性的：随着大学自身开始提供微证书，"传统"与"替代"证书之间的界限变得模糊，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.	替代学习形式绝大多数需要数字接入、数字素养和自主学习能力。这些先决条件在国家之间和国家内部分布不均。在中国，城乡教育基础设施差距仍然显著：北京和上海的学生可以使用世界一流的数字学习环境，而贵州或甘肃农村的学生可能缺乏可靠的网络连接。据中国互联网络信息中心（CNNIC）数据，城市互联网普及率超过80%，而农村地区低于60%——这一差距直接限制了对在线学习平台的使用。在欧洲，北部与南部、东部与西部成员国之间的数字鸿沟造成了类似的不平等：Eurostat数据显示，拥有高于基本水平数字技能的人口比例从荷兰和芬兰的70%以上到罗马尼亚和保加利亚的30%以下不等。
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.	风险在于，替代学习形式非但不会使教育民主化，反而可能强化现有的不平等——为本已处于有利地位的人提供更多机会，同时将最需要技能更新的人抛在身后。山西的煤矿工人或葡萄牙的纺织工人因自动化而失业后，若缺乏充分的制度支持、数字基础设施和财务援助，不太可能通过Coursera纳米学位实现技能转型。任何严肃的替代教育政策框架都必须将接入公平作为优先考虑的问题，而非事后补救。
6.3 The Loss of Humanistic Education	6.3 人文教育的丧失
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.	将教育与劳动力市场需求对齐的压力可能边缘化恰恰最能抵抗人工智能颠覆的教育维度：人文探究、哲学反思、审美鉴赏和公民塑造。一支仅在"与工作相关"的技能方面受训的劳动力队伍可能技术上胜任却文化上贫瘠——无力提出人类社会需要其成员提出的伦理、政治和存在论问题。Ole Döring在本卷中的分析——区分知性（Verstand，技术智能）和理性（Vernunft，实践智慧）——与此直接相关：替代教育必须同时培养两者，才能服务于人类的全面发展而不仅仅是经济生产力。
7. Conclusion: Toward a Balanced Model	7. 结论：走向均衡模式
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.	欧洲和中国替代教育的格局揭示了一个共同的诊断——传统教育模式与AI时代劳动力市场需求之间的错位日益加剧——以及不同的应对策略。欧洲强调监管、标准化和质量保证。中国强调规模、速度和国家主导的整合。两种方法单独都不够充分。
We propose that the most productive path forward combines elements from both traditions:	我们认为最有成效的前进道路结合了两种传统的要素：
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.	从欧洲模式中：汲取防止证书通胀的健全质量保证框架；使终身学习跨越机构界限得以实现的认可机制；以及将人文教育视为任何教育项目不可或缺的组成部分而非可选附加的承诺。
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.	从中国模式中：汲取大规模实验的意愿；在正式大学结构中整合在线和线下学习的做法；以及务实地认识到教育必须同时服务于个人发展和集体经济需求。
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.	两种模式均未提供但迫切需要的是：对教育目标进行根本性的重新构想，将重心从知识传递转向适应能力。人工智能时代最有价值的教育不是教学生做什么——人工智能可以更快、更便宜地完成大多数具体任务——而是教学生如何思考、如何学习、如何判断和如何关怀。
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变成技能培训中心，或人文学科应被STEM教育取代，或每个学生都应学会编程。它意味着每个教育机构——从小学到研究生教育——都应自问：我们是在为一个不再存在的世界做准备，还是在为正在形成的世界做准备？这个问题并非修辞。人工智能革命不是即将到来——它已经到来。教育革命，无论在欧洲还是中国，才刚刚开始。
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.	本文所调查的替代学习形式、机构和目标代表了这场革命的早期实验。有些将转瞬即逝；有些将重塑未来几代人的教育。教育政策制定者、研究者和实践者——无论在欧洲还是中国——的任务是辨别哪些创新服务于人类的全面发展，哪些仅仅服务于市场便利，并建立兼顾两者而不牺牲其一的教育体系。这些能力不是传统教育的替代品，而是其最深层使命在技术变革压力下的重新发现。
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].	由欧盟共同资助。所表达的观点和意见仅代表作者本人，不一定反映欧盟的立场[101126782]。
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Part III: Technology and Innovation	第三部分：技术与创新