Rethinking Higher Education/Chapter 9/en-zh

Language: EN · ZH · EN-ZH · ← Book

📌 Hinweis (Stand 8.5.2026): Diese Seite wurde strukturell überarbeitet, damit jeder Absatz seinen eigenen Tabellen-Row hat. Bisherige chinesische Übersetzungen wurden automatisch zugeordnet — die Zuordnung ist nicht in jedem Fall korrekt. Bitte prüfen Sie die rechte Spalte und verschieben/korrigieren Sie die ZH-Übersetzungen, falls sie nicht zum DE-Absatz passen. Bei nicht übersetzten Absätzen steht (zu übersetzen).

English (Source)	中文 (Übersetzung)
	(zu übersetzen)
Language: EN · ZH · EN-ZH · ← Book	(zu übersetzen)
	(zu übersetzen)
Digital Natives in China and Europe: Comparative Digital Literacy, AI Attitudes, and Educational Implications	(中国与欧洲的数字原住民：数字素养、人工智能态度及教育启示的比较研究)
Martin Woesler	(吴漠汀)
'Abstract'	(摘要)
The concept of the „digital native“ — introduced by Marc Prensky in 2001 to describe a generation supposedly transformed by immersion in digital technology — has profoundly influenced educational policy on both sides of the Eurasian continent. Yet two decades of empirical research have consistently failed to validate its central claim: that growing up with technology produces uniformly high digital competence. This article examines digital literacy, AI attitudes, and educational implications through a systematic comparison of the European Union and China, drawing on the EU’s DigComp 2.2 framework (250+ competence examples across 21 areas), China’s centralized digital literacy campaigns, and recent empirical studies of student and teacher digital competence. We document significant gaps: only 55.6 percent of the EU population possesses at least basic digital skills, despite the Digital Decade target of 80 percent by 2030; China has achieved 99.9 percent broadband connectivity in schools while rural internet penetration remains at 69.5 percent. A multinational assessment of 1,465 university students across Germany, the United Kingdom, and the United States reveals substantial cross-national variation in AI literacy, while a latent profile analysis of 782 Chinese EFL teachers identifies four distinct AI literacy profiles ranging from „poor“ (12.1 percent) to „excellent“ (14.1 percent). We argue that the digital native myth has created dangerous policy assumptions — that young people need less, rather than more, structured digital education — and that both European and Chinese approaches must shift from measuring access to cultivating critical digital competence, AI literacy, and the capacity for responsible digital citizenship.	(“数字原住民”这一概念由马克·普伦斯基（Marc Prensky）于2001年提出，旨在描述一代据称因沉浸于数字技术而发生质变的人群，该概念对欧亚大陆两侧的教育政策均产生了深远影响。然而，过去二十年的实证研究始终未能证实其核心主张：伴随着科技成长就能自然产生普遍且高水平的数字能力。本文基于欧盟与中国的系统性比较，对数字素养、对人工智能（AI）的态度以及教育启示进行了探讨。研究依据包括：欧盟的 DigComp 2.2 框架（涵盖21个领域的250多个能力示例）、中国集中的数字素养推广行动，以及近期关于师生数字能力的实证研究。 我们发现了显著的差距：尽管欧盟设定了到2030年达到80%的“数字十年”目标，但目前仅有55.6%的欧盟人口具备至少基本的数字技能；中国虽然实现了学校99.9%的宽带接入，但农村地区的互联网普及率仍为69.5%。一项针对德国、英国和美国1465名大学生的跨国评估显示，各国在人工智能素养方面存在显著的跨国差异；而一项针对782名中国英语作为外语（EFL）教师的潜在剖面分析，则识别出四种截然不同的人工智能素养特征，涵盖从“较差”（占12.1%）到“优秀”（占14.1%）的区间。我们认为，“数字原住民”的神话制造了危险的政策预设——即年轻人需要的是更少而非更多的结构化数字教育。因此，欧洲和中国的相关策略都必须从单纯衡量“接入程度”，转向培养批判性数字能力、人工智能素养以及成为负责任的数字公民的能力。)
Keywords: digital natives, digital literacy, AI literacy, DigComp 2.2, China digital education, European digital skills, digital divide, Gen Z, digital competence, comparative education	(关键词：数字原住民，数字素养，人工智能素养，DigComp 2.2，中国数字教育，欧洲数字技能，数字鸿沟，Z世代，数字能力，比较教育)
'1. Introduction'	(1. 引言)
In 2001, Marc Prensky published a short essay in On the Horizon that would reshape educational discourse for a generation. „Digital Natives, Digital Immigrants“ argued that students entering the education system had been fundamentally transformed by their immersion in digital technology: they „think and process information fundamentally differently from their predecessors,“ and educators — digital immigrants who had adopted technology later in life — must adapt or become irrelevant (Prensky 2001). The metaphor was powerful, intuitive, and immediately influential. Within a decade, it had become a foundational assumption of educational technology policy worldwide.	(2001年，马克·普伦斯基（Marc Prensky）在《地平线》（On the Horizon）杂志上发表了一篇短文，这篇文章彻底重塑了随后一代人的教育话语体系。他在《数字原住民，数字移民》一文中提出，步入教育体系的学生们已经因其沉浸于数字技术而发生了根本性的转变：他们“思考和加工信息的方式与前人有着本质的区别”。因此，作为教育者的“数字移民”（那些在生命后期才接触科技的人）必须主动适应，否则将面临被淘汰的命运（Prensky 2001）。这个比喻既有力又直观，并迅速产生了广泛的影响。不到十年，它便成为了全球教育技术政策的一项基础性预设。)
It was also, as subsequent research would demonstrate, largely wrong. Bennett, Maton, and Kervin (2008), in what remains the most widely cited critical assessment, showed that the empirical evidence did not support claims of a generation with uniformly high technological skills or radically different learning styles. The variation within age cohorts far exceeded the variation between them. Socioeconomic status, educational background, and individual motivation were far stronger predictors of digital competence than generational membership. The „digital natives„ debate, they concluded, resembled an academic „moral panic“ more than an evidence-based policy framework. Reid, Button, and Brommeyer (2023) confirmed these findings in a narrative review spanning two further decades of evidence: exposure to digital technologies does not equate to digital literacy, and the myth has created deficits in educational programs by assuming students already possess adequate digital skills.	然而，正如后续研究所证明的，它在很大程度上是错误的。Bennett、Maton和Kervin（2008）在迄今被引用最多的批判性评估中表明，实证证据不支持关于一代人具有普遍高技术技能或根本不同学习方式的主张。同一年龄群体内部的差异远大于群体之间的差异。社会经济地位、教育背景和个人动机是数字能力的预测因素，其效力远强于代际成员身份。他们得出结论，"数字原住民"之争更像是一场学术"道德恐慌"，而非基于证据的政策框架。Reid、Button和Brommeyer（2023）在涵盖此后二十年证据的叙述性综述中确认了这些发现：接触数字技术并不等同于数字素养，这一神话因假设学生已具备足够的数字技能而在教育项目中制造了缺陷。
Mertala and colleagues (2024), in a bibliometric analysis of 1,886 articles published between 2001 and 2022, document the remarkable persistence of the digital native concept despite its empirical weakness. The initial literature relied on unvalidated claims and waned upon facing empirical challenges, yet the concept continues to shape policy and public discourse — particularly in contexts where rapid digitalization creates pressure to demonstrate technological readiness.	(梅尔塔拉及其同事（2024），在对2001年至2022年间发表的1886篇文章进行文献计量分析后发现，尽管“数字原住民”这一概念在实证上站不住脚，却依然表现出了惊人的持久性。早期的相关文献主要依赖于未经证实的论断，并在面临实证挑战后逐渐式微，但这一概念至今仍深刻影响着政策制定和公共话语——特别是在那些快速数字化带来巨大压力、迫切需要通过展示“技术就绪”来证明自身实力的环境中。)
This article examines the contemporary reality behind the digital native myth through a systematic comparison of the European Union and China. Both are rapidly digitizing their education systems. Both face significant digital divides. Both are developing frameworks to measure and cultivate digital competence. Yet they approach these challenges from fundamentally different institutional, cultural, and political positions. By comparing their frameworks, their empirical outcomes, and their policy responses, we aim to move beyond the digital native myth toward an evidence-based understanding of what young people in China and Europe actually know, can do, and need to learn about digital technology and artificial intelligence.	(本文旨在通过系统比较欧盟与中国，来审视“数字原住民”神话背后的当代现实。双方都在快速推进各自教育体系的数字化进程，也都面临着显著的数字鸿沟，并且都在着手制定框架以衡量和培养数字能力。然而，它们在应对这些挑战时，所立足的制度、文化和政治立场却有着根本性的不同。通过对比双方的框架体系、实证成果以及政策响应，我们旨在超越“数字原住民”这一神话，基于证据去深入理解中国和欧洲的年轻人在数字技术与人工智能方面，实际上究竟懂什么、能做什么，以及需要学习什么。)
'2. Frameworks: DigComp 2.2 versus China‘s Digital Literacy Initiatives'	(2. 框架体系：欧盟 DigComp 2.2 与中国数字素养倡议的比较)
'2.1 The European Approach: DigComp 2.2'	(2.1 欧洲模式：DigComp 2.2)
The European Union‘s primary instrument for defining and measuring digital competence is the Digital Competence Framework for Citizens (DigComp), developed by the Joint Research Centre. The most recent version, DigComp 2.2, published in 2022, provides over 250 new examples of knowledge, skills, and attitudes organized across 21 competences in five areas: information and data literacy, communication and collaboration, digital content creation, safety, and problem solving. Notably, the 2022 update incorporates examples related to artificial intelligence systems and data-driven technologies, reflecting the recognition that digital competence now encompasses AI literacy as a core component (Vuorikari, Kluzer, and Punie 2022).	(欧盟用于定义和衡量数字能力的主要工具是《公民数字能力框架》（DigComp），该框架由欧盟委员会联合研究中心（JRC）制定。最新版本 DigComp 2.2 于2022年发布，在信息素养、沟通与协作、数字内容创作、安全以及问题解决这五大领域、共计21项具体能力中，提供了超过250个关于知识、技能和态度的新示例。值得注意的是，2022年的这次更新纳入了与人工智能系统及数据驱动技术相关的示例，这反映出欧盟已经认识到：数字能力如今已将人工智能素养涵盖为其核心组成部分（Vuorikari, Kluzer, and Punie 2022）。)
DigComp 2.2 is explicitly citizen-oriented. Its competence descriptors are designed to be applicable to all individuals regardless of professional context, and it serves as the reference framework for the EU’s Digital Decade target of 80 percent of citizens with at least basic digital skills by 2030. The framework has been adopted or adapted by numerous member states for national curricula, teacher training programs, and digital skills assessment tools.	(DigComp 2.2 具有明确的“公民导向”。其能力描述符旨在适用于所有个人，不受职业背景的限制。同时，它也作为欧盟“数字十年”计划的参考框架，支撑着“到2030年至少80%的公民具备基本数字技能”这一目标的实现。目前，已有众多欧盟成员国采纳或改编了该框架，将其应用于本国的课程设置、教师培训项目以及数字技能评估工具中。)
The Digital Education Action Plan 2021–2027 provides the strategic context for DigComp’s implementation in education. The plan establishes 14 actions across two priority areas — fostering a high-performing digital education ecosystem and enhancing digital skills and competences — with specific targets for updating DigComp to incorporate AI and data skills and for establishing a European Digital Skills Certificate (European Commission 2020).	《数字教育行动计划2021–2027》为DigComp在教育中的实施提供了战略背景。该计划在两个优先领域——促进高性能数字教育生态系统和提升数字技能和能力——下确立了14项行动，包括更新DigComp以纳入AI和数据技能以及建立欧洲数字技能证书等具体目标（European Commission 2020）。
'2.2 The Chinese Approach: Centralized Digital Literacy Campaigns'	(2.2 中国模式：集中式的数字素养提升行动)
China‘s approach to digital literacy differs fundamentally in its institutional architecture. Rather than a single citizen-oriented framework, China deploys digital literacy through centralized government-led initiatives coordinated across multiple ministries. The 2025 Plan for Enhancing National Digital Literacy and Skills, jointly issued by the Office of the Central Cyberspace Affairs Commission, the Ministry of Education, the Ministry of Industry and Information Technology, and the Ministry of Human Resources and Social Security, establishes priorities including developing digital talent cultivation systems, expanding AI application and governance, building an inclusive digital society, and promoting international cooperation (CNNIC 2025; Central Cyberspace Affairs Commission et al. 2025).	(中国的数字素养推进路径在制度架构上存在根本性的差异。中国并没有采取单一的、以公民为导向的框架，而是通过跨多个部委协同的、政府主导的集中化举措来部署数字素养工作。由中央网络安全和信息化委员会办公室、教育部、工业和信息化部以及人力资源和社会保障部联合印发的《全民数字素养与技能提升2025年规划》，确立了若干重点任务，包括建立健全数字人才培养体系、拓展人工智能应用与治理、构建包容性数字社会以及推动国际合作（CNNIC 2025; 中央网信办等 2025）。)
The Education Informatization 2.0 Action Plan, launched in 2018, set targets for teaching applications covering all teachers, learning applications covering all students, and digital campus construction covering all schools (Yan and Yang 2021). The results have been dramatic in infrastructure terms: by 2025, 99.9 percent of all Chinese schools have 100 Mbps or faster broadband, 99.5 percent have multimedia classrooms, and over 75 percent offer wireless campus internet. The National Smart Education Platform connects 519,000 schools, serving 18.8 million teachers and 293 million students (Ma 2025).	《教育信息化2.0行动计划》（2018年启动）设定了教学应用覆盖全体教师、学习应用覆盖全体学生、数字校园建设覆盖全部学校的目标（Yan和Yang 2021）。在基础设施方面的成果是显著的：到2025年，99.9%的中国学校拥有100Mbps或更快的宽带，99.5%拥有多媒体教室，超过75%提供校园无线网络。国家智慧教育平台连接了519,000所学校，服务1,880万教师和2.93亿学生（Ma 2025）。
Wang and d’Haenens (2025), in what appears to be the first direct comparison of the EU’s State of the Digital Decade 2024 report and China‘s National Digital Literacy and Skills Development Survey Report 2024, identify a characteristic pattern: China’s progress is attributable to centralized government-led initiatives that achieve rapid infrastructure deployment and standardization, while the EU’s approach emphasizes individual competence development through framework-based assessment. Both face persistent challenges — China with the urban-rural divide, the EU with inter-state variation — but the nature of those challenges reflects their different institutional models.	(Wang 和 d’Haenens（2025）对欧盟的《2024年数字十年状况》报告与中国的《2024年全民数字素养与技能发展调查报告》进行了（目前看来）首次直接比较。他们从中发现了一种典型的模式：中国的进步主要归功于政府主导的集中化举措，这种模式能够实现基础设施的快速部署和标准化；而欧盟的路径则更侧重于通过基于框架的评估来提升个人能力。尽管双方都面临着持续性的挑战——中国主要在于城乡差距，欧盟则在于各成员国之间的差异——但这些挑战的本质恰恰反映了两者截然不同的制度模式。)
Wu (2024) proposes a Digital Literacy Framework for Chinese College Students structured as a progressive „Skills–Competencies–Awareness“ relationship, identifying 15 descriptors validated through empirical research. This framework reflects a growing recognition in Chinese educational research that infrastructure deployment alone is insufficient: students need structured competence development, not merely access to technology.	(Wu（2024）提出了一个面向中国大学生的数字素养框架，该框架以‘技能–能力–意识’的递进关系为结构，并确立了15项经过实证研究验证的描述指标。这一框架反映出中国教育研究领域日益增长的共识：单靠基础设施的部署是远远不够的，学生需要的是结构化的能力培养，而不仅仅是获得技术接入的机会。)
'3. AI Literacy Across Borders'	(3. 跨国界的人工智能素养)
'3.1 Policy Landscape'	(3.1 政策图景)
The rapid deployment of AI systems in education and the workplace has generated a parallel demand for AI literacy — the ability to critically evaluate AI technologies, communicate and collaborate effectively with AI, and use AI as a tool (Long and Magerko 2020). Yang and colleagues (2025), in a comparative analysis of 41 AI literacy policies across the European Union, the United States, India, and China, find convergent strategies — all four jurisdictions are expanding AI and STEM programs in higher education — alongside significant divergences that reflect different labor market conditions and strategic priorities.	人工智能系统在教育和工作场所的快速部署产生了对AI素养的并行需求——批判性评估AI技术、与AI有效沟通和协作以及将AI作为工具使用的能力（Long和Magerko 2020）。Yang等人（2025）在对欧盟、美国、印度和中国41项AI素养政策的比较分析中发现了趋同的策略——四个辖区都在扩大高等教育中的AI和STEM项目——同时也存在反映不同劳动力市场条件和战略优先事项的显著分歧。
The EU AI Act (Regulation 2024/1689) introduced a specific AI literacy obligation. Article 4, which took effect on 2 February 2025, mandates that providers and deployers of AI systems „shall take measures to ensure, to their best extent, a sufficient level of AI literacy of their staff“ (European Parliament and Council 2024). This provision applies directly to universities deploying AI tools for teaching, assessment, or administration, creating a legal obligation for AI literacy training that has no direct equivalent in Chinese law.	(欧盟《人工智能法案》（法规 2024/1689）专门引入了一项关于人工智能素养的法定义务。该法案第4条于2025年2月2日正式生效，强制规定人工智能系统的提供商和部署者‘应在其能力范围内采取措施，确保其员工具备足够的人工智能素养’（European Parliament and Council 2024）。这一条款直接适用于那些在教学、评估或行政管理中部署人工智能工具的大学，从而为人工智能素养培训确立了法律义务，而这一点在中国法律中并没有直接的对应规定。)
China‘s approach integrates AI literacy into its broader digital literacy campaigns and, from September 2025, into mandatory AI education in all primary and secondary schools. The 2025 Plan for Enhancing National Digital Literacy and Skills explicitly addresses AI application and governance as a priority area. Hilliard and colleagues (2026), in a comparative analysis of AI policies across eight jurisdictions, document China’s distinctive approach: sector-specific regulation combined with centralized deployment of AI education at scale.	(中国的模式是将人工智能素养融入更广泛的数字素养提升行动中，并计划从2025年9月起，将其纳入全国所有中小学的必修课程。《全民数字素养与技能提升2025年规划》明确将人工智能的应用与治理列为重点发展领域。Hilliard及其同事（2026）在对八个司法管辖区的人工智能政策进行比较分析后，记录了中国独具特色的做法：即行业专项监管与大规模集中部署人工智能教育相结合。)
'3.2 Empirical Findings'	(3.2 实证研究发现)
Empirical studies reveal significant variation in AI literacy across national contexts. Hornberger and colleagues (2025), in a multinational assessment of 1,465 university students across Germany, the United Kingdom, and the United States, find that German students demonstrate higher AI literacy, UK students hold more negative attitudes toward AI, and US students report greater AI self-efficacy. These differences persist even after controlling for demographic variables, suggesting that national educational and cultural contexts shape AI literacy in ways that generic frameworks do not capture.	(实证研究表明，不同国家背景下的AI素养存在显著差异。Hornberger及其同事（2025）对来自德国、英国和美国的1465名大学生进行了一项跨国评估。他们发现，德国学生的AI素养水平更高，英国学生对AI持有更负面的态度，而美国学生则表现出更强的AI自我效能感。即使在控制了人口统计学变量后，这些差异依然存在。这表明，国家层面的教育与文化背景会以通用框架无法捕捉的方式，深刻影响着AI素养的形成。)
In the Chinese context, Pan and Wang (2025) present a latent profile analysis of 782 Chinese EFL teachers that identifies four distinct AI literacy profiles: poor AI literacy (12.1 percent), moderate (45.5 percent), good (28.4 percent), and excellent (14.1 percent). Age and teaching experience significantly predict profile membership, with younger teachers generally demonstrating higher AI literacy but not uniformly so. The finding that nearly 58 percent of teachers fall in the poor or moderate categories has significant implications for AI literacy education: if teachers themselves lack AI competence, their capacity to develop it in students is necessarily limited.	(在中国语境下，Pan和Wang（2025）对782名中国英语作为外语（EFL）教师进行了潜在剖面分析，识别出四种截然不同的AI素养特征剖面：较差（占12.1%）、中等（45.5%）、良好（28.4%）和优秀（14.1%）。年龄和教龄是预测剖面归属的显著变量，总体而言，年轻教师表现出更高的AI素养，但这一规律并非绝对。研究发现，近58%的教师处于较差或中等水平，这对AI素养教育具有重要的启示意义：如果教师自身缺乏AI能力，那么他们培养学生AI能力的潜力必然会受到限制。)
Zhang, Ganapathy Prasad, and Schroeder (2025), in a systematic review of reviews on AI literacy, synthesize the rapidly growing field and identify a persistent gap between policy ambitions and educational practice. The review confirms that AI literacy education remains in its early stages in both European and Chinese universities, with most initiatives focused on awareness rather than critical evaluation or practical competence.	(Zhang, Ganapathy Prasad 和 Schroeder（2025）在对AI素养的综述文章进行系统性回顾时，综合梳理了这一快速发展的领域，并指出了政策雄心与教育实践之间持续存在的落差。该综述证实，无论是在欧洲还是中国的大学中，AI素养教育仍处于起步阶段，大多数举措主要侧重于提升认知，而非批判性评估或实践能力。)
The foundational work of Long and Magerko (2020) provides a conceptual framework for addressing this gap. Their definition of AI literacy — „a set of competencies that enables individuals to critically evaluate AI technologies; communicate and collaborate effectively with AI; and use AI as a tool“ — identifies 17 competencies across five themes. This framework has been widely adopted but not yet systematically implemented in either European or Chinese curricula. The gap between framework availability and educational practice is a recurring theme across both jurisdictions: sophisticated competence descriptions exist on paper, but translation into classroom practice remains the fundamental challenge.	(Long 和 Magerko（2020）的奠基性工作为弥补这一落差提供了概念框架。他们将AI素养定义为‘一套能力集合，使个体能够批判性地评估AI技术；与AI进行有效的沟通与协作；并将AI作为工具加以使用’。该框架涵盖了五大主题下的17项具体能力。尽管这一框架已被广泛采纳，但尚未在欧洲或中国的课程中得到系统性的落实。框架的可用性与教育实践之间的落差，是这两个司法管辖区共同面临的 recurring theme（反复出现的主题）：纸面上已经存在完善的能力描述，但如何将其转化为真实的课堂教学实践，依然是根本性的挑战。)
The cross-national variations documented in these studies have important implications for the design of international educational programs. A joint EU-China degree program cannot assume that students from both contexts arrive with equivalent digital and AI competences. German students’ higher AI literacy (Hornberger et al. 2025) and Chinese teachers’ AI literacy deficits (Pan and Wang 2025) suggest that international programs must diagnose and address digital competence asymmetries as a prerequisite for effective collaboration — a finding that connects directly to the data protection and ethical challenges discussed in the companion chapters of this anthology (Woesler, this volume).	(这些研究记录的跨国差异，对国际教育项目的设计具有重要的启示意义。一个中欧联合学位项目不能想当然地认为，来自双方的学生都具备同等的数字与AI能力。德国学生较高的AI素养（Hornberger et al. 2025）与中国教师在AI素养上的短板（Pan and Wang 2025）表明，国际项目必须将诊断并弥补数字能力上的不对称为前提，才能实现有效的协作——这一发现与本选集其他章节（Woesler, 本书）所讨论的数据保护与伦理挑战直接相关。)
'4. The Digital Divide'	(4. 数字鸿沟)
'4.1 China: The Urban-Rural Gap'	(4.1 中国：城乡数字鸿沟)
China‘s digital divide is primarily geographic. The 55th Statistical Report on China’s Internet Development, published by the China Internet Network Information Center (CNNIC) in 2025, reports 1.099 billion internet users as of December 2024, representing a national penetration rate of 79.0 percent. However, rural internet penetration stands at 69.5 percent, nearly ten percentage points below the national average, and urban users constitute 71.3 percent of total internet users (CNNIC 2025).	(中国的数字鸿沟主要体现在地理维度上。中国互联网络信息中心（CNNIC）于2025年发布的第55次《中国互联网络发展状况统计报告》显示，截至2024年12月，中国网民规模达10.99亿，全国互联网普及率为79.0%。然而，农村地区的互联网普及率仅为69.5%，比全国平均水平低近10个百分点；此外，城镇网民在全国网民总数中占比高达71.3%（CNNIC 2025）。)
The infrastructure achievements are nonetheless remarkable. With 99.9 percent of schools connected to broadband and the National Smart Education Platform serving 293 million students, the physical prerequisites for digital education are largely in place (Ma 2025). The challenge has shifted from access to quality: ensuring that rural students receive the same quality of digital education as their urban counterparts, despite differences in teacher competence, institutional resources, and cultural capital.	(不过，基础设施建设取得的成就依然令人瞩目。随着99.9%的学校接入宽带，以及国家智慧教育平台服务2.93亿学生，数字教育的物理前提已基本具备（Ma 2025）。当前的挑战已从‘接入’转向‘质量’：尽管在教师能力、机构资源和文化资本上存在差异，但如何确保农村学生能够获得与城市学生同等质量的数字教育，成为了新的课题。)
The Regulations on the Protection of Minors in Cyberspace, effective 1 January 2024, add a regulatory dimension to the digital divide. The regulations require mandatory internet addiction prevention measures, „minor modes“ on platforms, and screen time limits — provisions that reflect Chinese policymakers’ awareness that digital access without digital literacy and parental oversight can produce harm rather than benefit (State Council 2023). Zheng and colleagues (2025), in a comprehensive meta-analysis of 164 epidemiological studies involving 737,384 Chinese adolescents, find a pooled internet addiction prevalence of 10.3 percent, with rural adolescents showing higher rates — a finding that underscores the need for digital literacy education that addresses risks as well as opportunities.	2024年1月1日生效的《未成年人网络保护条例》为数字鸿沟增添了监管维度。该条例要求强制实施网络成瘾预防措施、平台"未成年人模式"和屏幕时间限制——这些规定反映了中国政策制定者的认识：没有数字素养和家长监督的数字接入可能产生危害而非收益（国务院 2023）。Zheng等人（2025）在涵盖737,384名中国青少年的164项流行病学研究的综合荟萃分析中发现，互联网成瘾的合并患病率为10.3%，农村青少年比率更高——这一发现强调了数字素养教育需要同时应对风险和机遇。
'4.2 Europe: Socioeconomic and Inter-State Variation'	(4.2 欧洲：社会经济与国家间的差异格局)
The European digital divide operates along different axes: socioeconomic status, educational attainment, age, and — critically — member state. The European Commission’s State of the Digital Decade 2025 report documents that only 55.6 percent of the EU population possesses at least basic digital skills, far short of the 80 percent target for 2030. At the current pace of progress, the target will not be met. The Netherlands (83 percent) and Finland (82 percent) lead in basic digital skills, while Romania (28 percent) and Bulgaria (36 percent) lag far behind (European Commission 2025; Eurofound 2025).	(欧洲的数字鸿沟沿着不同的轴线展开：社会经济地位、受教育程度、年龄，以及——尤为关键的——所属成员国。欧盟委员会发布的《2025年数字十年现状》报告显示，欧盟人口中仅有55.6%具备至少基本的数字技能，远低于2030年80%的目标。按照目前的进展速度，这一目标将无法实现。在基本数字技能方面，荷兰（83%）和芬兰（82%）处于领先地位，而罗马尼亚（28%）和保加利亚（36%）则远远落后（European Commission 2025; Eurofound 2025）。)
Eurofound’s 2025 report on the digital divide documents that historically lower-performing member states have been catching up with digital leaders, but significant inequalities persist. Vulnerable groups — low-income, older, less educated populations — remain disproportionately affected. The digital divide in Europe is thus not primarily a generational divide but a socioeconomic one, further undermining the digital native assumption that age cohort is the primary determinant of digital competence.	(“欧洲劳工基金会2025年关于数字鸿沟的报告指出，历史上数字表现较弱的成员国虽然在不断追赶数字领先国家，但显著的不平等现象依然存在。弱势群体——即低收入、高龄和低学历人群——受到的冲击依然最为严重。因此，欧洲的数字鸿沟主要并非代际鸿沟，而是社会经济鸿沟，这进一步动摇了‘数字原住民’的假设，即年龄层是决定数字能力的首要因素。)
PISA 2022 data provide an educational lens on the divide. Students spending up to one hour per day on digital devices for learning scored 14 points higher in mathematics, but students distracted by others’ device use scored 15 points lower. Only 60 percent of students expressed confidence in their ability to manage their own motivation for digital schoolwork (OECD 2023). These findings suggest that the relationship between digital technology and educational outcomes is mediated by context, pedagogy, and self-regulation — not by generational membership.	(PISA 2022的数据为审视这一鸿沟提供了教育学的视角。数据显示，每天使用数字设备学习不超过一小时的学生，其数学成绩要高出14分；而那些因他人使用设备而分心的学生，数学成绩则低了15分。此外，只有60%的学生对自己管理数字化学习动力的能力表示有信心（OECD 2023）。这些发现表明，数字技术与教育成果之间的关系，是由具体情境、教学法以及自我调节能力所中介的，而非由代际归属所决定。)
'5. Screen Time, Digital Habits, and Platform Ecosystems'	(5. 屏幕时间、数字习惯与平台生态系统)
The digital environments inhabited by young people in China and Europe differ not only in scale but in kind. Chinese youth primarily use WeChat (95.76 percent), QQ (72.25 percent), Douyin (65.57 percent), and Little Red Book (Xiaohongshu, 36.50 percent). Zhao, Wang, and Hu (2025) document a pattern of „platform-swinging“ — spontaneous movement between platforms driven by peer resonance, self-management needs, and content discovery — that challenges the assumption of stable digital identities.	(中国和欧洲年轻人所处的数字环境，不仅在规模上存在差异，在本质上更是截然不同。中国年轻人主要使用微信（95.76%）、QQ（72.25%）、抖音（65.57%）和小红书（36.50%）。Zhao, Wang 和 Hu（2025）记录了一种被称为‘平台摇摆’的模式——即在同伴共鸣、自我管理需求和内容发现等驱动下，在不同平台之间自发地来回切换。这一现象挑战了人们通常认为数字身份是稳定不变的假设。)
European youth inhabit a different platform ecosystem. The Flash Eurobarometer Youth Survey 2024, covering 25,933 young EU citizens aged 16–30 across 27 member states, finds that social media platforms (42 percent) are the most commonly used sources of news among young Europeans (European Parliament 2025). The platform landscape is more fragmented than in China, with Instagram, TikTok, YouTube, and Snapchat competing for attention alongside nationally specific platforms.	(欧洲年轻人则身处一个截然不同的平台生态系统。涵盖欧盟27个成员国、共25,933名16至30岁欧盟青年公民的《2024年欧洲晴雨表青年调查》发现，社交媒体平台（42%）是欧洲年轻人最常用的新闻来源（European Parliament 2025）。与中国的平台生态相比，欧洲的平台格局更为碎片化，Instagram、TikTok、YouTube 和 Snapchat 相互争夺用户注意力，此外还存在一些具有国家特定属性的本土平台。)
Livingstone, Mascheroni, and Stoilova (2023), in a systematic evidence review of digital skills outcomes for young people aged 12–17, find a double-edged relationship: greater digital skills are positively associated with online opportunities and information benefits, but they also correlate with greater exposure to online risks. This finding has important implications for digital literacy education in both contexts: the goal cannot be simply to increase digital skills but to develop the critical judgment needed to navigate digital environments safely and productively.	(Livingstone、Mascheroni 和 Stoilova（2023）在一项针对12至17岁青少年数字技能成果的系统性证据综述中发现了一种‘双刃剑’关系：较高的数字技能虽然与更多的在线机会和信息收益呈正相关，但同时也与更高的网络风险暴露度相关。这一发现对两种语境下的数字素养教育都具有重要意义：教育的目标不能仅仅是提升数字技能，更要培养青少年所需的批判性判断力，从而让他们能够安全且富有成效地在数字环境中穿行。)
The mental health implications of intensive digital engagement are increasingly documented. Research on short-video platforms such as Douyin and TikTok reveals themes of anxiety, sleep disruption, digital addiction, and body image concerns across Chinese, American, and British contexts. The scale of concern in China is quantified by Zheng and colleagues’ (2025) meta-analysis: 10.3 percent internet addiction prevalence among adolescents, with rural youth disproportionately affected. China’s regulatory response — the mandatory „minor modes“ and screen time limits introduced in the Regulations on the Protection of Minors in Cyberspace (effective January 2024) — represents a more interventionist approach than the EU’s reliance on digital literacy education and platform self-regulation (State Council 2023).	密集数字参与的心理健康影响日益受到记录。对抖音和TikTok等短视频平台的研究在中国、美国和英国的背景下都揭示了焦虑、睡眠障碍、数字成瘾和身体形象担忧等主题。中国的担忧规模通过Zheng等人（2025）的荟萃分析得到量化：青少年互联网成瘾患病率为10.3%，农村青少年受影响不成比例。中国的监管应对——《未成年人网络保护条例》（2024年1月生效）中的强制"未成年人模式"和屏幕时间限制——代表了比欧盟依赖数字素养教育和平台自律更具干预性的方法（国务院 2023）。
The PISA 2022 findings add nuance to the screen time debate. Students who spent up to one hour per day on digital devices for learning scored 14 points higher in mathematics than those who did not, but students frequently distracted by others’ device use scored 15 points lower. Only 60 percent of students expressed confidence in their self-motivation for digital schoolwork (OECD 2023). These data suggest that the relationship between screen time and educational outcomes is not linear but mediated by the quality and purpose of engagement — a finding that argues for pedagogical guidance rather than simple time restrictions.	(PISA 2022 的发现为‘屏幕时间’的争论增添了更细腻的视角。数据显示，每天使用数字设备学习不超过一小时的学生，其数学成绩比完全不使用设备的学生高出14分；然而，那些经常因他人使用设备而分心的学生，数学成绩则低了15分。此外，只有60%的学生对自己进行数字化课业学习的自我激励能力表示有信心（OECD 2023）。这些数据表明，屏幕时间与教育成果之间的关系并非线性的，而是受到参与质量和目的所中介的——这一发现有力地论证了教育中需要的是教学法层面的引导，而非简单的时长限制。)
The platform ecosystems themselves differ in ways that shape digital literacy demands. Chinese platforms operate within a regulated ecosystem where content moderation, algorithmic recommendation, and data collection are governed by a combination of the Cyberspace Administration of China, platform-specific regulations, and the PIPL. European users navigate a more fragmented ecosystem where the Digital Services Act, the GDPR, and national regulations create a patchwork of protections. Students in both contexts need the critical capacity to understand how algorithmic recommendation shapes their information environment — a competence that neither DigComp 2.2 nor China’s digital literacy campaigns currently address with sufficient depth.	(平台生态系统本身的差异，也深刻塑造了对数字素养的不同要求。中国的平台运作于一个受严格监管的生态系统之中，其内容审核、算法推荐和数据收集行为，受到国家网信办、平台特定规章以及《个人信息保护法》（PIPL）的共同约束。相比之下，欧洲用户则身处一个更为碎片化的生态系统，依靠《数字服务法》（DSA）、《通用数据保护条例》（GDPR）以及各国的本土法规，拼凑出一套保护机制。在这两种语境下，学生都需要具备一种关键的批判性能力，即理解算法推荐是如何塑造其信息环境的——然而，无论是欧盟的 DigComp 2.2 框架，还是中国的数字素养提升行动，目前都未能对这一核心能力给予足够深入的探讨。)
'6. Digital Competence and Innovation Capability'	(6. 数字能力与创新潜能)
A critical question for both European and Chinese policymakers is whether digital literacy translates into innovation capability — the ability to create new solutions, not merely to consume digital content. Zhou and colleagues (2025), in a study of 1,334 students at 12 universities in Ningbo, China, find a strong positive correlation between digital literacy and innovation capability (beta = 0.76, p < 0.001), with cognitive emotion and responsibility literacy showing the strongest associations (r = 0.72–0.73). These findings suggest that digital literacy is not merely a consumption skill but a foundation for the creative and critical thinking that both economies need.	(对于欧洲和中国的决策者而言，一个关键问题在于：数字素养究竟能否转化为创新能力——即创造全新解决方案的能力，而不仅仅是消费数字内容。Zhou 及其同事（2025）针对中国宁波12所高校的1334名学生开展的一项研究发现，数字素养与创新能力之间存在显著的正相关关系（beta = 0.76, p < 0.001），其中认知情绪与责任素养的相关性最强（r = 0.72–0.73）。这些发现表明，数字素养绝不仅仅是一项消费技能，而是两大经济体当前所迫切需要的创造性思维和批判性思维的基石。)
Wu’s (2024) Digital Literacy Framework for Chinese College Students, structured as a progressive „Skills–Competencies–Awareness“ relationship, offers a complementary perspective. The framework identifies 15 descriptors validated through empirical research, reflecting a growing recognition in Chinese educational research that mere access to technology does not translate into innovation capacity. The framework’s emphasis on „awareness“ as the highest level of digital literacy — beyond skills and competences — resonates with the European DigComp framework’s attention to attitudes and values alongside knowledge and skills.	(Wu（2024）提出的中国大学生数字素养框架，构建了一种递进式的‘技能—能力—意识’关系，为此提供了一个互补的视角。该框架通过实证研究验证了15项描述符，反映出中国教育研究界日益达成的一个共识：仅仅拥有技术接入渠道，并不能转化为实际的创新能力。该框架将‘意识’置于数字素养的最高层级（超越技能与能力之上），这一强调与欧盟 DigComp 框架在知识与技能之外，同样高度重视态度与价值观的理念不谋而合。)
However, the EU Education and Training Monitor 2024 presents a more sobering picture for Europe. Only 42 percent of young Europeans report having had a good opportunity to learn about sustainability in school — a proxy for the kind of structured, interdisciplinary learning that connects digital competence to real-world challenges. While 84 percent of young people believe in the value of environmental change, only 30 percent act on sustainability daily. Over 40 percent of 13- and 14-year-olds lack basic digital skills (European Commission 2024). The gap between belief and action, and between access and competence, mirrors the broader digital native myth: being surrounded by technology does not automatically produce the ability — or the inclination — to use it productively.	(然而，《2024年欧盟教育与培训监测报告》为欧洲描绘了一幅更为严峻的图景。只有42%的欧洲年轻人表示在学校获得了学习可持续发展知识的良好机会——而这正是将数字能力与现实世界挑战相联系的那种结构化、跨学科学习的典型代表。尽管有84%的年轻人认同环境变革的价值，但每天真正践行可持续生活方式的仅有30%。此外，超过40%的13至14岁青少年缺乏基本的数字技能（European Commission 2024）。这种信念与行动之间的脱节，以及技术接入与真实能力之间的鸿沟，恰恰映射出了更广泛的‘数字原住民’神话的虚妄：仅仅被技术所包围，并不会自动催生出富有成效地使用技术的能力——甚至是意愿。)
Roh, Yoo, and Ok (2025), in a cross-national text mining analysis of national curriculum standards using the DigComp framework, find that „information and data literacy“ and „communication and collaboration“ are the most emphasized digital competences across compared nations, but digital literacy keywords have low centrality in curricula overall. This finding suggests that even when digital literacy is nominally part of the curriculum, it often remains peripheral to the core educational mission — a problem that will only intensify as AI becomes more central to both education and employment.	(Roh、Yoo 和 Ok（2025）利用 DigComp 框架对各国课程标准进行了一项跨国文本挖掘分析。他们发现，尽管‘信息与数据素养’和‘沟通与协作’是被比较国家最为强调的两种数字能力，但数字素养相关的关键词在整体课程中的中心度依然偏低。这一发现表明，即便数字素养在名义上已被纳入课程体系，它往往仍处于核心教育使命的边缘地带——而随着人工智能（AI）在教育与就业领域中的地位日益核心化，这一问题只会愈发严峻。)
'7. Implications for Curriculum Design'	(7. 对课程设计的启示)
The evidence reviewed in this article points to several implications for curriculum design in both European and Chinese universities.	(本文所综述的证据表明，这对欧洲和中国高校的课程设计都具有若干重要的启示。)
First, digital literacy education must be structured and explicit, not assumed. The digital native myth’s most pernicious legacy is the assumption that young people arrive at university already digitally competent. The empirical evidence — 55.6 percent basic digital skills in the EU, 12.1 percent of Chinese EFL teachers with poor AI literacy, over 40 percent of European young teenagers lacking basic digital skills — refutes this assumption decisively. Universities must provide systematic digital literacy education as part of the core curriculum, not as an optional supplement.	(第一，数字素养教育必须是结构化且显性的，绝不能想当然地认为学生‘自带技能’。‘数字原住民’神话留下的最有害的遗产，就是误以为年轻人进入大学时就已经具备了数字能力。然而，实证证据已经彻底推翻了这一假设：欧盟仅有55.6%的人具备基本数字技能，中国12.1%的英语（EFL）教师人工智能素养不足，且欧洲有超过40%的青少年缺乏基本数字技能。因此，高校必须将系统的数字素养教育纳入核心课程体系，而绝不能将其仅仅作为一种可有可无的补充。)
Second, AI literacy requires specific pedagogical attention. The EU AI Act‘s Article 4 mandate for AI literacy among AI system deployers applies directly to universities. The finding that German, British, and American students differ significantly in AI literacy (Hornberger et al. 2025) suggests that national educational contexts matter, and that generic AI literacy frameworks must be adapted to local conditions. China‘s decision to mandate AI education from September 2025 represents a more direct approach, but its effectiveness will depend on teacher competence — a concern highlighted by Pan and Wang’s (2025) finding that 57.6 percent of Chinese EFL teachers have poor or moderate AI literacy.	(第二，人工智能素养的培养需要专门的教学关注。欧盟《人工智能法案》第4条明确要求AI系统的部署者必须具备AI素养，这一规定直接适用于各大高校。Hornberger等人（2025）的研究发现，德国、英国和美国学生在AI素养上存在显著差异，这表明国家教育背景至关重要，通用的AI素养框架必须结合本土实际进行调整。中国决定自2025年9月起强制推行AI教育，代表了一种更为直接的推进路径；但其实际成效将高度取决于教师的胜任力——而Pan和Wang（2025）的研究指出，57.6%的中国英语（EFL）教师AI素养处于较差或中等水平，这一发现恰恰凸显了教师能力方面的隐忧。)
Third, digital literacy education must address risks as well as opportunities. Livingstone, Mascheroni, and Stoilova’s (2023) finding that greater digital skills correlate with greater exposure to online risks, and Zheng and colleagues’ (2025) documentation of 10.3 percent internet addiction prevalence among Chinese adolescents, underscore the need for digital literacy curricula that develop critical judgment, self-regulation, and awareness of digital wellbeing — competences that neither the DigComp framework nor China‘s infrastructure-focused approach currently emphasizes sufficiently.	第三，数字素养教育必须同时关注风险和机遇。Livingstone、Mascheroni和Stoilova（2023）关于更高数字技能与更大在线风险暴露相关的发现，以及Zheng等人（2025）关于中国青少年10.3%互联网成瘾患病率的记录，强调了数字素养课程需要培养批判性判断力、自我调节能力和数字健康意识——这些是DigComp框架和中国以基础设施为重点的方法目前都未充分强调的能力。
Fourth, the digital divide must be addressed as a socioeconomic and geographic challenge, not a generational one. Both the EU’s inter-state variation (55 percentage points between the Netherlands and Romania in basic digital skills) and China‘s urban-rural gap (nearly ten percentage points in internet penetration) demand targeted interventions that go beyond universal frameworks. Curriculum design must account for the reality that students arrive with vastly different levels of digital access, competence, and cultural capital.	(第四，必须将数字鸿沟视为一个社会经济和地理层面的挑战，而不仅仅是代际问题。无论是欧盟内部巨大的州际差异（荷兰与罗马尼亚在基本数字技能上相差55个百分点），还是中国的城乡鸿沟（互联网普及率相差近10个百分点），都要求我们必须采取针对性的干预措施，而不能仅仅依赖普适性的框架。课程设计必须正视这样一个现实：学生入学时所具备的数字接入条件、能力水平以及文化资本，存在着天壤之别。)
Fifth, digital literacy frameworks must evolve to address the algorithmic dimension of digital life. Current frameworks — including DigComp 2.2 — emphasize information literacy, communication, and content creation but give insufficient attention to algorithmic literacy: the capacity to understand how recommendation systems, content moderation algorithms, and AI-driven personalization shape the information environment. As students in both China and Europe spend increasing proportions of their time in algorithmically mediated environments, this competence becomes essential for informed citizenship.	(第五，数字素养框架必须与时俱进，以应对数字生活中无处不在的算法维度。目前的各类框架——包括DigComp 2.2——虽然高度重视信息素养、沟通能力和内容创作，却对‘算法素养’（algorithmic literacy）关注不足。所谓算法素养，是指理解推荐系统、内容审核算法以及AI驱动的个性化技术如何塑造信息环境的能力。随着中国和欧洲的学生越来越多地将时间花费在由算法中介的环境中，这种能力已成为培养知情公民的必备素养。)
Sixth, cross-cultural digital literacy education must resist the temptation to treat one system as the standard against which others are measured. Roh, Yoo, and Ok’s (2025) cross-national analysis of curriculum standards using DigComp as the analytical framework illustrates both the utility and the limitations of this approach: the framework provides a common vocabulary for comparison, but the low centrality of digital literacy keywords in curricula across all compared nations suggests that the challenge is not framework design but implementation — a challenge that both Europe and China share, despite their different institutional contexts.	(第六，跨文化的数字素养教育必须抵制将某一体系视为衡量其他体系标准的诱惑。Roh、Yoo 和 Ok（2025）以 DigComp 为分析框架对各国课程标准进行的跨国分析，恰好阐释了这种方法的效用与局限：该框架确实为跨国比较提供了一套通用的词汇，但所有被比较国家的课程中，数字素养相关关键词的中心度普遍偏低。这表明，真正的挑战并不在于框架的设计，而在于落地实施——尽管欧洲和中国有着不同的制度背景，但双方都共同面临着这一挑战。)
'8. Conclusion'	(8. 结论)
The digital native is a myth that has outlived its usefulness. Twenty-five years after Prensky’s original essay, the empirical evidence is unambiguous: growing up with technology does not produce digital competence. Digital literacy, like any other form of literacy, must be taught, practiced, and assessed. AI literacy adds a new dimension to this challenge, requiring not merely the ability to use AI tools but the critical capacity to evaluate their outputs, understand their limitations, and navigate their ethical implications.	(数字原住民是一个早已失去存在价值的迷思。在 Prensky 发表那篇开创性文章二十五年后，实证证据已经明确无误：在科技环境中长大，并不会自动带来数字能力。数字素养就像任何其他形式的素养一样，必须通过教学、实践和评估来习得。而人工智能（AI）素养则为这一挑战增添了新的维度，它要求的不仅仅是使用AI工具的能力，更包括评估其输出结果、理解其局限性以及驾驭其伦理影响的批判性能力。)
The comparison of European and Chinese approaches reveals complementary strengths and weaknesses. The EU’s framework-based approach — DigComp 2.2 with its 250+ competence examples, the Digital Education Action Plan, the AI Act’s literacy mandate — provides conceptual clarity and individual rights protection but struggles with implementation: 55.6 percent basic digital skills against an 80 percent target tells its own story. China‘s centralized, infrastructure-led approach achieves remarkable deployment speed — 99.9 percent school broadband, 293 million students on a single platform, mandatory AI education within two years of policy announcement — but faces challenges in teacher competence, urban-rural equity, and the gap between access and critical use.	(对欧洲和中国模式的比较，揭示了双方各自互补的优势与短板。欧盟采取的基于框架的路径——包括 DigComp 2.2 及其 250 多项能力范例、《数字教育行动计划》以及《人工智能法案》中的素养强制要求——虽然在概念界定和个人权利保护上清晰明确，却在落地实施上步履维艰：基本数字技能普及率仅为 55.6%，距离 80% 的目标相去甚远，这一数据本身就足以说明问题。而中国采取的集中式、以基础设施建设为导向的路径，则实现了惊人的部署速度——99.9% 的学校宽带接入率、单一平台覆盖 2.93 亿学生、政策发布两年内即强制推行 AI 教育——但也面临着教师能力不足、城乡公平性以及从‘拥有接入’到‘批判性使用’之间巨大鸿沟的挑战。)
The implications extend beyond education policy to questions of democratic citizenship and social cohesion. In Europe, where the Flash Eurobarometer Youth Survey 2024 finds that 42 percent of young people use social media as their primary news source (European Parliament 2025), the capacity to critically evaluate algorithmically curated information is not merely an educational desideratum but a democratic necessity. In China, where the state plays a more active role in content curation, digital literacy includes the capacity to navigate between domestic and global information ecosystems — a skill that Yang and colleagues’ (2025) comparative analysis of AI literacy policies suggests is receiving increasing policy attention.	(这些启示不仅限于教育政策层面，更延伸到了民主公民身份与社会凝聚力的核心议题。在欧洲，《2024年青年快闪欧洲民意调查》发现，42%的年轻人将社交媒体作为获取新闻的首要来源（欧洲议会 2025），因此，批判性地评估算法推送信息的能力，已不再仅仅是一个教育上的美好愿景，而是一项民主社会的刚需。而在中国，由于国家在内容策展（信息筛选与分发）中扮演着更为积极的角色，其数字素养的内涵还包括在国内与全球信息生态之间自如穿梭的能力——正如 Yang 及其同事（2025）对 AI 素养政策的比较分析所表明的那样，这一技能正日益受到政策层面的重视。)
Neither approach has solved the fundamental problem that the digital native myth was supposed to address: how to prepare young people for a world in which digital technology is ubiquitous but digital competence is unevenly distributed. We argue that the most promising path forward combines European rigor in competence definition and assessment with Chinese speed in deployment and scaling — a synthesis that is easier to propose than to achieve, but that both systems are, in their different ways, beginning to explore. The companion chapters in this anthology on AI ethics (Woesler, this volume), data protection (Woesler, this volume), and the university of the future (Woesler, this volume) address the institutional, regulatory, and pedagogical dimensions of this challenge.	(“无论是哪种模式，都尚未解决数字原住民迷思最初试图回应的那个根本问题：如何让年轻人为这样一个世界做好准备——在这个世界里，数字技术无处不在，但数字能力的分布却极不均衡。我们认为，最有希望的前进道路，是将欧洲在能力界定与评估上的严谨性，与中国在部署和规模化上的速度相结合。这种融合虽然说起来容易做起来难，但两大体系正以各自不同的方式开始对此进行探索。本选集中关于人工智能伦理（Woesler, 本书）、数据保护（Woesler, 本书）以及未来大学（Woesler, 本书）的配套章节，将分别从制度、监管和教学维度来探讨这一挑战。)
'Acknowledgments'	(致谢)
This research was conducted within the framework of the Jean Monnet Centre of Excellence „EUSC-DEC“ (EU Grant 101126782, 2023–2026). The author thanks the members of Research Group 4 (Cross-Cultural Perspectives on Digital Education) for their contributions to the comparative analysis.	(本研究是在让·莫内卓越中心‘EUSC-DEC’的框架下开展的（欧盟资助项目 101126782，2023–2026年）。作者感谢第四研究组（数字教育的跨文化视角）成员为本次比较分析所作出的贡献。)
'References'	(参考文献)
Bennett, S., Maton, K. & Kervin, L. (2008). The ‘digital natives’ debate: A critical review of the evidence. British Journal of Educational Technology, 39(5), 775–786. DOI: 10.1111/j.1467-8535.2007.00793.x	(Bennett, S., Maton, K. 与 Kervin, L. (2008). “数字原住民”之争：对现有证据的批判性综述. 《英国教育技术期刊》, 39(5), 775–786. DOI: 10.1111/j.1467-8535.2007.00793.x)
Central Cyberspace Affairs Commission, Ministry of Education, MIIT & MOHRSS. (2025). 2025 Plan for Enhancing National Digital Literacy and Skills. Beijing.	(中央网信办、教育部、工业和信息化部、人力资源社会保障部. (2025). 《2025年提升全民数字素养与技能工作要点》. 北京.)
China Internet Network Information Center (CNNIC). (2025). The 55th Statistical Report on China’s Internet Development. Beijing: CNNIC.	(中国互联网络信息中心. (2025). 《第55次中国互联网络发展状况统计报告》. 北京: 中国互联网络信息中心.)
Eurofound. (2025). Narrowing the digital divide: Economic and social convergence in Europe’s digital transformation. Publications Office of the European Union, Luxembourg.	(欧洲改善生活和工作条件基金会. (2025). 《缩小数字鸿沟：欧洲数字化转型中的经济与社会融合》. 卢森堡: 欧盟出版办公室.)
European Commission. (2020). Digital Education Action Plan 2021–2027: Resetting education and training for the digital age. COM(2020) 624 final, 30 September 2020.	(欧盟委员会. (2020). 《数字教育行动计划（2021–2027年）：重塑数字时代的教育与培训》. COM(2020) 624 最终版, 2020年9月30日.)
European Commission. (2024). Education and Training Monitor 2024. Publications Office of the European Union.	(欧盟委员会. (2024). 《2024年教育与培训监测报告》. 欧盟出版办公室.)
European Commission. (2025). State of the Digital Decade 2025. COM(2025) 262 final.	(欧盟委员会. (2025). 《2025年数字十年状况报告》. COM(2025) 262 最终版.)
European Parliament. (2025). Flash Eurobarometer 556: Youth Survey 2024. February 2025.	(这是一条关于欧盟青年调查的官方参考文献，按照中文学术规范，可以翻译为： 欧洲议会. (2025). 《快闪欧洲晴雨表556：2024年青年调查》. 2025年2月.)
European Parliament and Council. (2024). Regulation (EU) 2024/1689 of 13 June 2024 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Official Journal of the European Union, L series.	(欧洲议会与欧盟理事会. (2024). 2024年6月13日关于制定人工智能协调规则（《人工智能法案》）的(EU) 2024/1689号条例. 《欧盟官方公报》，L系列.)
Hilliard, A., Gulley, A., Kazim, E. & Koshiyama, A. S. (2026). Artificial intelligence policy worldwide: a comparative analysis. Royal Society Open Science, 13(2), 242234. DOI: 10.1098/rsos.242234	(Hilliard, A., Gulley, A., Kazim, E. 与 Koshiyama, A. S. (2026). 全球人工智能政策：一项比较分析. 《英国皇家学会开放科学》, 13(2), 242234. DOI: 10.1098/rsos.242234)
Hornberger, M., Bewersdorff, A., Schiff, D. S. & Nerdel, C. (2025). A multinational assessment of AI literacy among university students in Germany, the UK, and the US. Computers in Human Behavior: Artificial Humans, 4, 100132. DOI: 10.1016/j.chbah.2025.100132	(Hornberger, M., Bewersdorff, A., Schiff, D. S. 与 Nerdel, C. (2025). 德国、英国和美国大学生人工智能素养的多国评估. 《人类行为中的计算机：人工人类》, 4, 100132. DOI: 10.1016/j.chbah.2025.100132)
Livingstone, S., Mascheroni, G. & Stoilova, M. (2023). The outcomes of gaining digital skills for young people’s lives and wellbeing: A systematic evidence review. New Media and Society, 25(5), 1176–1202. DOI: 10.1177/14614448211043189	(Livingstone, S., Mascheroni, G. 与 Stoilova, M. (2023). 获取数字技能对年轻人生活与福祉的影响：一项系统性证据综述. 《新媒体与社会》, 25(5), 1176–1202. DOI: 10.1177/14614448211043189)
Long, D. & Magerko, B. (2020). What is AI Literacy? Competencies and Design Considerations. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–16). ACM. DOI: 10.1145/3313831.3376727	(Long, D. 与 Magerko, B. (2020). 什么是人工智能素养？能力与设计考量. 收录于：《2020年人机交互系统CHI大会论文集》（第1–16页）. ACM. DOI: 10.1145/3313831.3376727)
Ma, C. (2025). China‘s Achievements in Digital Education in the Wake of Education Informatization 2.0 Action Plan. Science Insights Education Frontiers, 27(1), 4435–4451. DOI: 10.15354/sief.25.re488	(Ma, C. (2025). 教育信息化2.0行动计划背景下中国数字教育的成就. 《科学洞察：教育前沿》, 27(1), 4435–4451. DOI: 10.15354/sief.25.re488)
Mertala, P., Lopez-Pernas, S., Vartiainen, H., Saqr, M. & Tedre, M. (2024). Digital natives in the scientific literature: A topic modeling approach. Computers in Human Behavior, 152, 108076. DOI: 10.1016/j.chb.2023.108076	(Mertala, P., Lopez-Pernas, S., Vartiainen, H., Saqr, M. 与 Tedre, M. (2024). 科学文献中的数字原住民：一种主题建模方法. 《人类行为中的计算机》, 152, 108076. DOI: 10.1016/j.chb.2023.108076)
OECD. (2023). PISA 2022 Results (Volume I): The State of Learning and Equity in Education. PISA, OECD Publishing, Paris. DOI: 10.1787/53f23881-en	(经济合作与发展组织. (2023). 《2022年国际学生评估项目结果（第一卷）：学习与教育公平状况》. 经合组织出版局. DOI: 10.1787/53f23881-en)
Pan, Z. & Wang, Y. (2025). From Technology-Challenged Teachers to Empowered Digitalized Citizens: Exploring the Profiles and Antecedents of Teacher AI Literacy in the Chinese EFL Context. European Journal of Education, 60(1), e70020. DOI: 10.1111/ejed.70020	(Pan, Z. 与 Wang, Y. (2025). 从受技术挑战的教师到被赋能的数字化公民：探索中国EFL语境下教师AI素养的概况与前因. 《欧洲教育杂志》, 60(1), e70020. DOI: 10.1111/ejed.70020)
Prensky, M. (2001). Digital Natives, Digital Immigrants, Part 1. On the Horizon, 9(5), 1–6. DOI: 10.1108/10748120110424816	(Prensky, M. (2001). 数字原住民，数字移民（第一部分）. 《地平线》, 9(5), 1–6. DOI: 10.1108/10748120110424816)
Reid, L., Button, D. & Brommeyer, M. (2023). Challenging the Myth of the Digital Native: A Narrative Review. Nursing Reports, 13(2), 573–600. DOI: 10.3390/nursrep13020052	(Reid, L., Button, D. 与 Brommeyer, M. (2023). 挑战数字原住民的神话：一项叙事综述. 《护理报告》, 13(2), 573–600. DOI: 10.3390/nursrep13020052)
Roh, D., Yoo, J. & Ok, H. (2025). Mapping digital literacy in language education: A comparative analysis of national curriculum standards using text as data approach. Education and Information Technologies, 30, 6287–6313. DOI: 10.1007/s10639-024-13056-5	(Roh, D., Yoo, J. 与 Ok, H. (2025). 绘制语言教育中的数字素养图谱：基于“文本即数据”方法的国家课程标准比较分析. 《教育与信息技术》, 30, 6287–6313. DOI: 10.1007/s10639-024-13056-5)
State Council of the People’s Republic of China. (2023). Regulations on the Protection of Minors in Cyberspace. Effective 1 January 2024.	(中华人民共和国国务院. (2023). 《未成年人网络保护条例》. 2024年1月1日起施行.)
Vuorikari, R., Kluzer, S. & Punie, Y. (2022). DigComp 2.2: The Digital Competence Framework for Citizens — With new examples of knowledge, skills and attitudes. EUR 31006 EN, Publications Office of the European Union, Luxembourg. DOI: 10.2760/115376	(Vuorikari, R., Kluzer, S. 与 Punie, Y. (2022). DigComp 2.2：公民数字能力框架——知识、技能与态度的新示例. EUR 31006 EN, 欧盟出版局, 卢森堡. DOI: 10.2760/115376)
Wang, C. & d’Haenens, L. (2025). Report-Based Interpretation of 2024 Digital Literacy and Skills in China and the EU. In: Communications in Computer and Information Science (CCIS, vol. 2537). Springer.	(Wang, C. 与 d'Haenens, L. (2025). 基于报告的《2024年中国与欧盟数字素养与技能》解读. 收录于：《计算机与信息科学通讯》（CCIS, 第2537卷）. Springer.)
Wu, D. (2024). Exploring digital literacy in the era of digital civilization: A framework for college students in China. Information Services and Use, 44(2), 69–91. DOI: 10.3233/ISU-230199	(Wu, D. (2024). 探索数字文明时代的数字素养：中国大学生的框架. 《信息服务与利用》, 44(2), 69–91. DOI: 10.3233/ISU-230199)
Yan, S. & Yang, Y. (2021). Education Informatization 2.0 in China: Motivation, Framework, and Vision. ECNU Review of Education, 4(2), 291–302. DOI: 10.1177/2096531120944929	(Yan, S. 与 Yang, Y. (2021). 中国教育信息化2.0：动力、框架与愿景. 《华东师范大学教育评论（英文）》, 4(2), 291–302. DOI: 10.1177/2096531120944929)
Yang, H., Xu, J., Zeng, X. & Gu, X. (2025). Comparing AI literacy policies in the European Union, the United States, India, and China. Telecommunications Policy. DOI: 10.1016/j.telpol.2025.102939	(Yang, H., Xu, J., Zeng, X. 与 Gu, X. (2025). 比较欧盟、美国、印度和中国的AI素养政策. 《电信政策》. DOI: 10.1016/j.telpol.2025.102939)
Zhang, S., Ganapathy Prasad, P. & Schroeder, N. L. (2025). Learning About AI: A Systematic Review of Reviews on AI Literacy. Journal of Educational Computing Research. DOI: 10.1177/07356331251342081	(Zhang, S., Ganapathy Prasad, P. 与 Schroeder, N. L. (2025). 学习关于AI的知识：关于AI素养的系统性综述. 《教育计算研究杂志》. DOI: 10.1177/07356331251342081)
Zhao, H., Wang, J. & Hu, X. (2025). „A Wandering Existence“: Social Media Practices of Chinese Youth in the Context of Platform-Swinging. Social Media + Society, 11(1). DOI: 10.1177/20563051251315265	(Zhao, H., Wang, J. 与 Hu, X. (2025). “流浪的存在”：平台摇摆背景下中国青年的社交媒体实践. 《社交媒体与社会》, 11(1). DOI: 10.1177/20563051251315265)
Zheng, M.-R. et al. (2025). Prevalence of internet addiction among Chinese adolescents: A comprehensive meta-analysis of 164 epidemiological studies. Asian Journal of Psychiatry, 105, 104458. DOI: 10.1016/j.ajp.2025.104458	(Zheng, M.-R. 等. (2025). 中国青少年网络成瘾的流行率：一项涵盖164项流行病学研究的综合荟萃分析. 《亚洲精神病学杂志》, 105, 104458. DOI: 10.1016/j.ajp.2025.104458)
Zhou, Y., Sun, X., Zhu, Y., Feng, Z., Sun, Q. & Zhong, X. (2025). The impact of digital literacy on university students’ innovation capability: evidence from Ningbo, China. Frontiers in Psychology, 16, 1548817. DOI: 10.3389/fpsyg.2025.1548817	(Zhou, Y., Sun, X., Zhu, Y., Feng, Z., Sun, Q. 与 Zhong, X. (2025). 数字素养对大学生创新能力的影响：来自中国宁波的证据. 《心理学前沿》, 16, 1548817. DOI: 10.3389/fpsyg.2025.1548817)
'Part IV: Future Directions'	(第四部分：未来展望)
	'