Rethinking Higher Education/Chapter 9

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Digital Natives in China and Europe: Comparative Digital Literacy, AI Attitudes, and Educational Implications

Martin Woesler

Hunan Normal University

Abstract

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.

Keywords: digital natives, digital literacy, AI literacy, DigComp 2.2, China digital education, European digital skills, digital divide, Gen Z, digital competence, comparative education

1. Introduction

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.

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.

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.

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.

2. Frameworks: DigComp 2.2 versus China‘s Digital Literacy Initiatives

2.1 The European Approach: 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).

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.

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).

2.2 The Chinese Approach: Centralized Digital Literacy Campaigns

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).

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).

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.

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.

3. AI Literacy Across Borders

3.1 Policy Landscape

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.

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.

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.

3.2 Empirical Findings

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.

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.

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.

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.

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).

4. The Digital Divide

4.1 China: The Urban-Rural Gap

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).

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.

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.

4.2 Europe: Socioeconomic and Inter-State Variation

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).

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.

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.

5. Screen Time, Digital Habits, and Platform Ecosystems

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.

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.

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.

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).

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.

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.

6. Digital Competence and Innovation Capability

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.

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.

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.

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.

7. Implications for Curriculum Design

The evidence reviewed in this article points to several implications for curriculum design in both European and Chinese universities.

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.

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.

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.

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.

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.

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.

8. Conclusion

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.

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.

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.

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.

Acknowledgments

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.

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Part IV: Future Directions