Rethinking Higher Education/Chapter 10

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University of the Future: AI-Enhanced Higher Education Between European Humanism and Chinese Innovation

Martin Woesler

Hunan Normal University

Abstract

Higher education stands at a crossroads. The convergence of artificial intelligence, post-pandemic hybrid learning, and smart campus technologies is transforming universities from knowledge-transmission institutions into adaptive learning ecosystems. This article examines how European and Chinese universities are responding to this transformation, drawing on institutional data, policy analysis, and recent empirical research. We document China‘s state-led approach — exemplified by the UNESCO-award-winning Smart Education Platform serving 293 million learners — and contrast it with the EU’s decentralized model operating through 65 European Universities alliances encompassing over 570 institutions across 35 countries. Through a systematic comparison of AI adoption policies, hybrid learning models, smart campus infrastructure, and generative AI integration, we argue that neither the Chinese emphasis on speed and scale nor the European emphasis on democratic governance and faculty autonomy is sufficient alone. A synthesis — combining China’s capacity for rapid deployment with Europe’s commitment to humanistic values and institutional self-governance — offers the most promising model for the university of the future.

Keywords: university transformation, AI in higher education, smart campus, hybrid learning, EU-China comparison, generative AI policy, digital education

1. Introduction

On 13 March 2024, the Jean Monnet Centre of Excellence at Hunan Normal University inaugurated its lecture series „Digitalization in China and Europe“ with a presentation entitled „University of the Future.“ The lecture, attended by more than 200 participants, explored how artificial intelligence, virtual reality, and hybrid pedagogies are reshaping the institution that has served as the primary vehicle for advanced knowledge transmission since the founding of the University of Bologna in 1088.

The university faces a paradox. As an institution, it is remarkably stable — its basic organizational form (departments, faculties, lectures, examinations, degrees) has changed less in a millennium than almost any other social institution. Yet the environment in which it operates has changed beyond recognition within a single decade. Students entering university today will graduate into a labour market in which an estimated 44 percent of workers will need to change their skill profile within five years (WEF 2023). They will use AI tools daily — 50 percent already use them at least weekly inside and outside the classroom (EDUCAUSE 2025). They expect hybrid learning options — 86 percent of residential undergraduate students prefer some combination of in-person and online courses (Rize Education 2025). And they will work in an economy where the institutions that train them — corporations, online platforms, bootcamps — increasingly compete with universities for the credential market.

This article examines how European and Chinese universities are navigating this transformation. It builds on the companion chapters in this volume that address specific dimensions of educational digitalization — AI in language learning, alternative learning forms, data protection, AI ethics, and sustainability — by addressing the overarching institutional question: what kind of university do we need for the AI age, and how are the world’s two largest educational systems working toward it?

2. The Smart Campus: Infrastructure for the Future

2.1 China‘s Smart Education Platform

China‘s approach to university transformation is exemplified by the National Smart Education Platform (国家智慧教育平台), launched in March 2022 and recognized with the UNESCO King Hamad Bin Isa Al-Khalifa Prize for the Use of ICT in Education in 2023. By the end of 2023, the platform linked 519,000 educational institutions, 18.8 million teachers, and 293 million learners, with over 100 million registered users from more than 200 countries and 36.7 billion visits (UNESCO 2023).

The platform integrates four sub-platforms — for basic education, vocational education, higher education, and teacher training — into a unified digital infrastructure. For higher education specifically, it provides access to more than 27,000 online courses, virtual simulation experiments, and inter-institutional collaborative learning spaces. The scale is unprecedented: no other country operates a single educational platform serving nearly 300 million users.

The Smart Education Platform reflects a distinctly Chinese approach to educational technology — centralized, state-funded, rapidly deployed, and integrated with broader national strategies. The 14th Five-Year Plan’s education digitalization strategy positions digital transformation as essential to China‘s goal of becoming a global education leader by 2035. Provincial governments have supplemented the national platform with regional initiatives; Hunan Province, for example, has invested in smart classroom infrastructure across its public universities, including Hunan Normal University.

2.2 European Smart Campus Initiatives

The European approach to smart campus development is characteristically decentralized. Rather than a single national or continental platform, European universities pursue smart campus initiatives individually or through collaborative networks. The European Universities Initiative, funded through Erasmus+, has established 65 alliances encompassing over 570 higher education institutions across 35 countries (European Commission 2025). These alliances facilitate shared digital infrastructure, joint online programmes, and collaborative research, but each institution retains autonomy over its technological choices and pedagogical approaches.

Individual European universities have developed notable smart campus projects. The University of Edinburgh’s Smart Data Campus integrates IoT sensors, learning analytics, and predictive systems. TU Delft’s Smart Campus initiative uses digital twin technology to optimize building management and learning environments. The European Commission’s Digital Education Action Plan (2021-2027) provides policy guidance and funding incentives but, unlike China‘s approach, does not mandate specific technological solutions.

The EU approach offers advantages in institutional diversity and faculty autonomy — universities can adapt technologies to their specific contexts and pedagogical traditions. However, it also produces fragmentation, duplication of effort, and slower adoption compared to China‘s centralized model.

2.3 Smart Classroom Design and Learning Outcomes

Research on smart classroom effectiveness provides empirical grounding for institutional investment decisions. A study of 421 Chinese undergraduates from Project 985, Project 211, and local universities found that „psychological enjoyment triggered by immersive smart-classroom infrastructure is an important source of perceived academic improvement“ and that „teacher-directed scaffolding with AI amplifies this effect“ (Zhang, C. 2026). The study suggests that smart classroom technology enhances learning not primarily through information delivery but through the creation of engaging, immersive environments that increase student motivation and attention.

A systematic review of smart campus technologies identified the integration of IoT, AI, cloud computing, and Big Data analytics as the key elements of intelligent campus infrastructure, with personalized dashboards, AI chatbots, and predictive analytics emerging as the most promising applications (Elbertsen, Kok, & Salimi 2025). However, the review also noted that most smart campus implementations remain at the level of facilities management rather than pedagogical transformation — optimizing building energy use rather than fundamentally changing how teaching and learning occur.

3. AI-Personalized Learning

3.1 The Promise and the Reality

The aspiration for AI-personalized learning — instruction tailored to each student’s pace, style, prior knowledge, and learning objectives — represents one of the most compelling visions for the university of the future. China has embraced this vision with particular enthusiasm. The Smart Education Platform incorporates adaptive learning algorithms that recommend courses, adjust difficulty levels, and provide individualized feedback based on student performance data.

However, empirical research reveals a more nuanced picture. A thematic analysis of 48 Chinese undergraduates’ perceptions of AI-personalized learning found that students valued efficiency gains — faster access to relevant materials, more targeted practice exercises, immediate feedback — but expressed concerns about over-reliance on AI recommendations and the potential loss of critical thinking and autonomous learning capacity (Wang et al. 2024). Students described a tension between the convenience of AI-guided learning paths and their desire to explore, struggle, and discover independently.

This tension reflects a deeper philosophical question about education. If the purpose of university education is merely to transmit predetermined knowledge and skills as efficiently as possible, then AI personalization represents an unequivocal improvement. If, however, education is also about developing intellectual autonomy, tolerance for ambiguity, the capacity for critical thinking, and the ability to formulate questions rather than merely answer them, then excessive personalization may be counterproductive — it may optimize the measurable while undermining the essential.

3.2 Personalization in Practice: Three Models

Three distinct models of AI-personalized learning have emerged in current practice:

The recommendation model uses AI to suggest courses, readings, and learning activities based on student performance data and preferences. This is the approach of most MOOC platforms and of China‘s Smart Education Platform. It treats education as analogous to content consumption — students are „recommended“ educational experiences in the same way that Netflix recommends films. The model is efficient for self-directed learning but raises concerns about filter bubbles (students being channelled toward comfortable material rather than challenging content) and about reducing education to consumption.

The adaptive model adjusts the difficulty, pace, and sequence of learning materials in real time based on student responses. Adaptive learning platforms such as Knewton, DreamBox, and China‘s Squirrel AI have demonstrated measurable improvements in standardized test performance. However, critics argue that adaptive learning optimizes for measurable outcomes while potentially neglecting the unmeasurable — curiosity, creativity, the willingness to engage with difficulty rather than routing around it.

The tutoring model uses AI chatbots or virtual tutors to provide individualized instruction, answering questions, explaining concepts, and guiding problem-solving. Studies of AI tutoring in mathematics and science have shown promising results, particularly for students who might not otherwise have access to individual tutoring. The AI-assisted language learning study in this volume found that AI tutoring provides psychological benefits — particularly the „no fear of making mistakes“ effect — that supplement rather than replace human instruction (Woesler, this volume).

Each model has legitimate applications, but none constitutes „personalized education“ in the deeper sense envisioned by educational philosophers. Genuine personalization would involve not merely adapting content delivery to individual learning patterns but supporting each student’s development as a unique intellectual and moral agent — a goal that remains beyond current AI capabilities.

3.3 European Caution and Chinese Enthusiasm

European institutions have generally approached AI-personalized learning with greater caution than their Chinese counterparts. This reflects several factors: stronger data protection frameworks (the GDPR imposes strict requirements on the processing of student data for personalization purposes; see the companion chapter on data protection, Woesler, this volume), a tradition of faculty autonomy in pedagogical decisions, and a humanistic educational philosophy that emphasizes Bildung — the formation of the whole person — over skill acquisition.

Chinese institutions, operating within a more permissive data environment and encouraged by state policy, have moved more aggressively toward AI personalization. The Ministry of Education’s emphasis on „smart education“ (智慧教育) and the integration of AI into the National Smart Education Platform create institutional incentives for adoption. However, the empirical evidence on effectiveness remains mixed, and Chinese researchers are increasingly calling for critical evaluation alongside enthusiasm.

4. Hybrid Learning as Post-Pandemic Standard

4.1 The Durability of Hybrid Models

The COVID-19 pandemic forced universities worldwide to adopt online learning at scale. The question in 2024-2026 is not whether to continue some form of online delivery but how to optimize the blend. Data suggest that hybrid learning is not a temporary accommodation but a permanent feature of higher education. Over 68 percent of universities have increased their online offerings since 2020, with nearly half planning to make online programmes a central strategic pillar (EDUCAUSE 2025). A survey of residential undergraduate students found that 86 percent prefer a hybrid approach with one to four online courses per semester (Rize Education 2025).

Research using Structural Equation Modeling and Deep Neural Network analysis confirmed that hybrid learning represents a sustainable post-pandemic model, though its effectiveness depends heavily on institutional support infrastructure — including reliable technology, trained faculty, appropriate assessment design, and student support services (Yaqin et al. 2025). The study identified institutional support as a stronger predictor of hybrid learning success than either technology quality or student digital competence.

4.2 Chinese Blended Learning Adoption

In China, a study of factors influencing behavioural intention to adopt blended learning among university students in the post-pandemic era found broad acceptance alongside specific concerns (Yu et al. 2023). Students valued the flexibility of blended learning — the ability to review recorded lectures, access materials asynchronously, and study at their own pace — but expressed concern about reduced social interaction and the difficulty of maintaining self-discipline in online environments.

These concerns resonate with longstanding critiques of online education. The social dimension of university learning — the informal conversations in corridors, the debates in seminars, the collaborative projects that build professional networks — cannot be fully replicated in digital environments. Chinese universities have responded by developing hybrid formats that combine online content delivery with intensive face-to-face workshops, collaborative projects, and mentoring sessions. Hunan Normal University’s own experience with the Jean Monnet lecture series illustrates this approach: lectures were delivered to audiences of 150-200 participants with simultaneous online streaming, followed by in-person discussion sessions with smaller groups.

A meta-analysis covering 37 blended learning studies from 2000 to 2024 found a positive upper-medium effect on learning outcomes (SMD = 0.698), with the optimal online proportion at approximately 50 percent. This finding is significant for curriculum design: it suggests that the most effective hybrid model is not primarily online with occasional face-to-face sessions, nor primarily face-to-face with supplementary online materials, but a roughly equal blend of both. The pedagogical implication is that hybrid learning should be designed as an integrated experience — not simply „the same course delivered through two channels“ — with specific learning activities assigned to the mode best suited to their educational purpose: information transmission online, social learning and deep discussion in person.

5. Generative AI in the Curriculum

5.1 The Adoption Wave

Generative AI has moved from novelty to ubiquity in higher education with remarkable speed. The EDUCAUSE 2025 survey found that 57 percent of higher education institutions now prioritize AI integration, up from 49 percent in 2024. A global analysis of institutional adoption policies found a spectrum of responses, from outright bans (increasingly rare) to mandatory integration (still uncommon) to the emerging mainstream of „regulated integration“ — permitting AI use under specified conditions with appropriate attribution (Computers and Education: Artificial Intelligence 2025).

In the United States, 63 percent of high-research-activity universities encourage generative AI use, while 27 percent discourage or limit it. In Europe, policies vary dramatically by institution and country, reflecting the decentralized governance model. In China, the picture is complicated by the restriction of access to global AI tools (ChatGPT, Claude, Gemini, Copilot) and the promotion of domestic alternatives (Ernie Bot, Tongyi Qianwen, DeepSeek).

5.2 Chinese Students Navigating Global and Domestic AI

A study of how Chinese university students navigate global and domestic generative AI found that students develop complex strategies to access restricted tools alongside officially permitted domestic alternatives (Xie et al. 2025). Students reported using VPNs, foreign accounts, and peer networks to access ChatGPT and other restricted platforms, while also using domestic tools for assignments that would be reviewed by instructors. This dual-use pattern reflects a tension in China‘s AI education strategy between promoting AI adoption and controlling the AI ecosystem.

5.3 Academic Integrity Challenges

The integration of generative AI raises pressing questions about academic integrity. A study found that 58 percent of students admitted to using AI to complete assignments dishonestly, while 65 percent acknowledged that plagiarism is a concern with unmodified AI content (ICAI 2024; Frontiers in Artificial Intelligence 2024). Chinese universities have responded with quantitative measures — Tianjin University of Science and Technology (天津科技大学), for example, mandated in 2024 that AI-generated content in undergraduate theses must not exceed 40 percent. The Ministry of Education has prohibited students from submitting AI-generated content as their own academic work.

These responses illustrate the difficulty of regulating AI use in education. A 40 percent threshold is measurable in principle but difficult to enforce in practice — how does one determine the exact proportion of AI-generated content in a text that has been substantially rewritten by its human author? The deeper challenge is not detecting AI use but developing assessment methods that value the distinctly human contributions — original thinking, critical analysis, creative synthesis — that AI cannot replicate. This is where the philosophical traditions of European Bildung and Chinese 修身 (xiushen, self-cultivation) converge: both emphasize the development of the whole person, not merely the production of correct answers.

5.4 Institutional Policy Landscapes

The global picture of GenAI policy in higher education is characterized by rapid evolution and significant institutional variation. In China, Fudan University became one of the first major Chinese universities to issue comprehensive AI guidelines in January 2026, addressing both student and teacher use. The guidelines permit AI for research assistance and learning support but prohibit its use in formal assessments without explicit instructor authorization. Other Chinese universities have followed with varying degrees of permissiveness, though the overarching direction — shaped by the Ministry of Education — is toward regulated integration rather than prohibition.

In Europe, the OECD Digital Education Outlook (2023) found that none of the 18 countries or jurisdictions surveyed had issued specific regulations on generative AI in education, though nine had published non-binding guidance. By 2025, this picture had evolved significantly, with several countries (including the Netherlands, Finland, and Ireland) issuing national guidelines and individual universities developing institution-specific policies. However, the pace of policy development continues to lag behind the pace of AI adoption by students and faculty.

The policy landscape in higher education increasingly shows a readiness gap. The EDUCAUSE 2024 AI Landscape Study found that 80 percent of faculty and staff use AI tools, yet fewer than one in four are aware of their institution’s formal AI policy. This gap between practice and governance represents one of the most urgent challenges for the university of the future — institutional policies must not only exist but be communicated, understood, and perceived as relevant by the academic community.

6. The Role of Faculty in University Transformation

6.1 Faculty as Agents of Change

The transformation of the university cannot be imposed from above — it must be led, or at least co-led, by faculty. A critical reflection on current AI trends in higher education argued that transformation must be „faculty-led rather than technology-driven“ (Ruano-Borbalan 2025). Faculty members are the carriers of disciplinary knowledge, the designers of curricula, and the primary interface between students and the institution. Their engagement — or resistance — determines whether technological innovation translates into genuine pedagogical improvement.

Yet faculty face significant challenges. Many academics trained before the AI era lack personal experience with the tools their students use daily. The incentive structures of academic careers — which reward research publications over teaching innovation in most systems — provide limited motivation for investing time in pedagogical transformation. And the speed of technological change creates uncertainty about which innovations are worth adopting and which are ephemeral trends.

A study of regulations, technology policies, and university attitudes to AI in China, based on policy analysis and 33 faculty interviews at Chinese research universities, found that faculty generally view AI as enhancing personalization and research productivity but express concerns about academic integrity, algorithmic bias, and over-reliance on automated systems (Higher Education Quarterly 2025). These concerns mirror those of European faculty, suggesting that the faculty experience of AI-driven transformation transcends cultural and systemic differences.

6.2 Professional Development for the AI Era

Both European and Chinese universities face the challenge of faculty professional development for AI-augmented teaching. China‘s approach has been characteristically large-scale: the national teacher training platform, integrated into the Smart Education Platform, has trained over 10 million teachers in digital competencies. However, training for AI integration in university teaching specifically remains uneven, with elite research universities moving faster than regional teaching-oriented institutions.

European approaches to faculty development vary by institution but are increasingly supported by EU funding. The Erasmus+ programme’s 2026 call allocates EUR 145.6 million for cooperation partnerships and forward-looking projects, with digital transformation in education as a priority theme. The European University Alliances have also created inter-institutional faculty exchange programmes focused on digital pedagogy.

The most effective faculty development programmes share several characteristics: they are discipline-specific rather than generic, they involve hands-on experimentation rather than passive instruction, they create peer communities of practice rather than one-off workshops, and they explicitly address the ethical and pedagogical questions raised by AI rather than focusing solely on technical skills.

7. EU vs. China: Two Visions of the Future University

7.1 The Chinese Model: State-Led Transformation

China‘s vision of the future university is characterized by centralized planning, massive digital infrastructure, rapid deployment, and tight integration with national development goals. The State Council’s „Education Modernization 2035“ plan positions education as a strategic national priority and explicitly links educational transformation with technological innovation and economic competitiveness. The „New Generation Artificial Intelligence Development Plan“ (2017) further positions AI competence as a national strategic asset, with implications for every level of education.

The practical manifestation of this vision is impressive in scale. From September 2025, AI education became compulsory in all primary and secondary schools — primary schools focus on AI literacy and exposure, junior high schools on logic and critical thinking, and senior high schools on applied innovation and algorithm design. At the university level, China‘s „Double First-Class“ university initiative (双一流) has created a tier of elite institutions that serve as laboratories for AI-enhanced pedagogy. Peking University, Tsinghua University, and Zhejiang University, among others, have established AI-specific research centres and integrated AI tools into teaching across disciplines.

The advantages of this model are evident in scale and speed. No other educational system can deploy a platform serving 293 million learners within two years of launch, or mandate AI education in all primary and secondary schools within a single policy cycle. The near-universal broadband connectivity rate in Chinese schools (99.9 percent of schools connected at 100 Mbps or faster, per the Ministry of Education 2023) (achieved through the Education Informatization 2.0 Action Plan) provides a technological foundation that many European countries have not yet matched.

The disadvantages are equally evident in the constraints on institutional autonomy, the limited space for faculty-driven pedagogical innovation outside state-defined parameters, and the restrictions on access to global AI tools that may limit students’ preparation for international careers. The „walled garden“ approach — promoting domestic AI platforms while restricting access to ChatGPT, Claude, and Gemini — creates a paradox: students are trained in AI literacy using a limited subset of the tools available globally, potentially disadvantaging them in international academic and professional contexts.

7.2 The European Model: Democratic Governance and Humanistic Values

The European vision of the future university is shaped by the tradition of institutional autonomy enshrined in the Magna Charta Universitatum (1988, reaffirmed 2020), the Bologna Process framework, and the European Higher Education Area. The European Universities Initiative, launched in 2019 and significantly expanded since, represents the most ambitious attempt to create a continental university system while preserving institutional diversity. With EUR 145.6 million allocated in the 2026 Erasmus+ call for cooperation partnerships and forward-looking projects, the EU is investing substantially — though not at the scale of China‘s centralized funding.

The intellectual foundation of the European model draws on the concept of Bildung — the German educational ideal of personal formation through engagement with knowledge, culture, and critical inquiry — and its analogues in other European traditions (the French tradition of culture générale, the British tradition of liberal education). These traditions share an emphasis on education as more than skill acquisition: it is the development of the capacity for independent thought, ethical reasoning, and civic participation.

The EU AI Act provides a distinctive regulatory framework that shapes how European universities approach AI adoption. The Act classifies as „high-risk“ AI systems used to evaluate learning outcomes, determine access to education, and monitor student behaviour during examinations. Emotion recognition in educational settings is banned. These regulatory constraints, which have no Chinese equivalent, create a framework of ethical guardrails that encourages careful, reflective AI integration.

The advantages of this model lie in its protection of academic freedom, its respect for disciplinary diversity, and its integration of ethical reflection into technological adoption. The disadvantages are fragmentation, slow adoption, uneven implementation across member states, and the risk that democratic governance processes become obstacles to timely adaptation. A comparison of digital transformation across European higher education institutions found significant „similarities and differences“ — the similarities driven by shared challenges and EU policy incentives, the differences reflecting divergent national traditions, funding levels, and institutional cultures (EUA 2025).

7.3 Towards a Synthesis

The most promising model for the university of the future draws on the strengths of both approaches while mitigating their weaknesses. From the Chinese model, it takes the willingness to invest in digital infrastructure at scale, the integration of AI competencies across all disciplines (not only computer science), and the urgency of rapid adaptation. From the European model, it takes the commitment to institutional autonomy, the integration of ethical frameworks into technological adoption, the emphasis on critical thinking and humanistic education alongside technical skills, and the principle that educational transformation should be faculty-led rather than administratively imposed.

Concretely, this synthesis might involve: shared digital infrastructure (as in the Chinese model) with institutional freedom in its pedagogical application (as in the European model); mandatory AI literacy across disciplines (as in China‘s new primary-secondary mandate) combined with ethical reflection on AI’s limitations and risks (as in the EU AI Act framework); large-scale data platforms for learning analytics (as in China’s Smart Education Platform) governed by strict data protection principles (as in the GDPR); and national investment in smart campus infrastructure (as in China’s provincial initiatives) deployed through competitive, merit-based processes (as in the Erasmus+ model).

7.4 Comparative Summary

The following table summarizes the key dimensions of difference between the Chinese and European approaches to the future university:

Infrastructure: China operates a centralized national platform (Smart Education Platform, 293M users); the EU operates decentralized institutional and alliance-based systems (65 alliances, 340 HEIs).

AI Integration: China mandates AI education at all levels from September 2025, with domestic AI tools promoted; the EU encourages AI literacy through the AI Act (February 2025) and institutional policies, with open access to global tools.

Governance: China follows a top-down, state-directed model with the Ministry of Education and State Council setting direction; the EU follows a bottom-up, faculty-governed model with institutional autonomy protected by the Bologna Process.

Data Environment: China‘s Personal Information Protection Law (PIPL) permits broader educational data use with state oversight; the EU’s GDPR imposes strict data protection requirements that constrain learning analytics.

Speed of Adoption: China‘s centralized model enables rapid deployment across hundreds of millions of users; the EU’s democratic processes ensure careful deliberation but slow implementation.

Quality Assurance: China relies on institutional accreditation and state-led quality monitoring; the EU relies on disciplinary peer review, institutional accreditation, and the European Standards and Guidelines (ESG).

Cultural Foundation: China draws on Confucian educational traditions emphasizing diligence, respect for teachers, and state service; Europe draws on Enlightenment traditions emphasizing critical thinking, individual autonomy, and civic participation.

Neither model fully addresses the core challenge of the future university: integrating technological capability with humanistic purpose. China demonstrates what technology can achieve at scale; Europe demonstrates why scale alone is insufficient without ethical reflection and individual flourishing as educational goals.

7.5 The Skills Question: What Should the Future University Teach?

Behind the institutional and technological questions lies a more fundamental one: what should the university of the future teach? The traditional answer — disciplinary knowledge organized into degree programmes — is increasingly insufficient in a world where knowledge is abundant, freely accessible, and rapidly obsolescing. The World Economic Forum’s Future of Jobs Report (2024) estimates that 44 percent of workers’ core skills will be disrupted within five years, rendering static skill sets obsolete faster than universities can update their curricula.

China has responded to this challenge with characteristic directness, mandating AI literacy as a core competency from primary school onward and integrating „innovation and entrepreneurship“ (创新创业) courses into university curricula across all disciplines. The companion chapter on alternative learning forms (Woesler, this volume) documents how Chinese institutions are also embracing micro-credentials, competency-based education, and project-based learning as supplements to traditional degree programmes.

European responses have been more varied but increasingly converge on a framework of „transversal competencies“ — critical thinking, creativity, collaboration, communication, digital literacy, and ethical reasoning. The EU’s Key Competences for Lifelong Learning framework (updated 2018) provides a reference point, but implementation at the institutional level remains inconsistent.

The deeper question is whether the future university should primarily teach students what to think (knowledge transmission), how to think (critical reasoning), or how to learn (adaptive capacity). The Chinese model, with its emphasis on practical skills and national development priorities, tends toward the first and third. The European model, with its emphasis on Bildung and academic freedom, tends toward the second. The university of the future will need all three — and the wisdom to know when each is appropriate.

This brings us back to the Confucian-Enlightenment convergence noted earlier. Both traditions recognize that education must go beyond mere knowledge transfer to develop the whole person — the Confucian 君子 (junzi, exemplary person) and the Enlightenment ideal of the autonomous, critically thinking citizen. In an age of artificial intelligence, these humanistic ideals are not obsolete; they are more essential than ever. The distinctly human capacities that both traditions cultivate — moral judgement, aesthetic sensitivity, empathy, the ability to navigate ambiguity and complexity — are precisely the capacities that AI cannot replicate and that the labour market of the future will increasingly value.

8. Conclusion

The university of the future will be neither a Chinese smart campus nor a European humanistic seminar — it will need to be both. The challenge of the coming decade is not to choose between technological efficiency and humanistic depth but to integrate them. AI-personalized learning must coexist with the cultivation of intellectual autonomy. Smart classroom infrastructure must serve pedagogical innovation, not merely facilities management. Generative AI must be integrated into curricula in ways that develop students’ critical thinking rather than replacing it. And hybrid learning must be designed not merely for convenience but for genuine educational effectiveness.

The comparison of European and Chinese approaches to university transformation reveals that neither system has fully solved the puzzle of the future university. China demonstrates what is possible when a state commits massive resources to educational digitalization; Europe demonstrates what is necessary when educational transformation is guided by democratic values and humanistic traditions. The university that will thrive in 2035 — and in 2050 — will be the one that learns from both traditions, combining scale with subtlety, speed with reflection, and innovation with the enduring human values that have sustained the university for nearly a millennium.

Acknowledgments

This research was supported by the Jean Monnet Centre of Excellence „EU-Studies Centre: Digitalization in Europe and China„ (EUSC-DEC), funded by the European Union under Grant Agreement No. 101126782. Views and opinions expressed are those of the author only and do not necessarily reflect those of the European Union.

References

Computers and Education: Artificial Intelligence. (2025). Generative AI in higher education: A global perspective of institutional adoption policies and guidelines. Elsevier. DOI: 10.1016/j.caeai.2024.100348.

EDUCAUSE. (2025). Shaping the future of learning: AI in higher education.

Elbertsen, L., Kok, R., & Salimi, N. (2025). Designing the future smart campus: Integrating key elements to enhance user experience. Journal of Science and Technology Policy Management, 16(10), 117–137.

European Universities Association (EUA). (2025). Similarities and differences in the digital transformation of higher education. Global University Associations Forum (GUAF).

International Center for Academic Integrity (ICAI). (2024). Academic integrity and generative AI: Student survey data. As cited in: Frontiers in Artificial Intelligence (2024), „Shaping integrity: Why generative artificial intelligence does not have to undermine education.“ DOI: 10.3389/frai.2024.1471224.

Liu, X., et al. (2025). Regulations, technology policies and universities’ attitudes to artificial intelligence in China. Higher Education Quarterly, 79(4). DOI: 10.1111/hequ.70055.

OECD. (2023). Digital Education Outlook 2023: Emerging governance of generative AI in education.

Rize Education. (2025). The hybrid college of the future.

Ruano-Borbalan, J.-C. (2025). The transformative impact of artificial intelligence on higher education: A critical reflection on current trends and future directions. International Journal of Chinese Education. SAGE. DOI: 10.1177/2212585X251319364.

UNESCO. (2023). Smart Education Platform of China: Laureate of UNESCO Prize for ICT in Education.

Wang, X., Xu, X., Zhang, Y., Hao, S., & Jie, W. (2024). Exploring the impact of artificial intelligence application in personalized learning environments: Thematic analysis of undergraduates’ perceptions in China. Humanities and Social Sciences Communications, 11(1), 1644.

Yu, T., Dai, J., & Wang, C. (2023). Adoption of blended learning: Chinese university students’ perspectives. Humanities and Social Sciences Communications. 10(1), 390. Nature.

Woesler, M. (this volume). Ethical frameworks for AI in higher education: Between European regulation and Chinese innovation.

Woesler, M. (this volume). Student data protection in the digital university: GDPR and China‘s PIPL compared.

Woesler, M. (this volume). Learning a foreign language with and without AI: An empirical comparative study.

World Economic Forum (WEF). (2023). Future of Jobs Report 2023.

Xie, Q., Li, M., & Cheng, F. (2025). Between regulation and accessibility: How Chinese university students navigate global and domestic generative AI. Globalisation, Societies and Education. Taylor & Francis.

Yaqin, A. M. A., Muqoffi, A. K., Rizalmi, S. R., Pratikno, F. A., & Efranto, R. Y. (2025). Hybrid learning in post-pandemic higher education systems: an analysis using SEM and DNN. Cogent Education, 12(1), 2458930. Taylor & Francis.

Zhang, C. (2026). Unlocking academic gains in smart-classroom settings: A moderated-mediation study among Chinese undergraduates. Acta Psychologica. Elsevier.