Artificial intelligence has become the new geopolitical fault line – and universities now sit squarely on it. Washington’s export-control regime blocks sales and technical support for advanced AI chips to China; Beijing, for its part, requires recommendation algorithms and generative-AI models to be filed with – and in some cases to be licensed by – state regulators; and Brussels has approved the world’s first cross-sector ‘trustworthy AI’ act.
These rival rule-sets decide who may collaborate, what data may cross borders and which discoveries become strategic assets.
Universities that misread this terrain risk forfeiting funding, partnerships and, ultimately, academic freedom itself. How can universities protect – and even strengthen – their missions amid such a fragmented AI policy regime?
First, we need to map the fault lines of today’s competitive landscape of national AI rules, based on our analysis of national AI policy.
Second, we need to arm university leaders with four lenses – structural, political, human-resource-related and symbolic – to navigate the turbulence, reinforcing governance, recalibrating diplomacy, retraining talent and reaffirming ethical purpose.
A fragmented, competitive landscape
AI may be everywhere, but each national policy regime reads the technology through a very different lens.
Under United States President Donald Trump’s second term, Washington has cast AI as a lynchpin of its ‘America First’ industrial and security strategy.
Executive Order 14179, ‘Removing Barriers to American Leadership in Artificial Intelligence’, revokes prior directives and instructs all federal agencies to identify and repeal internal rules that “inhibit AI innovation” and to publish an agency-wide plan for sustaining US dominance “without unnecessary bureaucracy”.
Overall, AI policy couples domestic deregulation with export-control nationalism to accelerate US innovation while denying strategic competitors’ access to the tools to catch up.
The UK aspires to become an “AI superpower” by pursuing market-driven AI growth under a light-touch regulatory framework that emphasises responsible, trustworthy innovation and upholds UK independence in AI governance.
China advocates an “agile governance” approach to AI, focused on driving economic growth and national strength, aiming to be a global AI leader by 2030. Its strategy relies on a state-guided AI ecosystem aligned with party-defined values and national security interests, while officially pledging to respect privacy and individual rights.
China’s President Xi Jinping has emphasised that “it is necessary to strengthen the determination of the potential risks of the development of artificial intelligence and to strengthen our watchfulness against them, to safeguard the interests of the people and national security, and to ensure the security, reliability and control of artificial intelligence”.
Russia is taking a state-centric path to AI, emphasising technological sovereignty and teaming up with non-Western partners (for example, launching a new AI alliance with BRICS countries) to advance its AI goals.
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India champions an “AI for All” strategy – leveraging AI as a tool for inclusive growth and societal benefit. Its national approach emphasises public-private partnerships and seeks to serve as a model for other developing countries in using AI for social good.
The European Union (EU) is pushing for digital “technological sovereignty”, combatting its fragmented AI landscape by building a unified, pan-European AI ecosystem grounded in European values of trust, safety and human-centric innovation. The EU’s strategy seeks to collaborate with like-minded partners while reducing reliance on those who don’t share its standards.
UNESCO serves as a global norm-setter on AI ethics – stressing the need to bridge the digital divide and uphold human dignity in line with the Sustainable Development Goals. It advocates inclusive AI governance and international cooperation, with dedicated support to help developing countries shape AI policies.
Meanwhile, large technology companies are portraying themselves as champions of an AI leadership that is aligned with liberal democratic values, forming partnerships with governments and pledging “responsible” AI innovation. Google, Microsoft and OpenAI have all agreed to voluntary US commitments to ensure AI safety.
Given these diverse AI policy perspectives – some emphasising national security and ideological safeguarding, while others focus on global benefit and public good – how can higher education institutions, traditionally committed to international and cross-sectoral collaboration, navigate this complexity? How do they reconcile their commitment to institutional autonomy and academic freedom with the pressures of national AI policies?
As universities stand at this critical intersection, they must consider whether AI policies will become an efficient tool for their growth or a challenge to their foundational values.
Four frames to navigate a complex ecosystem
Universities are coming under a barrage of pressures, demands and diverse needs from various actors. Political leaders and university rectors frequently refer to higher education as the backbone of digital and societal transformation, emphasising that universities must lead AI integration while upholding core academic values.
As the UN Secretary-General stated: “We need a systematic effort to increase access to AI so that developing economies can benefit from its enormous potential. We need to bridge the digital divide instead of deepening it.”
Universities play a critical role in bridging this gap through education and research. To grasp how AI’s policy turbulence bears down on universities, it helps to look through four frames – structural, political, human resource-related and symbolic. Together, these lenses offer a systems-level guide for leaders navigating the complex ecosystem of national AI policies.
Structural: AI regulation and institutional constraints
How do AI regulations and regulatory divergence influence the organisation of higher education institutions?
China and Russia maintain stricter state control over AI (China has detailed rules on algorithms and data and Russia has its own state-driven AI programmes), whereas the US and UK favour pro-innovation approaches with lighter regulation. By contrast, the EU and UNESCO place a strong emphasis on ethics and human rights in AI governance, embedding principles of trust and responsibility in their frameworks.
As AI policies and regulations diverge globally, universities must adapt their organisational structures to align with national priorities, international regulations and interests.
Universities must adjust their structure by revising research protocols, protecting intellectual rights, strengthening data-sharing policies and embedding AI ethics into institutional structures and curricula. Many institutions are already adopting interdisciplinary structures to bridge the gap between AI governance and academic research.
As UNESCO International Institute for Higher Education in Latin America and the Caribbean (IESALC) Director Francesc Pedró noted: Higher education institutions “are expected to be a lighthouse whenever a major crisis emerges”, a reminder of universities’ leadership role in managing the complex challenges of AI governance.
Political: AI geopolitics and university diplomacy
The geopolitical dimensions of AI policy complicate university diplomacy. AI is inevitably shaped by national interests, economic competition and corporate influence. Countries create protective barriers by restricting international partnerships.
For example, the CHIPS and Science Act’s guardrail provisions bar US-subsidised semiconductor firms – and the university labs they partner with – from undertaking advanced-node R&D in China, while China’s Export Control Law (which came into effect in December 2020) and related measures can prevent Chinese entities – including universities – from sharing certain AI research or technology abroad without permission.
Further, Big Tech’s lobbying power complicates the balance between academic independence and industrial priorities. Conversely, UNESCO urges an inclusive approach to AI governance – encouraging universities to act as neutral bridges in global AI diplomacy and knowledge exchange.
Without careful navigation, academic research risks being redirected from societal benefits toward politically driven agendas. Academic freedom and collaboration should not be sacrificed to become the global superpower in AI.
Human resources: AI talent, workforce development and university responsibilities
AI policies are reshaping talent development, skills training and workforce needs, compelling universities to align education programmes proactively with national AI strategies and industry demands.
Policy-makers expect universities to cultivate AI talent through interdisciplinary approaches that integrate technical expertise with critical thinking and ethical awareness. This mandate extends to faculty as well – universities are urged to upskill lecturers and researchers in AI proficiency, so they can teach the latest developments and adapt their research in line with state-of-the-art developments.
Moreover, universities play a vital role in addressing the digital divide and technological inclusivity, ensuring that AI benefits are equitably distributed. As such, universities are emerging as key hubs for developing AI talent pipelines and guiding AI innovations towards broader societal goals.
“AI will touch every sector, every industry, every business function, and significantly change the way we live and work,” according to Google CEO Sundar Pichai. This indicates the urgency for universities to prepare the workforce of tomorrow.
Symbolic: Higher education’s ethical role
How is AI culturally embedded in higher education? How can AI shape the identity of higher education institutions?
Each country infuses its AI narrative with its own values: UNESCO and the EU frame AI around the global public good and human rights; Russia stresses technological and economic sovereignty and self-reliance; China highlights AI as key to national rejuvenation and strength; and the US talks about AI largely in terms of economic competitiveness and innovation leadership.
The question is: How will universities craft their symbolic discourse on AI? Universities need to guide the AI integration process and technological change with ethical principles, not just technical competencies.
Given that, they should serve as ethical stewards, adopting a human-centred approach that upholds democratic values, human rights and responsible AI use. This perspective must be deeply embedded in curricula, teaching and research while addressing the societal impact of AI.
The risk of forming strategic AI blocs – segregating knowledge flows and dividing countries into allies and competitors – looms large. However, universities must not be passive followers of such divisions. Instead, they have the potential to act as mediators, bridging diverse perspectives and calling for a globally inclusive AI transformation.
If universities are to lead in shaping AI’s future, they must boldly champion open collaboration, ethical governance and knowledge-sharing that transcends ideological divides.