The conceptual starting point is that legitimacy in global governance has never rested on sovereign command. International institutions hold authority only insofar as their justificatory claims are recognised, contested or denied by audiences around the world. Zürn’s idea of reflexive legitimation conflict describes a situation in which disputes escalate beyond questions of performance or participation to the meta-criteria of legitimacy itself: not only what counts as an effective basis for legitimation, but who decides this and how the answers get institutionalised. Pegram shows that AI governance, fragmented and weakly institutionalised, is unusually exposed to this kind of conflict, because there is no settled regime whose performance can be evaluated. What is contested instead is the evaluative script of global rule itself.

The empirical pivot is the rupture between the Biden and Trump approaches to AI. In 2023, Biden’s Executive Order 14110 on safe and trustworthy AI built explicitly on emerging multilateral precedents, including the OECD AI Principles of 2019, UNESCO’s 2021 Recommendation on the Ethics of AI adopted by 193 member states, and the European Commission’s risk-based AI Act. The order committed the United States to allied engagement in multilateral bodies, international technical standards and global AI safety monitoring, and even adopted the OECD’s definition of AI. Washington subsequently joined the 2023 Bletchley Declaration, signed the Council of Europe Framework Convention on AI in 2024, and sponsored a UN General Assembly resolution calling for safe, secure and trustworthy AI systems for sustainable development.

That trajectory reversed abruptly. On his first day back in office, Trump revoked the Biden order, leaving the United States without federal regulatory guidance. Three days later he issued Executive Order 14179, declaring national policy to sustain and enhance America’s global AI dominance in the name of human flourishing, economic competitiveness and national security. At the Paris AI Action Summit in February 2025, Vice President J.D. Vance warned that excessive regulation could kill a transformative industry, rejected the European model as burdensome, and framed international cooperation chiefly as a defence against authoritarian powers capturing technological infrastructure. The administration declined to sign the summit’s declaration on inclusive and sustainable AI, and in July 2025 withdrew from UNESCO, the body anchoring global AI ethics norms.

Crucially, the study finds, the Trump administration did not abandon international forums altogether. Instead it recast engagement in plurilateral and mercantile terms. America’s AI Action Plan, published in July 2025, promotes plurilateral controls on the AI technology stack while avoiding multilateral treaty bodies, coordinates with allies to extend US export controls against adversaries, and pledges to leverage the US position in standard-setting bodies such as the ITU, OECD, G7 and G20 to counter Chinese influence and ensure AI governance reflects American values. In August 2025, Washington backed a second term for Doreen Bogdan-Martin as ITU Secretary-General, explicitly linking that contest to control over the more than 150 AI standards already set. Trump framed the stakes geopolitically, warning that foreign nations must not make rules and regulations that make it impossible for American firms to do business.

China’s response forms the other half of the analysis, and it is here that the study identifies a striking role reversal. Beijing’s 2017 New Generation Artificial Intelligence Development Plan had already pledged active participation in global AI governance, but Western observers long dismissed Chinese claims as authoritarian and censorial, describing ambitions ranging from the anodyne to the dystopian. Successive initiatives, including a 2022 position paper on ethical AI governance and the Global AI Governance Initiative launched by Xi Jinping at the Third Belt and Road Forum in October 2023, emphasised sovereignty, developing AI for good, greater voice for developing countries and a UN-centred governance body. Yet these claims struggled to gain traction, and China’s inclusion in the Bletchley Declaration was contested so vigorously that UK Prime Minister Rishi Sunak had to defend it publicly.

Trump’s return marked the inflection point. Confronted with tightened chip sanctions in January 2025, Beijing did not retreat into secrecy as many predicted. Instead, firms such as Alibaba and DeepSeek released advanced models as open source, with Premier Li Qiang declaring that China’s innovation is open and open source and that the country was willing to share indigenous technologies. The strategy served a dual purpose: it mitigated the effect of sanctions by globalising Chinese models, and it recast China’s legitimacy claims, transforming a state once portrayed as censorial into a putative provider of AI as a global public good. At the World AI Conference in Shanghai in July 2025, Li called global AI governance fragmented, warned against technological monopoly, and unveiled a Global AI Governance Action Plan proposing two UN-based mechanisms: an International Scientific Panel on AI and a Global Dialogue on AI Governance.

The juxtaposition of the two strategies reveals what Pegram calls rival scripts of legitimation, each mobilising distinct repertoires conditioned by material power and normative legacy. The American script invokes sovereign discretion, market dynamism and technological nationalism, delegitimising liberal norms of ethical oversight and multilateral coordination while instrumentalising international forums for competitive advantage. The Chinese script invokes sovereign equality, developmental inclusion and UN-centred ethics, aligning open-sourcing practices with discursive claims of ethical multilateralism. By August 2025, Western commentators were warning of America’s AI colonialism, while Trump officials confirmed the United States was explicitly rejecting China’s call for global governance, offering instead customised AI packages tied to American technology. Neither script has achieved uncontested recognition, but their antagonism demonstrates that legitimacy itself has become the object of the contest.

The study’s findings carry implications well beyond AI. Legitimation, it concludes, is processual and precarious, continually reconfigured through cycles of challenge and response, in which delegitimation by one actor creates openings for others to reformulate their claims. Repertoires of legitimation remain structurally conditioned: US claims drew credibility from material dominance and corporate alignment, while China’s gained traction precisely because Washington’s rejection of multilateral norms created space for alternative scripts. Crises act as catalytic junctures, and the advent of generative AI, reframed as systemic risk since ChatGPT’s release in November 2022, combined with Trump’s sovereigntist turn, has consolidated competing visions of global order rather than convergence on shared norms.

AI governance, the article suggests, may serve as a bellwether of a post-hegemonic or multiplex world order. What is at stake is not simply the regulation of a transformative technology but the capacity of global governance to sustain any authority at all in a system fractured by sovereignty claims, geopolitical rivalry and competing visions of justice. As the United States and China advance incompatible legitimacy scripts, one grounded in sovereign voluntarism and competitive dynamism, the other in UN-centred cooperation and developmental equity, the study warns that the field exhibits pluralised and antagonistic legitimation that resists consolidation, offering a preview of how authority itself may be contested across the international system in the decade ahead.

Subject of Research: The crisis of legitimacy in global AI governance under competing US and Chinese legitimation strategies

Article Title: Reflexive legitimation conflict: trumpism and the crisis of legitimacy in global AI governance

Article References: Reflexive legitimation conflict: trumpism and the crisis of legitimacy in global AI governance. (n.d.). https://doi.org/10.1007/s43508-025-00123-x

Image Credits: AI Generated

DOI: 10.1007/s43508-025-00123-x

Keywords: AI governance, legitimacy, Trumpism, global governance, China, United States, multilateralism, technological nationalism, UNESCO, open-source AI, sovereignty, post-hegemonic order

The technical foundation of this shift lies in the architecture of large language models. These systems are trained on vast text corpora and learn statistical patterns of language that allow them to generate coherent explanations, solve problems, draft essays, and answer questions across virtually every academic discipline. Unlike earlier educational software, which followed rigid, pre-programmed rules, generative models produce novel responses tailored to each prompt. Wu and Chen point to DeepSeek, an open-source large language model developed for Chinese education research, as an example of how accessible, high-capability AI can empower learners across an entire society rather than remaining locked behind expensive proprietary systems. Open-source approaches matter because they allow universities to inspect, adapt, and integrate models into their own platforms without depending entirely on a handful of commercial vendors, a consideration that touches on cost, sovereignty, and pedagogical control.

One of the most concrete applications the editorial highlights is cognitive diagnosis, the task of figuring out precisely what a student knows and where their understanding breaks down. A companion study by Dong, Chen, and Wu introduces a model-agnostic framework that uses large language models together with the Solo taxonomy, a classification scheme for levels of understanding, to diagnose learners’ cognitive states. Related work by Ma and colleagues demonstrates that large language models can act as zero-shot cross-domain diagnosticians, meaning they can assess knowledge in subject areas they were never explicitly trained to evaluate. The technical significance is considerable: traditional diagnostic systems required large amounts of labeled response data for each specific domain, whereas language-model-based approaches can generalize across subjects. For universities, this means assessment could evolve from infrequent, high-stakes examinations toward continuous, fine-grained feedback that identifies misconceptions the moment they appear.

Mathematical reasoning remains a demanding benchmark for these systems, and the editorial’s reference list acknowledges the challenge directly. Liu and colleagues present MathEval, a comprehensive benchmark designed to evaluate how well large language models handle mathematical reasoning across difficulty levels and topic areas. Mathematics exposes both the power and the fragility of generative models: they can often produce elegant solution steps, yet they may also fail in ways that look convincing to a novice. This is precisely why the human-in-the-loop framing matters. In a collaborative paradigm, the AI proposes, explains, and drills; the instructor verifies, contextualizes, and corrects. The division of labor plays to the strengths of each side, with machines providing tireless availability and breadth while humans supply judgment, accountability, and the ability to recognize when an answer is plausible but wrong.

Personalization is the second pillar of the paradigm shift. Li, Chai, and colleagues explore how detailed student portraits, structured profiles built from learning behavior, performance data, and preferences, can drive personalized learning at scale. Tu, Chen, and Huang examine the mechanisms by which generative AI empowers personalized learning, along with the challenges and pathways institutions must navigate. Combined with diagnostic models, such portraits allow an AI tutor to adapt explanations to an individual student’s level, pace, and prior knowledge in ways a single lecturer facing hundreds of students never could. The editorial situates these capabilities within the characteristics of Generation Z, a cohort that has grown up with interactive digital environments and often expects immediate, individualized feedback. For these students, a static textbook and a weekly lecture are no longer the default interface to knowledge; conversational, adaptive systems are.

The editorial also engages with the geopolitical and cultural dimensions of educational AI. A commentary by Wang argues that DeepSeek’s Chinese-style innovation reinforces China’s educational confidence, while Wu’s own companion piece describes DeepSeek as a vehicle for global education empowerment for a whole society. The underlying point transcends any single model: when capable AI systems are open and affordable, they can reach institutions and learners who have historically been excluded from elite educational resources. The authors suggest that the paradigm shift is therefore not only pedagogical but also structural, potentially redistributing access to high-quality tutoring, feedback, and content creation. At the same time, the emphasis on open models reflects a recognition that educational AI embedded in one nation’s values and curricula will not automatically transfer to another, making locally adaptable systems essential.

None of this enthusiasm is presented without caution. The editorial and the studies it accompanies repeatedly stress that generative models hallucinate, produce confident errors, and can be misused for academic dishonesty. Krause, Panchal, and Ubhe assess the transformative impact of generative AI on higher education and document how institutions are wrestling with questions of assessment design, skill development, and the changing role of the lecturer. If an AI can draft an essay in seconds, universities must rethink what written assignments are meant to measure and how to evaluate the process of thinking rather than only the final product. The collaborative paradigm implies that assessment itself must evolve, rewarding students for how effectively they direct, question, and verify AI output, a skill the authors implicitly treat as a new form of literacy.

Zhu, Wang, and colleagues extend the discussion to K-12 education, surveying current trends and future prospects for large-scale foundation models in schools. Their inclusion in the editorial’s reference frame signals that the paradigm shift begins long before university. Students arriving on campus will already be accustomed to AI-assisted learning, and higher education institutions that fail to adapt risk offering an experience that feels outdated to their own applicants. The technical trajectory supports this expectation: foundation models are becoming multimodal, capable of processing text, images, and diagrams, and increasingly efficient enough to run in settings with limited computing infrastructure. Wu and Chen’s argument is that universities should lead this integration deliberately, shaping tools to pedagogical goals, rather than reacting after the fact to technologies students have already adopted on their own.

What emerges from the editorial is a vision of higher education in which the lecturer’s role is elevated rather than diminished. Freed from repetitive explanation and routine grading support, instructors can focus on mentorship, critical thinking, ethical reasoning, and the design of learning experiences that machines cannot supply. The AI handles scale; the human handles meaning. Wu and Chen describe this as a paradigm shift in the strict sense, a change in the underlying assumptions of the field rather than an incremental tool upgrade. Their position as editors of the journal, disclosed and excluded from the peer-review of their own piece, underscores how seriously the academic publishing community is taking the question. As generative models continue to improve, the institutions that thrive will likely be those that treat the human-AI partnership not as a compromise to be managed but as the defining educational relationship of the coming decades.

Subject of Research: Human-AI collaboration and large language models in higher education

Article Title: Collaboration of Generative AI and Human: Paradigm Shift for Higher Education

Article References: Wu, F., & Chen, J. (2025). Collaboration of Generative AI and Human: Paradigm Shift for Higher Education. Frontiers of Digital Education, 2(2), Article 24. https://doi.org/10.1007/s44366-025-0061-z

Image Credits: AI Generated

DOI: 10.1007/s44366-025-0061-z

Keywords: generative AI, large language models, higher education, DeepSeek, personalized learning, cognitive diagnosis, educational technology, paradigm shift, open-source AI, assessment, Generation Z, Zhejiang University