分析：高校AI战略正成为国际科学会议申办的新筹码
AI Strategy: The New Convention Currency for Universities
本文指出，拥有成熟人工智能（AI）战略的高校在国际科学会议申办中展现出更强的执行承诺能力，这一趋势正在改变传统依赖学术声望的竞标模式。以科隆大学为例，其发布的《价值驱动的AI》战略文件虽未直接提及会议投标，但其涵盖的基础设施、治理框架及伦理审查机制，实质上构成了承接大型学术会议所需的运营底座。
文章分析了高校AI战略如何成为国际科学会议申办中的新竞争优势，为会展目的地机构评估学术合作伙伴能力提供了新的视角。
When 600 staff members at the University of Cologne, Germany, gathered to draft the institution’s AI strategy, they failed to consider conference bidding. And yet, these policy documents may dictate which cities host major scientific conferences over the coming years. This structural blind spot was recently highlighted when an American medical conference allowed prompt-based language models to draft the event’s scientific programme.
From cheating rules to strategy It began as a question of cheating. When generative language models emerged, universities’ first instinct was to draft exam rules: Can students use AI, and if so, how should they report it? Three years on, the focus has shifted. The leading universities have moved beyond rulemaking and built something entirely different – institutional AI strategies encompassing research, education, administration, infrastructure and governance. The implications extend far beyond campus, into an industry rarely mentioned in strategy documents but entirely dependent on them: the scientific conference industry.
The reasoning is simple. Academics drive international scientific conferences. That is the researcher, the professor, the head of department who sits on the board of an international organisation, a person in position to say, “Next time, we should hold the meeting in my city.” Convention bureaux around the world have built their entire business model on this insight. The Madrid Convention Bureau launched Spain’s first conference ambassador programme as early as 2008 and now has nearly 300 ambassadors who act as a link between academic and scientific institutions and international associations.
Fáilte Ireland honoured 109 sector leaders at its biennial Conference Ambassador Recognition Awards last November. The ICCA defines an ambassador as “a person of knowledge and stature, influential within their field,” prepared to work with the destination’s meetings professionals to secure conference business. Similarly, Visit Britain’s Meet England Impact Network brings together ambassadors, academic leaders and sector experts in a national network.
So far, the ambassador has brought academic prestige and a network of contacts to the bid. Now something else is being added: what the home university can actually deliver in operational terms. That’s where AI strategies come into play.
Three levels of maturity No official global ranking of universities with AI strategies exists. However, the data can be categorised across three levels.
At the top are institutions with a genuine, adopted institutional strategy. The University of Cologne is the clearest European example. Their document is entitled Value-Driven AI – The AI Strategy of the University of Cologne, 2026–2029 and is supplemented by a separate AI Policy that sets out the legal framework. The strategy was developed through a participatory process that began in late 2024, in which 600 people from all faculties and staff groups took part in moderated discussion forums known as ‘arenas.’ It encompasses robust infrastructure, the development of AI expertise, and the application of AI in research, teaching, knowledge transfer and administration.
For 2026 and 2027, the university has set the following priorities: further developing an AI office, recruiting additional expertise, centralising information on the university’s AI activities, expanding infrastructure, and supporting flagship projects. Governance is based on a stated ‘dual-track approach,’ whereby established applications can be introduced directly, while entirely new approaches are tested in a controlled environment.
In a global benchmark by Dutch publishing and analytics firm Elsevier, institutions are grouped by their governance maturity. Arizona State University leads its tier with a values-based framework, Principled Innovation and Digital Trust, where cross-faculty teams review AI guidelines.
The University of Bologna grounds its generative AI (GenAI) policy in human-centred principles of transparency, accountability, and sustainability, supported by the Alma Mater AI Center in Vienna, Austria. Purdue University in the USA uses a three-stage ethics review process – rapid, expedited, and full – through its Data Ethics Committee. Yet another example: Macquarie University and Queensland University of Technology in Australia have published a principles-based framework focused on research integrity and transparency.
Singapore Management University applies a community-first model through its Centre for AI and Data Governance. Nanyang Technological University has established flexible frameworks for generative AI in research, including ethics certifications.
ETH Zurich hosts an AI Ethics Policy Network, while Carnegie Mellon University in Pittsburgh operates the K&L Gates Centre for AI Ethics and Policy. The University of Cambridge also maintains its own AI ethics policy. Their partnership with Google Deep Mind has funded the Centre for Human-Inspired Artificial Intelligence (CHIA) at the Institute for Technology and Humanity.
In China, Tsinghua University in northwest Beijing runs the Institute for AI International Governance, while Peking University has aligned its AI strategy with China’s 2035 education plan.
The middle tier includes higher education institutions focusing on operational guidelines and academic integrity frameworks. The University of Oxford permits generative AI in studies and research but requires explicit authorisation and a declaration for summative assessments. The University of Cambridge allows AI for personal study and formative assignments but considers unreported AI-generated content in examinations to be academic misconduct. The IT guidance at the Massachusetts Institute of Technology (MIT) highlights concerns about information security, data protection, and intellectual property rights, prohibiting the entry of personal or institutional data into public AI tools.
Harvard University encourages AI experimentation but requires confidential data to be protected. Imperial College London requires students to specify the tool, publisher, URL, and a description of the AI’s contribution. The University of California, Berkeley, and Yale University have adopted similar policies. In contrast, Columbia University in New York has finalised a draft university-wide policy that prohibits AI use without explicit authorisation. At the same time, Johns Hopkins University has issued comprehensive guidance on responsible AI. Cairo University also belongs to this group, introducing a responsible AI policy in 2025 that builds on the AI strategy the University Council adopted in October 2024.
The tier below that is the largest one, with higher education institutions where a national strategy has not yet translated into institutional action. In Sweden, the government’s action plan identifies public authorities and higher education institutions with specific mandates for data and AI. The Association of Swedish Higher Education Institutions (SUHF) has submitted recommendations on AI adoption, while the national AI Commission’s roadmap proposes skills development for all university teaching staff.
The gap between rhetoric and policy documents remains wide. An academic survey of 31 universities identified 98 relevant strategies, but only thirteen institutions had published cross-institutional strategic plans that actually addressed digital transformation or AI. This gap is where the competitive advantage lies.
The proof is already there The meetings and events industry need not speculate, as the experiment has already been carried out. At the American Medical Informatics Association’s AMIA 2024 Informatics Summit in Boston, prompt-based language models organised the scientific conference. The programme committee enabled the models to develop an algorithm that matched abstracts with reviewers, grouped presentations and posters into thematic sessions, suggested session titles, and generated one-line summaries for each presentation.
The objectives were clear: to reduce organisational burden, improve thematic coherence, simplify programme navigation, and help participants select appropriate sessions. Only publicly available submission information was used; AMIA’s management remained involved throughout, and the models were designed to complement rather than replace human expertise.
In parallel, dedicated systems are being developed. An AI-driven conference management system unveiled in 2025 uses machine learning methods – support vector machines, k-nearest neighbours and random forest – to automatically classify research articles by subject area and assign reviewers based on their field, to address the inefficiencies that characterise submission management, peer review, scheduling and communication.
The same body of literature supports these warnings. A review of AI in academic conferences notes that while AI-supported tools improve efficiency, challenges remain with traceability, reliability, and ethical oversight. AI alone cannot guarantee scientific integrity; ongoing human review remains essential.
The AMIA case was unprecedented. In medical informatics, reviewer shortages, broad subject matter, and high abstract volumes make manual programme planning nearly unmanageable. Similar challenges affect all major scientific conferences. The key factor is whether the host organisation has the mandate, tools, and approved procedures to address these issues, which must come from an institutional strategy rather than individual initiative. A panel at Metascience 2025 raised broader questions: how fundamentally and rapidly is AI transforming the scientific system, and how significant are the knowledge gaps about its driving forces? The business events industry is part of this system but has largely been absent from the conversation.
Five benefits of having a strategy in place
Bidding strength The difference between making a promise and delivering on it comes down to execution. In a conference bid, a higher education institution with an adopted AI strategy, a dedicated AI office, and approved tools can commit to concrete review processes, attendee matchmaking, and programme organisation – whereas competitors can only offer vague promises. This gives universities a powerful new advantage to list in their formal bid proposals, alongside traditional assets like lecture theatres and hotel capacity.
Shorter and cheaper programme organisation Abstract handling, peer-review allocation, session organisation, and scheduling represent the most labour-intensive phases of planning a scientific conference. They are also the exact areas where AI tools have already proven they can dramatically reduce turnaround times and administrative costs.
Sustainability and hybrid formats The AI field’s own events have become cautionary environmental tales, as a recent position paper describes the centralised conference model as unsustainable. That paper showed that travel now accounts for the majority of emissions at non-hybrid events such as the International Conference on Machine Learning (ICML) and the International Conference on Learning Representations (ICLR), where emissions generated by participating authors of accepted papers have more than quadrupled over just five years. Meanwhile, academic conferences are increasingly adopting hybrid formats in response to growing demands for inclusion, technological innovation, and not least environmental responsibility. Achieving digital maturity within the university is a prerequisite for meeting these requirements in a bid.
Legitimacy and risk management Scientific societies place value on unpublished abstracts. Data protection, governance and ethical review are key factors. Purdue’s review levels, the Bologna Process’s transparency requirements, and Cologne’s AI Policy are not just bureaucracy, but selling points.
Content magnet Those with AI strategies, infrastructure and research environments attract AI conferences of their own accord. The third international conference on Hybrid Human-Artificial Intelligence, HHAI2024, was hosted by Malmö University in June 2024. Musashino University in Tokyo will host AIESC 2027 on AI in education and smart campuses in April 2027. Wharton held its conference on AI and the future of work in Philadelphia in May 2026.
What does this mean for the destination?
For convention bureaux and conference venues, this means ambassador programmes should now ask whether their higher education institution has adopted an AI strategy and what it entails. The response will guide what the bureau can commit to in a bid and identify which support functions the destination must develop to address any institutional gaps.
Higher education institutions face a different situation. Vice-chancellors’ offices often develop AI strategies that focus on research, teaching, and administration, but rarely address conference activities. This gap can have significant consequences. Elsevier’s review highlights how effective AI strategy and governance in higher education require clear guiding principles aligned with institutional objectives, citing Vice-Chancellor Chris Day at the University of Newcastle on the importance of prioritising transparency.
If institutional objectives include international visibility, hosting scientific conferences should form part of the strategy – not as an administrative footnote, but as a distinct area of focus with its own priorities.
Moreover, the window of opportunity is narrow. As long as only a minority of higher education institutions have published institution-wide AI plans, this remains a distinctive advantage. Once everyone has a strategy, it will simply become a basic requirement, and competition will shift to who can actually demonstrate that it works.
来源：Meetings International · meetingsinternational.com