Navigating a Shifting Landscape for Students, Staff, and International Offices
This week brought a mix of significant policy announcements and emerging debates that could reshape how universities approach AI across teaching, research, services, and global engagement. The UK Government launched its AI for Science Strategy on 2025-11-20, backed by up to £137 million to train AI-fluent researchers and boost computational infrastructure. Meanwhile, the Higher Education Policy Institute (HEPI) published new thinking on how universities should move from “policing” AI use to ethically embedding AI for learning. Across the Atlantic, attention turned to how AI is affecting graduate employment, with stark evidence that entry-level jobs are shrinking as automation transforms what employers expect from new hires. These themes – AI in research, ethical pedagogy, and graduate employability – intersect in ways that matter for practitioners across the sector.
Learning and Teaching
The most thought-provoking development this week came from HEPI, where Mike Larsen argued that universities should shift from a “police and punish” approach to a “support and validate” model for AI in learning. Rather than investing resources in detecting misuse, institutions could instead focus on building student capability and validating authorship and learning through well-designed support systems.
This is a significant reframe. For teaching staff, it suggests that assessment design and student support should take priority over detection software. The evidence points to a gap: while two-thirds of students now believe using AI is essential in today’s world, only about one-third have received formal AI skills training from their institution. That gap creates both risk and opportunity. If universities can get ahead of the curve by integrating AI literacy into curricula and redesigning assessments, they may build stronger graduate outcomes. But if they continue to focus primarily on policing, they risk missing the chance to prepare students for a workplace where AI fluency is becoming the baseline expectation.
A related point emerged from US coverage: graduates are struggling to find entry-level jobs, with only 30% of 2025 graduates securing positions in their field. AI is automating tasks once assigned to juniors, from document review in law to basic coding in tech. This puts pressure on universities to rethink not just what students learn, but how they demonstrate and apply their learning in ways that complement – not compete with – AI tools.
Research
The UK’s new AI for Science Strategy is the headline research development this week. With up to £137 million earmarked from the broader £2 billion AI investment (2026-2030), the strategy aims to train at least 1,000 AI-fluent researchers through expanded doctoral programmes and interdisciplinary fellowships. It also opens access to new compute infrastructure via the AIRR (AI Research Resource) programme.
For research managers and directors, this signals both opportunity and competition. Institutions with strong AI research capabilities – or partnerships with those that have them – may be well placed to attract funding and talent. However, the strategy also raises questions about equity: will all institutions benefit, or will resources concentrate in already well-resourced centres? Early engagement with UKRI’s new compute calls, and cross-institutional collaboration, could help spread the benefits more widely.
Beyond the UK, the broader trend is clear: AI is reshaping research methods, collaboration, and integrity. Research support teams should be preparing for increased demand for data governance advice, training on responsible AI use in research, and guidance on how AI-generated outputs fit within evolving norms of academic integrity.
Administration and Professional Services
The implications for professional services cut across several domains. Careers services face a particularly urgent challenge. The evidence that AI is shrinking entry-level employment – with Stanford data showing a 13% decline in employment for 22-25 year olds in AI-exposed fields over just three years – means careers teams must rethink both the advice they give and the partnerships they build with employers.
- Curriculum and employability: Collaboration between careers, teaching, and employer engagement teams will be essential to ensure graduates develop skills that complement AI rather than compete with it.
- Student support: The shift from “police and punish” to “support and validate” has implications for disability services, academic skills teams, and registry functions. If AI is to be treated as a legitimate support tool – as some argue it should be for students with disabilities – institutions will need clear, consistent policies and staff training.
- IT and data governance: The UK’s AI for Science Strategy, and the broader adoption of AI tools for student support and administration, will increase demand for secure, well-governed data infrastructure. Professional services teams should be reviewing vendor contracts, data protection arrangements, and bias audit processes.
This is a moment for professional services leaders to advocate for investment in staff capability and governance, not just in new tools.
International Education Management
For international offices, this week’s developments have several implications.
Graduate employability and recruitment messaging: The evidence on shrinking entry-level jobs could affect how prospective international students – and their families – perceive the value of a UK or US degree. Recruitment and marketing teams may need to emphasise how their institutions are preparing graduates for an AI-shaped job market, including through AI literacy, employer partnerships, and work-integrated learning.
Policy divergence and compliance: The UK’s AI for Science Strategy is a reminder that national approaches to AI in research and education are diverging. For institutions with transnational education (TNE) partnerships, branch campuses, or joint programmes, this raises questions about how AI governance – including data sovereignty, research integrity, and assessment standards – will be managed across borders. The EU AI Act’s classification of educational AI systems as potentially “high risk” adds another layer of complexity for UK-EU partnerships.
Student support and onboarding: International students often face unique challenges navigating new systems and expectations. AI-powered support tools – chatbots, document verification, personalised guidance – are increasingly being used to assist international students before and after arrival. But reliance on such tools brings risks: bias, accessibility gaps, and the need for multilingual capability. International offices should ensure that AI-driven support is equitable, transparent, and complemented by human expertise.
Regional nuances: The UK’s investment in AI for science positions it as a competitor for research talent and international research collaboration. Institutions in Australia, the US, and Europe will be watching closely – and may respond with their own initiatives. International education managers should monitor how AI policy develops across key markets, and consider how to position their institutions within an increasingly competitive global landscape.
Takeaway
This week’s developments point to a common theme: the need for universities to move from reactive stances – policing AI, worrying about job losses – to proactive strategies that build capability, support students, and govern AI responsibly. For practitioners, the practical next step is to convene cross-functional conversations: teaching, research, careers, IT, and international teams together, asking how these trends land differently in each area and what shared governance and skills development might look like. The institutions that adapt fastest – not by chasing every new tool, but by embedding AI thoughtfully into their core missions – are likely to be best placed for what comes next.