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[April 2026] UK’s first community-designed AI awareness campaign launched by University of Sheffield researchers

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[April 2026] UK’s first community-designed AI awareness campaign launched by University of Sheffield researchers

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UK’s first community-designed AI awareness campaign launched by University of Sheffield researchers

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A new AI awareness campaign designed to address people’s fears and questions about the technology, has been launched by researchers from the University of Sheffield.

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[April 2026] AI in higher education: balancing innovation with academic integrity

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[April 2026] AI in higher education: balancing innovation with academic integrity

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AI in higher education: balancing innovation with academic integrity - HEPI

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This blog was kindly authored by Lan Murdock, Senior Communications Manager, Taylor & Francis. Artificial Intelligence (AI) is transforming the research landscape, offering tools to analyse massive datasets, uncover patterns and simplify complex findings. However, as AI is increasingly integrated into research and higher education, ensuring responsible use is not merely a technical challenge – […]

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[March 2026] Live from The Secret Life of Students: AI special

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[March 2026] Live from The Secret Life of Students: AI special

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Live from The Secret Life of Students: AI special

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This week on the podcast recorded live at The Secret Life of Students, new Wonkhe research has found that nearly half of students worry their grades don’t reflect what they actually know – so what does that tell us about how students are making decisions around AI use, and what does it mean for assessment in higher education?Plus highlights from sessions across the conference, and a tuition fees row that threatens to derail the government’s Brexit reset – with the EU pushing for home fee rates for students arriving under a new youth mobility deal.With Helen King, Director of Learning Innovation, Development and Skills at Bath Spa University, Rosie Birch, Communities Officer at Brighton Students’ Union and John Blake, Director at The Post-18 Project, and presented by Jim Dickinson, Associate Editor at Wonkhe.

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[March 2026] AI policy is penalising the students most trying to comply

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[March 2026] AI policy is penalising the students most trying to comply

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AI policy is penalising the students most trying to comply

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Jim Dickinson presents findings from new research on students’ use of AI – and argues the sector is punishing precisely the disposition it should be cultivating

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[March 2026] AI enforcement apparatus exists to punish the anxious middle

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[March 2026] AI enforcement apparatus exists to punish the anxious middle

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AI enforcement apparatus exists to punish the anxious middle

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Detection tools miss the determined cheaters and terrify the principled non-users into self-censoring their own punctuation. Jim Dickinson makes the case for accountability moments over algorithmic guesswork

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[March 2026] Disengaged AI use is a symptom of the structural squeeze on choice

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[March 2026] Disengaged AI use is a symptom of the structural squeeze on choice

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Disengaged AI use is a symptom of the structural squeeze on choice

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As universities cut optional modules to balance the books, Mack Marshall finds that restricted choice is one of the clearest correlates of disengaged AI use

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Trained to stop learning: How students are experiencing assessment and learning in an age of AI

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New research from Wonkhe finds that assessment design – not AI policy – is what determines whether students use AI to learn or to produce. Jim Dickinson and Mack Marshall explain

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[March 2026] Educating the AI generation – institutional AI response is a human, not a policy challenge

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[March 2026] Educating the AI generation – institutional AI response is a human, not a policy challenge

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Educating the AI generation – institutional AI response is a human, not a policy challenge

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Debbie McVitty and Rachel Maxwell talk to education leaders from across the sector about institutional responses to AI – and find that traditional mechanisms for managing and developing learning and teaching may be ill-matched to the pace of change

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[March 2026] Educating for the AI economy: skills, social mobility and the future of opportunity

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[March 2026] Educating for the AI economy: skills, social mobility and the future of opportunity

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WEEKEND READING: Educating for the AI economy: skills, social mobility and the future of opportunity - HEPI

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This blog was kindly authored by Professor Aleks Subic, Vice-Chancellor and Chief Executive, Aston University (Incoming Vice-Chancellor and President of Torrens University Australia). For decades, the pathway from higher education into work underpinned social mobility in the UK. A university degree was a gateway to higher earnings, professional roles, and expanded life opportunities. That narrative […]

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[March 2026] How Ukraine regulates AI in education during the Russian invasion

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[March 2026] How Ukraine regulates AI in education during the Russian invasion

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In the fast-moving landscape of higher education policy, the debate often oscillates between two extremes: the "wait and see" approach of cautious legislators and the "move fast and break things" ethos of technological evangelists.

However, for a nation where the "fog of war" is a daily reality, the luxury of waiting has evaporated. By the time a formal statute is debated and passed, the technology has already pivoted from text to multimodal video and autonomous agents. Ukraine does not yet have a formal law on AI but it has something perhaps more effective for the moment: a resilient soft law ecosystem.

Ukraine's experience in governing AI in education offers a compelling blueprint for the global sector - not as a dry manifesto, but as a practical, tiered playbook designed for agility. Central to this experience are two landmark documents: the Recommendations for the responsible implementation and use of AI technologies in higher education institutions and the Instructional and methodological recommendations on the introduction and use of AI technologies in secondary education institutions. Together, they represent a shift from viewing AI as a disruption to viewing it as critical national infrastructure that must be governed by design.

A tiered governance ecosystem

The central challenge of AI governance is the widening gap between formal law and daily practice. While national legislatures struggle to draft comprehensive statutes, students and staff are already treating AI as basic infrastructure. Ukraine's strategic choice has been to lean into soft law. This approach is not "soft" in its impact. Instead, it is a steering wheel that can iterate at the speed of the technology itself.

This ecosystem operates across four distinct layers to ensure that high-level ethical principles manifest as specific instructions in a student's syllabus:

National guidance: High-level principles and shared language produced by the Ministry of Digital Transformation and the Ministry of Education and Science.

Sector-level codes: Quality assurance guidance that translates principles into shared templates for the wider academic community

Institutional policies: Specific rules on roles, procurement, and data rules

Course-level rules: Where learning outcomes meet reality through syllabus AI clauses and assignment-specific instructions

For higher education Institutions, this governance is built on five operational pillars: Rules, Roles, Workflows, Training, and Review. This ensures that AI adoption is an institutional strategy rather than a random set of personal experiments.

HE and schools playbooks

The recommendations for higher education are explicitly framed not as a manifesto, but as an "implementation kit." The goal is to move beyond the binary of "ban vs. allow" and toward a sophisticated model of institutional governance.

In line with the EU AI Act, the recommendations classify AI applications into risk categories. Any tool used for high-stakes decision-making, such as student admissions, grading, or behavioral monitoring, is flagged as high risk. Rather than a blanket ban, which is explicitly labeled as "short-sighted and harmful," the focus has shifted to a robust risk-screening algorithm. Universities are encouraged to evaluate tools based on a "red flag" system. Tools that present risks to human rights or data privacy are flagged as Unacceptable/High Risk and rejected or strictly monitored. Tools flagged as Medium/Low Risk are approved for use with specific safeguards.

This vetting includes a "suitability checklist" that assesses functional capabilities and compliance with learning goals. It also includes vital local details, such as checking for Ukrainian-language interfaces and the ability to pay in local currency, a critical requirement under wartime financial constraints.

The document encourages a "problem-pilot-training-review" sequence. Universities are advised to identify a specific pedagogical or administrative problem, pilot a tool in a controlled environment, train staff on its nuances, and set a regular review cycle. This acknowledges that any policy drafted today will likely be obsolete in six months as autonomous agents become more prevalent.

While the HE guidelines focus on professional autonomy, the recommendations for general secondary education are more focused on protecting minors and developing foundational cognitive skills. Aligning with global standards and the UNESCO Guidance for Generative AI, the recommendations mandate that AI tools be used only by students aged 13 and above, with explicit parental consent required for those aged 13-18. This is a vital guardrail in an environment where AI tutors are often marketed directly to children without adequate privacy oversight.

The school playbook identifies specific "pedagogical substrates" - the underlying support layer that AI can provide to a teacher. AI is explicitly defined as a supporting tool, never the sole source of information. High-value substrates include inclusion, such as using speech-to-text and visual generation to support students with special educational needs or those displaced by conflict; teacher productivity, where AI can automate the generation of lesson plans and "mission-based quests" to reduce the administrative burden on teachers; and gamification, which is used to create personalised learning paths that keep students engaged in hybrid or remote learning environments necessitated by the war.

Resilience through design

The value of the Ukrainian model lies in its emphasis on operationalising governance immediately. Paralysed by the search for a perfect law or organisational policy, many sectors fail to act. The Ukrainian experience can be used as an action plan for immediate implementation:

Create a one-page data rule: Clearly define which sensitive data (grades, health information, confidential research) must never be pasted into public AI tools.

Move from literacy to verification fluency: Staff and students need "verification habits" - the technical and critical skills to verify the outputs of LLMs and recognise hallucinations.

Adopt lightweight risk checklists: Every department should have a simple "red flag" document to vet new software before it becomes part of the curriculum.

Set a review cycle: Acknowledging that the transition to autonomous agents and multimodal video requires a policy that is as dynamic as the technology itself.

Regulation is often seen as a brake on innovation. In the Ukrainian context, it has become a necessary shield. By adopting a tiered, soft law approach, the country has provided a framework that supports university autonomy while ensuring that AI implementation is not just a collection of experiments, but an organisational ecosystem built on rules, roles, and review.

The lesson for the global sector is clear: Don't wait for the formal statute. Don't outsource your pedagogical decisions to tech vendors. Instead, design and govern your own learning ecosystem. Ukraine's playbook demonstrates that even in the most challenging circumstances, it is possible to lead with both innovation and institutional responsibility.

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How Ukraine regulates AI in education during the Russian invasion

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Traditional regulatory systems aren't set up to manage fast-moving technologies. Dmytro Chumachenko and Bo Kelestyn argue that Ukraine's pragmatic approach could hold lessons for others

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[March 2026]The choices universities and colleges make about AI are political

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[March 2026]The choices universities and colleges make about AI are political

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The choices universities and colleges make about AI are political

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Ahead of this week’s Digifest, Michael Webb and Rebecca Flook confront the complex values systems behind general purpose AI technology

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[February 2026] 
King’s College London and ICS.AI light the path for safe, personalised AI in higher education

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[February 2026] 
King’s College London and ICS.AI light the path for safe, personalised AI in higher education

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How a university-approved copilot helped students understand complex topics, practise for exams, and build confidence - without compromising academic integrity.​

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[February 2026] Generation Ready: Scaling Safe, High Quality AI in England’s Schools

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[February 2026] Generation Ready: Scaling Safe, High Quality AI in England’s Schools

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[February 2026] How are GPs using AI? Insights from the front line

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[February 2026] How are GPs using AI? Insights from the front line

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Successive governments have sought to improve access to general practice, or the working lives of GPs, via a mix of technological, patient self-care, administrative and staffing interventions. Much newer, however, is the idea that Artificial Intelligence (AI) has a meaningful role to play. AI refers to machines that perform tasks normally requiring human intelligence, particularly by learning from data to find patterns and make predictions.

The study for this report aimed to determine the proportion of GPs in the UK currently using AI in their clinical practice, and to explore how they are using it. We sought to understand the range of tasks for which GPs use AI, the perceived benefits and concerns, and the barriers and enablers to greater adoption of AI in general practice.

AI refers to machines that perform tasks normally requiring human intelligence, particularly by learning from data to find patterns and make predictions.This can include tasks such as transcribing speech from patient consultations into summaries and patient notes, producing responses to patient queries, through to tools that identify potentially serious skin conditions from images. We used a mixed-methods approach, combining a nationwide survey (as part of the Royal College of General Practitioners' annual GP Voice Survey), with a series of online focus groups. This research is the largest and most up-to-date survey of general practitioners on this topic, giving insight into AI use just as the NHS 10 Year Health Plan commits to rapid expansion of AI use in the NHS.

Key findings from the survey include:

Of the 2,108 GP survey respondents, 598 (28%) said they currently use AI tools in their clinical practice. Breaking this figure down, 13% of all GPs use tools provided by their practice, 11% use tools they have obtained independently, and 4% use a combination of both.

There is significant variation in AI adoption among GPs across different demographic groups:

Male GPs were significantly more likely to use AI than female GPs. Of the 848 male GPs who responded to the survey, a third (33%) said they used AI. This compared to a quarter (25%) of the 1,184 female GPs.

GPs working in socioeconomically deprived areas were less likely to use AI (and in particular practice-provided AI). Of the 1,046 GPs who said they worked in more deprived areas, just over a quarter (27%) said they used AI tools, compared to over a third (35%) of the 467 GPs who said they worked in more affluent areas.

Additionally, GPs in England were more likely to use AI (31%) than those in Scotland and Northern Ireland (20% and 9%, respectively). GPs in Wales were more likely to use AI (28%) than those in Northern Ireland (differences with Scotland were not statistically significant).

Younger GPs were more likely to use self-obtained AI tools than relatively older GPs. Of the 461 GPs aged under 35, 15% reported using AI tools they had brought to work compared to 11% of GPs aged 35-54 and 8% of GPs aged 55 or more. GPs aged 45-54 were more likely (15%) to use practice-provided AI tools than those aged under 35 (11%) and those aged 65 or over (6%).

Among the 597 respondents who reported which tasks they use AI tools for, over half (57%) were using AI tools for clinical documentation and note taking. Around four out of 10 GPs use AI tools for professional development (45%) and administrative tasks (44%), but fewer use AI tools to support clinical decision-making (28%) - though focus groups suggested that GPs are actively testing AI tools for this purpose.

GPs want AI tools to handle routine time-consuming tasks reliably, allowing them to focus on complex clinical reasoning and meaningful patient relationships. For example, when asked to prioritise up to three areas for AI development to focus on over the next two to three years, the 2,108 GPs selected:

Regardless of their current use of AI, participating GPs at all career stages expressed concerns about AI adoption in general practice, namely:

Professional liability and medico-legal issues (89% among non-users, 80% among those who use practice-selected AI tools, and 80% among those who use self-obtained AI tools)

Lack of regulatory oversight on AI (88% among non-users, 78% among those who use practice-selected AI tools, and 74% among those who use self-obtained AI tools)

Risks of clinical errors (83% among non-users, 69% among those who use practice-selected AI tools, and 70% among those who use self-obtained AI tools)

Patient privacy and data security (82% among non-users, 69% among those who use practice-selected and self-obtained AI tools).

Focus groups revealed that the greatest benefit GPs experience from AI is saving time and reducing administrative burden. While policymakers hope that this saved time will be used to offer more appointments, GPs reported using it primarily for self-care and rest, including reducing overtime working hours to prevent burnout.

Focus groups echoed survey findings. GPs emphasised a lack of regulatory oversight of AI as a major concern, as well as misleading or incorrect outputs ('hallucinations'). Beyond ambient voice technologies, for which guidance has been developed for the NHS at the national level, the implementation of AI in general practice appears to depend heavily on local policies developed by individual practices and Integrated Care Boards (ICBs), as well as local staff willing to test tools and share their learning. But practice is inconsistent across the country, with focus group participants suggesting some ICBs forbid all AI use and others actively encourage safe use and piloting. GPs highlighted the need for clear national standards, supported by local policies and aligned training.

To address variation in AI adoption and encourage responsible use, policymakers in England will need to:

Work towards rapidly establishing evidence-based national guidance to address variation and inconsistency across ICBs, avoiding a postcode lottery, which should cover both administrative AI tools and clinical decision-making AI tools, as well as generative AI tools.

Immediately clarify professional liability and safe AI use and regulatory and governance frameworks. This should be done by a consortium of national policymakers, professional and sector regulators and (like the guidance discussed above) should cover AI tools that are considered medical devices, as well as those that are not.

Develop comprehensive and structured training and education programmes during medical education, as well as for postgraduate NHS staff. This should be funded nationally by the Department for Health and Social Care, with regulators and Medical Royal Colleges involved in standardising and specifying the content of this training.

Use research into the impact of AI to set realistic ambitions about the potential benefits of AI. The 10 Year Health Plan suggests AI will radically improve patient access, but this study highlights a need to recognise that some of the time saved will reduce clinician overtime (and/or workforce burnout) rather than immediately equate to more appointments.

Take actions to mitigate the risk that AI use can widen health inequalities. This should include addressing the finding that AI users in more deprived areas were less likely to access practice-based tools, as well as that some AI tools don't support minority languages.

Consider the environmental impact. AI adoption may increase carbon emissions and electronic waste, conflicting with NHS and RCGP net zero goals. National guidance is needed to align AI use with environmental priorities.

These recommendations may also have relevance to policymakers in Scotland, Wales and Northern Ireland.

To ensure that future tools address the needs and concerns of GPs, AI developers and tech suppliers will need to:

Focus on developing AI tools that save GPs time in their routine work, assisting with automating administrative tasks and clinical documentation, not replacing clinical judgement.

Integrate tools seamlessly with GPs' electronic patient records rather than tools that are standalones or bolt-ons.

Address and reduce hallucinations and ensure their risks are emphasised in training.

Co-design tools for GPs with diverse groups of GPs and other practice staff.

Given that many GPs are already using AI, in the interim GP leaders and GPs in practice will need to:

Develop interim local AI practice guidance until clearer national guidance and regulatory and governance frameworks are available. Encourage open discussion of all AI use within practices, and consider developing clear local protocols which might cover patient consent, approved tools, and reporting of adverse events.

Test, learn and share collectively. Where possible, allocate time to evaluate tools, share learning across networks, report problems with tools and especially those that led to errors in care, and share resources such as policies and case studies.

Help to educate patients about the use of AI in practice. As AI becomes integrated into general practice, it will be important to explain the role that AI is playing in practice while also maintaining the human elements of care that patients value.

The government sees improving access to and experience of general practice as a key priority, with AI expected to play a major role. Our study finds that AI has the potential to enhance patient care and reduce GP workloads, but benefits are not guaranteed, nor is rapid adoption imminent. Currently, 28% of GPs across the UK (and 31% in England) use AI tools, yet guidance varies widely. Some ICBs urge caution, others encourage experimentation with approved tools. Concerns about regulation, liability, as well as a lack of national guidance, remain significant barriers.

Policymakers hope AI will free GPs for more appointments, but most use saved time to reduce overtime and burnout, so expectations may need to be re-examined. Successful implementation will require addressing system-level issues, including clear guidance, training, equity, and safeguarding professional values. Policymakers and those involved in AI innovation need to act now.

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How are GPs using AI? Insights from the front line

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While a number of governments have sought to improve access to general practice, or the working lives of GPs, via a mix of technological, administrative and staffing interventions, the idea that Artificial Intelligence (AI) could play a meaningful role is far more recent.

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Department for Science, Innovation and Technology

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The Department for Science, Innovation and Technology (DSIT) published the AI Opportunities Action Plan in January 2025, setting out a roadmap for government to capture the opportunities of AI to enhance growth and productivity and create tangible benefits for UK citizens. The plan outlines a comprehensive vision structured around 3 pillars: enabling foundational infrastructure (such as compute, data access, and skills); transforming public services through AI integration; and securing long-term technological sovereignty via homegrown frontier capabilities. Central to this vision is the ambition to embed AI across the UK economy—supporting productivity, competitiveness, and inclusive innovation.

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[February 2026] Building an AI manifesto

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[February 2026] Building an AI manifesto

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Jacob Tucker reflects on drafting an AI manifesto and why designing it with students and staff is central to its success

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[February 2026] The Impact of Artificial Intelligence on Jobs, Skills and Wages

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[February 2026] The Impact of Artificial Intelligence on Jobs, Skills and Wages

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The Impact of Artificial Intelligence on Jobs, Skills and Wages – City-REDI Blog

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AI is reshaping global labour markets, creating new digital roles while disrupting others. Explore key trends, risks, and opportunities from international evidence.

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[February 2026] What generative AI reveals about assessment reform in higher education

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[February 2026] What generative AI reveals about assessment reform in higher education

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What generative AI reveals about assessment reform in higher education - HEPI

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This blog was kindly authored by Dr Emma Ransome, Academic Lead for Teaching and Learning at Birmingham City University. Assessment is fast becoming a central focus in the higher education debate as we move into an era of generative AI, but too often institutions are responding through compliance and risk-management actions rather than fundamental pedagogical […]

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[January 2026] AI Skills for Life and Work: summary report

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[January 2026] AI Skills for Life and Work: summary report

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Department for Science, Innovation and Technology

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AI Skills for Life and Work: summary report

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This report was authored by Jacob Bellamy, Jamie Douglas, James Wickett-Whyte, and Nathan Bransden at Ipsos.

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[January 2026] Making human learning visible in a world of invisible AI

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[January 2026] Making human learning visible in a world of invisible AI

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Making human learning visible in a world of invisible AI

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As generative AI becomes increasingly invisible in higher education, universities risk struggling to explain what makes human learning distinctive. Abby Osborne and Robert Eaton suggest that critical thinking is where that challenge — and opportunity — comes into focus

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[January 2026] What grade inflation panics miss about the real value of higher education

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[January 2026] What grade inflation panics miss about the real value of higher education

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What grade inflation panics miss about the real value of higher education

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Mark Peace puts the case that pressure on the graduate premium, in combination with AI, should be causing higher education to look again at the challenge of capturing the full breadth of student outcomes, beyond a single grade

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[January 2026] Teaching in the age of generative AI: why strategy matters more than tools

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[January 2026] Teaching in the age of generative AI: why strategy matters more than tools

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Join HEPI and Advance HE for a webinar today (Tuesday, 13 January 2026) from 11am to 12pm, exploring what higher education can learn from leadership approaches in other sectors. Sign up here to hear this and more from our speakers.
This blog was kindly authored by Wioletta Nawrot, Associate Professor and Teaching & Learning Lead at ESCP Business School, London Campus.
Generative AI has entered higher education faster than most institutions can respond. The question is no longer whether students and staff will use it, but whether universities can ensure it strengthens learning rather than weakens it. Used well, AI can support personalised feedback, stimulate creativity, and free academic time for deeper dialogue. Used poorly, it can erode critical thinking, distort assessment, and undermine trust.

The difference lies not in the tools themselves but in how institutions guide their use through pedagogy, governance, and culture.

AI is a cultural and pedagogical shift, not a software upgrade

Across higher education, early responses to AI have often focused on tools. Yet treating AI as a bolt-on risks missing the real transformation: a shift in how academic communities think, learn, and make judgements.

Some universities began with communities of practice rather than software procurement. At ESCP Business School, stakeholders, including staff and students, were invited to experiment with AI in teaching, assessment, and student support. These experiences demonstrated that experimentation is essential but only when it contributes to a coherent framework with shared principles and staff development.

Three lessons have emerged as AI rollouts have been deployed. Staff report using AI to draft feedback or generate case study variations, but final decisions and marking remain human. Students learn more when they critique AI, not copy it. Exercises where students compare AI responses to academic sources or highlight errors can strengthen critical thinking. Governance matters more than enthusiasm. Clarity around data privacy, authorship, assessment and acceptable use is essential to protect trust.

Assessment: the hardest and most urgent area of reform

Once students can generate fluent essays or code in seconds, traditional take-home assignments are no longer reliable indicators of learning. At ESCP we have responded by:

Introducing oral assessments, in-class writing, and step-by-step submissions to verify individual understanding.

Asking students to reference class materials and discussions, or unique datasets that AI tools cannot access.

Updating assessment rubrics to prioritise analytical depth, originality, transparency of process, and intellectual engagement.

Students should be encouraged to state whether AI was used, how it contributed, and where its outputs were adapted or rejected. This mirrors professional practice by acknowledging assistance without outsourcing judgement. This shift moves universities from policing to encouraging by detecting misconduct and teaching responsible use.

AI literacy and academic inequality

AI does not benefit all students equally. Those with strong subject knowledge are better able to question AI's inaccuracies; others may accept outputs uncritically.

Generic workshops alone are insufficient. AI literacy must be embedded within disciplines, for example, in law through case analysis; in business via ethical decision-making; and in science through data validation. Students can be taught not just how to use AI, but how to test it, challenge it, and cite it appropriately.

Staff development is equally important. Not all academics feel confident incorporating AI into feedback, supervision or assessments. Models such as AI champions, peer-led workshops, and campus coordinators can increase confidence and avoid digital divides between departments.

Policy implications for UK higher education

If AI adoption remains fragmented, the UK's higher education sector risks inconsistency, inequity, and reputational damage. A strategic approach is needed at an institutional and a national level.

Universities should define the educational purpose of AI before adopting tools, and consider reforming assessments to remain robust. Structured professional development, opportunities for peer exchange, and open dialogue with students about what constitutes legitimate and responsible use will also support the effective integration of AI into the sector.

However, it's not only institutions that need to take action. Policymakers and sector bodies should develop shared reference points for transparency and academic integrity. As a nation, we must invest in research into AI's impact on learning outcomes and ensure quality frameworks reflect AI's role in higher education processes, such as assessment and skills development.

The European Union Artificial Intelligence Act (Regulation (EU) 2024/1689) sets a prescriptive model for compliance in education. The UK's principles-based approach gives universities flexibility, but this comes with accountability. Without shared standards, the sector risks inconsistent practice and erosion of public trust. A reduction in employability may also follow if students are not taught how to use AI ethically while continuing to develop their critical thinking and analytical skills.

Implications for the sector

The experience of institutions like ESCP Business School shows that the quality of teaching with AI depends less on the technology itself than on the judgement and educational purpose guiding its use.

Generative AI is already an integral part of students' academic lives; higher education must now decide how to shape that reality. Institutions that approach AI through strategy, integrity, and shared responsibility will not only protect learning, but renew it, strengthening the human dimension that gives teaching its meaning.

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Teaching in the age of generative AI: why strategy matters more than tools - HEPI

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Join HEPI and Advance HE for a webinar today (Tuesday, 13 January 2026) from 11am to 12pm, exploring what higher education can learn from leadership approaches in other sectors. Sign up here to hear this and more from our speakers.

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[January 2026] New HEPI Policy Note: Using Artificial Intelligence (AI) to Advance Translational Research

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[January 2026] New HEPI Policy Note: Using Artificial Intelligence (AI) to Advance Translational Research

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A new report by HEPI and Taylor & Francis explores the potential of AI to advance translational research and accelerate the journey from scientific discovery to real-world application.
Using Artificial Intelligence (AI) to Advance Translational Research (HEPI Policy Note 67), authored by Rose Stephenson, Director of Policy and Strategy at HEPI, and Lan Murdock, Senior Corporate Communications Manager at Taylor & Francis, draws on discussions at a roundtable of higher education leaders, researchers, AI innovators and funders, as well as a range of research case studies, to evaluate the future role of AI in translational research.

The report finds that AI has the potential to strengthen the UK's translational research system, but that realising these benefits will require careful implementation, appropriate governance and sustained investment.

You can find the press release and read the full report here.

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New HEPI Policy Note: Using Artificial Intelligence (AI) to Advance Translational Research - HEPI

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A new report by HEPI and Taylor & Francis explores the potential of AI to advance translational research and accelerate the journey from scientific discovery to real-world application. Using Artificial Intelligence (AI) to Advance Translational Research (HEPI Policy Note 67), authored by Rose Stephenson, Director of Policy and Strategy at HEPI, and Lan Murdock, Senior Corporate Communications Manager at [...]

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[November 2025] Report spotlights AI capabilities of Yorkshire Universities’ membership

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[November 2025] Report spotlights AI capabilities of Yorkshire Universities’ membership

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Report spotlights AI capabilities of Yorkshire Universities’ membership - Yorkshire Universities

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Background Recent government policy has focused on the importance of artificial intelligence (AI) for the future of the UK’s economy and society. In January 2025, the UK Government published the AI Opportunities Action Plan. The Action Plan set out the government’s intention to create a number of AI Growth Zones across the UK to boost ... Find out more

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[December 2025] Ethical AI in higher education: boosting learning, retention and progression

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[December 2025] Ethical AI in higher education: boosting learning, retention and progression

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New research highlights a vital policy window: deploying Artificial Intelligence (AI) not as a policing tool but as a powerful mechanism to support student learning and academic persistence.

Evidence from independent researcher Dr Rebecca Mace, drawing on data generated by a mix of high, middle and low-tariff UK universities, suggests a compelling, positive correlation between the use of ethically embedded 'AI for Learning' tools and student retention, academic skill development and confidence. The findings challenge the predominant narrative that focuses solely on AI detection and academic misconduct, advocating instead for a clear and supportive policy framework to harness AI's educational benefits.

Redefining the AI conversation: from threat to partner

The initial response of higher education institutions to generative AI has been, understandably, centred on fear of disruption. However, this focus overlooks its immense potential to address perennial challenges in the sector, particularly those related to retention and academic preparedness.

Understanding the purpose and pedagogical role of different types of AI - distinguishing between AI for learning, AI for correction, and AI for content generation - is crucial for their responsible and effective use in higher education, shaping institutional policy and student experience.

As Professor Rebecca Bunting, Vice-Chancellor of the University of Bedfordshire, notes in her Foreword to the new research:

The real conversation we should be having is not about whether students should use AI, but how it can be used ethically and effectively to improve learning outcomes for our students.

This sentiment was echoed in a recent webinar discussing the findings, where guest panelists argued that framing AI as a constant threat leads to a fundamental misunderstanding of how students perceive and use the technology.

HEPI's Director, Nick Hillman OBE, reinforces the policy relevance of this shift in his own contribution to the new report:

The roll-out of AI is a great opportunity to improve all that higher education institutions do.

Building on research published in HEPI's recent collection of essays on AI, he also urges policymakers to move away from simplistic binary thinking:

It is now becoming increasingly clear that AI is a tool for use by humans rather than a simple replacement for humans.

The measurable impact: confidence, skills, and retention

The new research focuses on a specific AI for Learning tool from Studiosity in which the AI acts as a learning partner, prompting reflection and supporting students in developing their own ideas, as opposed to generating content on their behalf.

The quantitative findings are striking:

Retention: There is a positive correlation on retention and progression for students using Studiosity . Students accessing this formative feedback were significantly more likely to continue their studies than those who did not. For high-risk students, in particular, higher engagement with Studiosity correlated with greater persistence. This suggests the tool acts as a 'stabilising scaffold', addressing not just academic gaps but also the psychological barriers (like low self-efficacy) that lead to attrition.

Academic skills development: Students showed measurable improvement across academic writing types, with the most significant gains observed in text analysis, scientific reports and essays. Critically, lower-performing students improved fastest, suggesting an equalising effect. This is because the Studiosity tool supports higher-order thinking skills like criticality, use of sources and complexity of language, not just mechanics.

Student voice and belonging: Students frequently said the Studiosity tool helped them 'articulate their ideas more clearly' and to 'say it right' rather than generating thoughts for them. During one of the focus groups, as one student said, 'It's not the ideas I struggled with; it's how to start writing them down in the right way'. This function, sometimes called academic code-switching, is crucial for students from underrepresented backgrounds and is vital to fostering a sense of academic belonging.

Bridging the policy-practice divide and the need for equity

However, the research revealed a 'concerning discrepancy' between student perception and institutional regulation. A 'low-trust culture' appears to be developing, driven by vague institutional messaging, which sees students hiding their use of AI even when it is for legitimate support.

Staff often centre their concerns on policy enforcement and 'spotting misuse' while students focus on the personal anxiety of unintentionally crossing 'ill-defined ethical lines'. As one student explained, ' I would feel so guilty ' even if the AI would make their life easier, a sign that the guilt is 'not rooted purely in fear of being caught, but in a deeper discomfort about presenting work as their own'.

Moreover, there is a clear equity issue. Paywalled AI tools risk deepening the digital divide and penalising students from lower-income backgrounds. Students with low AI literacy are more likely to be flagged for misconduct because they use AI clumsily, while digitally fluent students can blend AI support more subtly.

Recommendations for an ethical AI strategy

The solution is not to resist AI but to integrate it with intentionality, strategy and clarity. The research offers clear and constructive policy proposals for the sector:

1. Choose the right tool for the job: Focus on dedicated AI for Learning tools that develop skills and maintain academic integrity, rather than all-purpose content-generating chatbots.

2. Design clear and consistent policy: Develop nuanced policies that move beyond a binary definition of 'cheating' to reflect the complex and iterative ways students are now using AI, ensuring consistency across the institution.

3. Promote transparency: Educators should disclose their own appropriate AI use to remove stigma and foster a culture of critical engagement, allowing students to speak openly about their support needs.

4. Prioritise equitable access: Institutions should invest in institutionally funded tools to mitigate the digital literacy and economic divides, ensuring all students - especially those most at risk - have fair and transparent access to academic support.

In conclusion

The report concludes that AI offers a substantial policy opportunity to boost a student's sense of legitimacy and belonging, directly contributing to one of the sector's most pressing concerns: student success and retention. Policymakers should now shift their attention from policing to pedagogy. You can access a copy of the full report here.
Studiosity is writing feedback and assessment security to support students and validate learning outcomes at hundreds of universities across five continents, with research-backed evidence of impact.

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Ethical AI in higher education: boosting learning, retention and progression - HEPI

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This blog was kindly authored by Isabelle Bambury, Managing Director UK and Europe at Studiosity, a HEPI Partner New research highlights a vital policy window: deploying Artificial Intelligence (AI) not as a policing tool but as a powerful mechanism to support student learning and academic persistence.

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[December 2025] High quality learning means developing and upskilling educators on the pedagogy of AI

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[December 2025] High quality learning means developing and upskilling educators on the pedagogy of AI

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There's been endless discussion about what students do with generative AI tools, and what constitutes legitimate use of AI in assessment, but as the technology continues to improve there's a whole conversation to be had about what educators do with AI tools.

We're using the term "educators" to encompass both the academics leading modules and programmes and the professionals who support, enable and contribute to learning and teaching and student support.

Realising the potential of the technologies that an institution invests in to support student success requires educators to be willing and able to deploy it in ways that are appropriate for their context. It requires them to be active and creative users of that technology, not simply following a process or showing compliance with a policy.

So it was a bit worrying when in the course of exploring what effective preparation for digital learning futures could look like for our Capability for change report last year, it was noticeable how concerned digital and education leaders were about the variable digital capabilities of their staff.

Where technology meets pedagogy

Inevitably, when it comes to AI, some HE staff are enthusiastic early adopters and innovators; others are more cautious or less confident - and some are highly critical and/or just want it to go away. Some of this is about personal orientation towards particular technologies - there is a lively and important critical debate about how society comes into a relationship with AI technology and the implications for, well, the future of humanity.

Some of it is about the realities of the pressures that educators are under, and the lack of available time and headspace to engage with developmental activity. As one education leader put it:

Sometimes staff, they know that they need to change what they're doing, but they get caught in the academic cycle. So every year it's back to teaching again, really, really large groups of students; they haven't had the time to go and think about how to do things differently.

But there's also an institutional strategic challenge here about situating AI within the pedagogic environment - recognising that students will not only be using it habitually in their work and learning, but that they will expect to graduate with a level of competence in it in anticipation of using AI in the workplace. There's an efficiency question about how using AI can reprofile educator working patterns and workflows. Even if the prospect of "freeing up" lots of time might feel a bit remote right now, educators are clearly going to be using AI in interesting ways to make some of their work a bit more efficient, to surface insight from large datasets that might not otherwise be accessible, or as a co-creator to help enhance their thinking and practice.

In the context of learning and teaching, educators need to be ready to go beyond asking "how do the tools work and what can I do with them?" and be prepared to ask and answer a larger question: "what does it mean for academic quality and pedagogy when I do?"

As Tom Chatfield has persuasively argued in his recent white paper on AI and the future of pedagogy, AI needs to have a clear educative purpose when it is deployed in learning and teaching, and should be about actively enhancing pedagogy. Reaching this halcyon state requires educators who are not only competent in the technical use of the tools that are available but prepared to work creatively to embed those tools to achieve particular learning objectives within the wider framework and structures of their academic discipline. Expertise of this nature is not cheaply won - it takes time and resource to think, experiment, test, and refine.

Educators have the power - and responsibility - to work out how best to harness AI in learning and teaching in their disciplines, but education leaders need to create the right environment for innovation to flourish. As one leader put it:

How do we create an environment where we're allowing people to feel like they are the arbiters of their own day to day, that they've got more time, that they're able to do the things that they want to do?...So that's really an excitement for me. I think there's real opportunity in digital to enable those things.

Introducing "Educating the AI generation"

For our new project "Educating the AI generation" we want to explore how institutions are developing educator AI literacy and practice - what frameworks, interventions, and provisions are helpful and effective, and where the barriers and challenges lie. What sort of environment helps educators to develop not just the capability, but also the motivation and opportunity to become skilled and critical users of AI in learning and teaching? And what does that teach us about how the role of educators might change as the higher education learning environment evolves?

At the discussion session Rachel co-hosted alongside Kortext advisor Janice Kay at the Festival of Higher Education earlier this month there was a strong sense among attendees that educating the AI generation requires universities to take action on multiple fronts simultaneously if they are to keep up with the pace of change in AI technology.

Achieving this kind of agility means making space for risk-taking, and moving away from compliance-focused language to a more collaborative and exploratory approach, including with students, who are equally finding their feet with AI. For leaders, that could mean offering both reassurance that this approach is welcomed, and fostering spaces in which it can be deployed.

In a time of such fast-paced change, staying grounded in concepts of what it means to be a professional educator can help manage the potential sense of threat from AI in learning and teaching. Discussions focused on the "how" of effective use of AI, and the ways it can support student learning and educator practice, are always grounded in core knowledge of pedagogy and education.

On AI in assessment, it was instructive to hear student participants share a desire to be able to demonstrate learning and skills above and beyond what is captured in traditional assessment, and find different, authentic ways to engage with knowledge. Assessment is always a bit of a flashpoint in pedagogy, especially in constructing students' understanding of their learning, and there is an open question on how AI technology can support educators in assessment design and execution. More prosaically, the risks to traditional assessment from large language models indicate that staff may need to spend proportionally more of their time on managing assessment going forward.

Participants drew upon the experiences of the Covid pivot to emergency remote teaching and taking the best lessons from trialling new ways of learning and teaching as a useful reminder that the sector can pivot quickly - and well - when required. Yet the feeling that AI is often something of a "talking point" rather than an "action point" led some to suggest that there may not yet be a sufficiently pressing sense of urgency to kickstart change in practice.

What is clear about the present moment is that the sector will make the most progress on these questions when there is sharing of thinking and practice and co-development of approaches. Over the next six months we'll be building up our insight and we'd love to hear your views on what works to support educator development of AI in pedagogy. We're not expecting any silver bullets, but if you have an example of practice to share, please get in touch.
This article is published in association with Kortext. Join Debbie, Rachel and a host of other speakers at Kortext LIVE on Wednesday 11 February in London, where we'll be discussing some of our findings - find out more and book your place here.

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High quality learning means developing and upskilling educators on the pedagogy of AI

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AI has the potential to change educator practice, but it needs the right support to be in place. Debbie McVitty and Rachel Maxwell invite you to contribute to a new Wonkhe-Kortext project on educating the AI generation

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[December] The end of pretend – AI and the case for universities of formation

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[December] The end of pretend – AI and the case for universities of formation

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The end of pretend – AI and the case for universities of formation

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Jim Dickinson reflects on the end of scarcity, the rise of AI, and the challenge to a system of higher education built on pretence, performance and proxies Jim Dickinson reflects on the end of scarcity, the rise of AI, and the challenge to a system of higher education built on pretence, performance and proxies

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[December] Generative AI and the REF: closing the gap between policy and practice

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[December] Generative AI and the REF: closing the gap between policy and practice

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Generative AI and the REF: closing the gap between policy and practice - HEPI

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This blog was kindly authored by Liam Earney, Managing Director, HE and Research, Jisc. The REF-AI report, which received funding from Research England and co-authored by Jisc and Centre for Higher Education Transformations (CHET), was designed to provide evidence to help the sector prepare for the next REF. Its findings show that Generative AI is […]

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About the REF-AI project

REF-AI is a national study funded by Research England to explore the current and potential uses of Generative Artificial Intelligence (GenAI) tools in institutional preparations for REF2029. Led by Professor Richard Watermeyer (University of Bristol), with Professor Lawrie Phipps (Jisc and University of Chester), Professor Tom Crick (Swansea University and DCMS) and Rodolfo Benites (University of Bristol), the project seeks to understand how emerging generative AI technologies are impacting the preparations for REF2029.

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REF-AI is a national study funded by Research England to explore the current and potential uses of Generative Artificial Intelligence (GenAI) tools in institutional preparations for REF2029. Led by Professor Richard Watermeyer (University of Bristol), with Professor Lawrie Phipps (Jisc and University of Chester), Professor Tom Crick (Swansea University and DCMS) and Rodolfo Benites (University of Bristol), the project seeks to understand how emerging generative AI technologies are impacting the preparations for REF2029.

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Department for Science, Innovation and Technology

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This strategy sets out 15 actions government will take to cement the UK’s position as a global leader in AI-enabled science breakthroughs.

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[November] From detection to development: how universities are ethically embedding AI-for-learning

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[November] From detection to development: how universities are ethically embedding AI-for-learning

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From detection to development: how universities are ethically embedding AI-for-learning - HEPI

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The future of UK higher education rests upon the assurance of student learning outcomes. While GenAI presents the sector with immense opportunities for advancement and efficiency, the sector is constrained by an anachronistic model of plagiarism detection rooted in adversarialism. I believe the ‘Police and Punish’ model must now be replaced by ‘Support and Validate’. […]

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[November] Embedding AI, not bolting it on

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[November] Embedding AI, not bolting it on

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Over the weekend, we published a blog on the teacher training placement crisis. Today’s blog was kindly authored by Janice Kay, Director at Higher Futures, and former Provost and Senior Deputy Vice-Chancellor at the University of Exeter. We must become AI-first institutions. The Office for Students recently made a welcome intervention, encouraging universities to make […]

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[November] ‘We could have asked ChatGPT’: students fight back over course taught by AI

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[November] ‘We could have asked ChatGPT’: students fight back over course taught by AI

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‘We could have asked ChatGPT’: students fight back over course taught by AI

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Staffordshire students say signs material was AI-generated included suspicious file names and rogue voiceover accent

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[November] AI is unlocking insights from PTES to drive enhancement of the PGT experience faster than ever before

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[November] AI is unlocking insights from PTES to drive enhancement of the PGT experience faster than ever before

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AI is unlocking insights from PTES to drive enhancement of the PGT experience faster than ever before

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A new partnership between Advance HE, evasys and Student Voice AI is giving rapid access to a goldmine of insight about the PGT experience. Helena Lim explains

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•AI isn’t just about algorithms and data, it is about people.

•Collaboration across universities, students and businesses can result in a trifecta of wins.

•Building strong relationships is key to working effectively in the industry.

•Nothing new is created from Generative AI. However, the power of Predictive AI is huge because it can fix real world problems.

Social Sustainability

•AI for social sustainability needs a holistic approach (Social, Economic, Environmental).

•Social Sustainability is about building communities that are fair, inclusive, safe, secure and healthy.

•AI for social sustainability isn’t just about innovation, its about intention.

Environmental Sustainability

•AI and analytics aren’t just about efficiency but sustainability.

•When making a sustainability business proposal, think about the win-win. The initiative can be sustainable and also save them money.

•Need to make sure that the energy usage is going to be outweighed by the gain you get as an end result.

Economic Sustainability

•Goal to build a framework to help SMEs develop their AI-ready capabilities.

•Biggest problem with implementing AI at the moment is the skills gap.

•Digital divide is widening and the need for AI Literacy is increasing.

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[November] AI in Professional Services Community: November Session Review

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[November] AI in Professional Services Community: November Session Review

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Our latest AI in Professional Services Community meeting brought together institutions across the higher and further education sectors to share progress, practical approaches and emerging questions around integrating AI into day-to-day operations. The conversation reflected a clear shift: AI is no longer an abstract future trend, but an active area of development embedded in training, governance, and service design across professional services teams.

Our November session's discussion points were:

Building Meaningful Training Pathways

A key theme was the rise of tiered AI training frameworks tailored to different confidence and experience levels. Many institutions are structuring staff development into stages such as novice, explorer and practitioner, ensuring that learning pathways feel accessible and relevant. This layered approach acknowledges varying digital capabilities and helps staff adopt AI tools responsibly and effectively.

Data Awareness and GDPR at the Core

Across the community, institutions stressed the importance of helping staff understand what data can and cannot be entered into AI systems. Training is increasingly focused on data sensitivity, safeguarding, and GDPR compliance, with teams building clear guidance to prevent accidental misuse. This emphasis ensures that innovation continues with compromising data protection obligations.

Evolving Governance for Responsible AI

Rather than creating standalone AI policies, institutions are embedding AI considerations into existing frameworks such as plagiarism, data retention, and information governance policies. This integrated approach supports consistency and helps ensure that AI usage aligns with established organisational expectations around risk, ethics, and accountability.

Practical AI Applications in Professional Services

Participants shared a wide range of live use cases, from toolkits that help teams experiment with AI safely to automated processes for summarising data, reporting, and document generation. AI is also accelerating repetitive work - such as email summarisation and spreadsheet interpretation - freeing staff to focus on higher-value tasks. These examples demonstrate how AI is quietly reshaping everyday administrative workflows.

Chatbots and Internal Process Support

Interest in AI-driven chatbots continues to grow, with institutions exploring their potential for tasks like policy navigation, FAQs, and first-line IT support. Some teams are building in-house prototypes, while others are evaluating external solutions to scale more quickly. Early experiments show promising efficiency gains, though user experience design and ongoing governance remain important considerations.

AI in Recruitment: Proceeding with Care

Ethical concerns around AI-driven recruitment processes remain prominent. Participants expressed caution about using generative AI for shortlisting, interviews, or decision support, noting current limitations in transparency and fairness. The community agreed that any adoption in this area must be carefully governed and evidence based.

Collaboration and Shared Resources

A strong spirit of collaboration continues to define the Jisc community. Participants reaffirmed the value of sharing toolkits, training materials, governance models, and practical examples to accelerate collective learning. This open exchange helps institutions move faster, avoid duplication, and build on each other's progress.

Accessibility and Inclusivity as Foundations

Discussions in this session also highlighted the need to ensure AI supports, not hinders, accessibility and inclusivity. Institutions are planning dedicated workshops to explore how AI tools can enhance inclusive practice across both professional services and teaching, with particular focus on equal access and user-friendly design.

Looking Ahead

As AI adoption accelerates across professional services, our communities continue to play a vital role in supporting safe, thoughtful, and collaborate development. Going forward, we hope the community continues sharing practical resources, deepening governance approaches, and ensuring staff across all roles feel confident navigating AI-enhanced workflows. We look forward to building on this collaborative environment in upcoming sessions.

Our next meeting will be on 17th December 2025, we hope to see many of you there for our last session before the Christmas break.
Useful Links
Jisc Advice blog on AI tools in Professional Services - AI Tools: Professional Services - Artificial intelligence

Jisc blog on AI and Governance - Regulating the Future: AI and Governance - Artificial intelligence

Jisc blog on Chatbots in HE and FE - Implementing chatbots in Colleges and Universities: A Practical Guide based on learnings from the Jisc LearnWise Pilot - Artificial intelligence

Windsor Forest College's chatbot 'Winnie' - A bot named Winnie - Jisc

Scottish Tertiary Education statement on the use of GenAI - SCAITEN

University of Liverpool's guidance on GenAI - Generative Artificial Intelligence in Teaching, Learning and Assessment - Centre for Innovation in Education - University of Liverpool

Find out more by visiting our Artificial Intelligence page to view publications and resources, join us for events and discover what AI has to offer through our range of interactive online demos.

For regular updates from the team sign up to our mailing list.

Get in touch with the team directly at AI@jisc.ac.uk

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AI in Professional Services Community: November Session Review - Artificial intelligence

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Explore key insights from the AI in Professional Services Community's November session, covering practical AI applications, governance, GDPR compliance, training pathways, and collaborative strategies for higher and further education institutions.

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[November] In learning, AI must become a co-creator, not a shortcut

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[November] In learning, AI must become a co-creator, not a shortcut

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In learning, AI must become a co-creator, not a shortcut

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There are legitimate use-cases for AI that deliver efficiency, but for Nick Jennings and Sam Grogan the real prize is active development of AI learning partnership skills

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[November] Adopting AI across an institution is a pressing leadership challenge

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[November] Adopting AI across an institution is a pressing leadership challenge

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Adopting AI across an institution is a pressing leadership challenge

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Janice Kay and Rachel Maxwell set out all the elements of a whole-institution AI strategy and the leadership capabilities required to make it real

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[November 2025] AI and the Future of Universities

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[November 2025] AI and the Future of Universities

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Welcome! You are invited to join a webinar: HEPI / University of Southampton webinar on 'AI and the Future of Universities'. After registering, you will receive a confirmation email about joining the webinar.

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Join HEPI and the University of Southampton for a free webinar to mark the launch of 'AI and the Future of Universities'. This new essay collection arrives at a pivotal moment, offering a rich and timely exploration of how Artificial Intelligence is reshaping the academic landscape.

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[November] How generative AI could re-shape professional services and graduate careers

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[November] How generative AI could re-shape professional services and graduate careers

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How generative AI could re-shape professional services and graduate careers - HEPI

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Join HEPI and the University of Southampton for a webinar on Monday 10 November 2025 from 11am to 12pm to mark the launch of a new collection of essays, AI and the Future of Universities. Sign up now to hear our speakers explore the collection’s key themes and the urgent questions surrounding AI’s impact on higher education. […]

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[October] AI skills for the UK workforce

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[October] AI skills for the UK workforce

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This report analyses AI upskilling needs, barriers and opportunities across 10 growth sectors, and introduces tools to support employers and training providers.

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[October] STFC launches powerful new AI supercomputer for industry

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[October] STFC launches powerful new AI supercomputer for industry

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From new medicines to clean energy, the Mary Coombs supercomputer will help UK industry make breakthroughs through AI and advanced computing.

The Science and Technology Facilities Council's (STFC) Hartree Centre has launched a powerful new supercomputer designed to help UK industry accelerate innovation through artificial intelligence (AI) and advanced computing.

Located within STFC's new £30 million Supercomputing Centre at its Daresbury Laboratory, the system is named in honour of Mary Coombs, the UK's first female commercial programmer.

It will give businesses and public sector organisations of all sizes access to world-class computing power and expertise.

From new medicines to climate prediction, it will help UK businesses analyse vast datasets more quickly.

It will turn cutting-edge research into real-world solutions faster, more efficiently and at greater scale, boosting productivity and growth across the UK economy.

A leap in supercomputing processing power

Mary Coombs delivers 10 times the performance of its predecessor, Scafell Pike, while being more energy efficient.

A 24.41 petaflop system, it can perform 24.41 quadrillion floating-point calculations per second.

For context, carrying out one calculation per second would take nearly 773 million years to achieve the same number.

The GPU-based supercomputer is designed for AI workloads and advanced visualisation, marking a major step forward in the Hartree Centre's computing capabilities.

Accelerating discovery

The Hartree Centre at STFC's Daresbury Laboratory, at Sci-Tech Daresbury in the Liverpool City Region, is the UK's only supercomputing centre dedicated to working with industry to advance digital innovation.

Home to some of the UK's leading experts in supercomputing, AI and data science, it helps businesses turn complex challenges and ideas into innovations that benefit the economy and society.

Real-world solutions

Professor Kate Royse, Director of the STFC Hartree Centre, said:

Here at the Hartree Centre, our new Mary Coombs supercomputer can provide UK industry with the computing power, expertise and skills needed to turn ambitious ideas into real-world solutions.

From drug discovery to climate research, businesses can process vast and complex datasets faster and more efficiently than ever before, without needing in-house supercomputing or AI expertise.

By giving industry access to world-class AI and high-performance computing, and to the leading skills of our Hartree Centre scientists, Mary Coombs can help businesses innovate with confidence, accelerate research, and bring solutions to market more quickly, delivering tangible benefits for our economy and society.

A major national asset

Paul Vernon, Head of STFC's Daresbury Laboratory, said:

The launch of the STFC Supercomputing Centre and Mary Coombs marks an important milestone for UK innovation, giving businesses access to world-class tools and expertise to turn ideas into reality.

This new facility is an important national asset for research and innovation, strengthening the North West's position as a hub for advanced technology, digital skills and high-value jobs.

By giving businesses the infrastructure to explore and apply advanced digital technologies, we're helping to build a stronger, more innovative digital economy and ensuring the UK remains at the forefront of global research and development, driving growth and opportunity across the nation.

Leading the Digital Future

Steve Rotheram, Mayor of the Liverpool City Region, said:

When I speak to investors from the UK and beyond, they always sit up and pay attention when I tell them that the Liverpool City Region is at the heart of the UK's advanced computing sector.

From cutting edge AI research to breakthroughs in health innovation and climate science, the impact of the pioneering work happening at Daresbury can be felt far beyond our region, and this new supercomputer will turbocharge those capabilities even further.

A key part of the UK's digital innovation strategy

The new supercomputer forms part of the £210 million Hartree National Centre for Digital Innovation programme, a collaboration between STFC and IBM.

The programme helps UK businesses and public sector organisations explore and adopt advanced digital technologies such as AI, quantum computing and data analytics to boost productivity and growth.

The launch of the new STFC Supercomputing Centre also supports the government's AI Opportunities Action Plan.

Top image: Professor Kate Royse at the Mary Coombs supercomputer. Credit: STFC

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STFC launches powerful new AI supercomputer for industry

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From new medicines to clean energy, the Mary Coombs supercomputer will help UK industry make breakthroughs through AI and advanced computing.

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[October] Universities are embracing AI: will students get smarter or stop thinking?

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[October] Universities are embracing AI: will students get smarter or stop thinking?

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When students entered Tsinghua University in Beijing this year, one of the first representatives they met wasn't a person. Admission letters to the prestigious institution came with an invitation code to an artificial-intelligence agent. The bot is designed to answer students' questions about courses, clubs and life on campus.

The future of universities

At Ohio State University in Columbus, students this year will take compulsory AI classes as part of an initiative to ensure that all of them are 'AI fluent' by the time they graduate. And at the University of Sydney, Australia, students will take traditional, in-person tests to prove that they have learnt the required skills and not outsourced them to AI.

All this is part of a sea change sweeping through campuses as universities and students scramble to adapt to the use of generative AI tools such as ChatGPT. These tools can, within seconds, analyse complex information, answer questions and generate polished essays - some of the exact skills that universities have conventionally taught. Eighty-six per cent of university students were regularly using AI in their studies in 2024, according to one global survey - and some polls show even more. "We are seeing students become power users of these tools," says Marc Watkins, a researcher who specializes in AI and education at the University of Mississippi in Oxford.

To some, AI presents an exciting opportunity to improve education and prepare students for a rapidly changing world. Universities such as Tsinghua and Ohio State are already weaving AI into their teaching - and some studies hint that AI-powered tools can help students to learn. A randomized controlled trial involving physics undergraduates at Harvard University in Cambridge, Massachusetts, suggested that those who used a custom-built AI tutor learnt more in less time than did students who were taught by humans alone.

But many education specialists are deeply concerned about the explosion of AI on campuses: they fear that AI tools are impeding learning because they're so new that teachers and students are struggling to use them well. Faculty members also worry that students are using AI to short-cut their way through assignments and tests, and some research hints that offloading mental work in this way can stifle independent, critical thought.

And some academics are angry that universities are allowing or even encouraging the use of AI tools - many of which are controlled by companies, have unknown cognitive impacts and are dogged by ethical and environmental concerns. An open letter released in June by scholars objecting to the uncritical adoption of AI technologies by universities quickly racked up more than 1,000 signatures from academics worldwide (see go.nature.com/4qpkygs). "Our funding must not be misspent on profit-making companies, which offer little in return and actively de-skill our students," it reads.

There is one point of agreement among both enthusiasts and sceptics: the future has already arrived. "The rate of adoption of various generative AI tools by students and faculty across the world has been accelerating too fast for institutional policies, pedagogies and ethics to keep up," says Shafika Isaacs, chief of section for technology and AI in education at the United Nations cultural organization UNESCO, in Paris.

The AI generation

Universities have been adapting to new technologies for decades - from computers and the Internet to online courses. But generative AI tools present a fundamental challenge, researchers say. Not only can they quickly perform some of the core tasks that universities teach students, but they have spread and are changing at pace. "The thing that is different about AI to every other technology is the speed at which it moves," says Sue Attewell, head of AI at Jisc, a non-profit digital and data body for UK higher education, headquartered in Bristol.

Surveys around the world show that students are quickly incorporating AI into their lives. "The number of users who are essentially young teens is just astronomically high," says Watkins. "You're talking hundreds of millions of users worldwide below the age of 24." And science students seem to be early adopters. This year, the AI firm Anthropic, in San Francisco, California, analysed one million anonymized conversations between university students and its generative AI, Claude. It found that students in science, technology, engineering and mathematics were using it disproportionately more than were those in areas such as business and humanities.

But what are students using AI for? In a survey of more than 1,000 UK students by the Higher Education Policy Institute, a think tank in Oxford, UK, most were, unsurprisingly, using AI tools to write, improve or summarize text (see 'How university students use AI'). Nearly 90% of students said they had used generative AI for assessments such as exams or coursework. Most (58%) said they used it to explain concepts in the assessment, 25% used AI-generated text after editing it first and 8% admitted they'd used raw AI-written text.

Attewell, who has conducted student focus groups on AI, says many students are using the tools to streamline their studies and become more efficient, not to cheat. And many just want guidance on how to boost their learning with AI, she says. Students might "know how to use [AI] brilliantly on social media or for their social life, but it doesn't mean that they know how to use it for academic use".

This, however, is where many universities are falling short. Institutions around the world have been scrambling to introduce policies for staff and students on the best ways to use AI - but with mixed results. On US campuses, "it's been chaotic for the past two and a half years, and I don't think it's going to change anytime soon," Watkins says. That's because many institutions allow individual faculty members to decide whether and how AI can be used in their courses, he says, which leaves some students confused. "We might have a freshman student taking five classes, and they will be exposed to five different AI policies."

In Australia, by contrast, there has been a strong, nationally coordinated response, educators say, in part because the Tertiary Education Quality and Standards Agency has worked to develop guidelines with universities since 2023. And in China, "the integration of AI into universities is not optional, but an integral part of national strategy," says Ronghuai Huang, co-dean of the Smart Learning Institute at Beijing Normal University.

Data and anecdote suggest that faculty members are embracing AI more cautiously than students are. Some staff are using AI tools to automate their own work, such as by helping to draft feedback on assignments, say researchers, whereas others are rejecting it outright. A survey this year of more than 1,600 faculty members from 28 countries by the Digital Education Council, an organization focused on technology in higher education, found that around 60% were using AI tools in teaching - less than the proportion of students using them. However, 80% said their institution had not made it clear how to use AI in their teaching. "Faculty use is less sophisticated than student use," says George Siemens, who studies technology and education at the University of South Australia in Adelaide.

Meanwhile, big technology companies such as OpenAI and Google have been pitching their products to students and universities. "We're inundated by companies wanting to partner with us," says Ravi Bellamkonda, Ohio State University's executive vice-president and provost. Last year, OpenAI introduced ChatGPT Edu, a version of the popular chatbot tailored for universities. This February, when the California State University system announced it was giving its 520,000 students and faculty members access to that tool, it became "the largest implementation of ChatGPT by any single organization or company anywhere in the world", according to OpenAI. In August, Google announced it was making its most advanced AI tools available for free to students aged over 18 in the United States and in a handful of other countries.

Education specialists say that tech firms are motivated by commercial interests and the opportunity to embed their AI systems into the lives of millions of young users. Universities that strike agreements with tech firms typically get campus-wide access to the latest AI models and data protection, meaning that student and faculty data and academic work are not fed into an AI training model.

Some universities are grabbing the AI bull by the horns. Danny Liu, a biologist who specializes in educational technology at the University of Sydney, was one of the first to develop a generative AI platform tailored to higher education. Called Cogniti and developed in 2023, it is now embedded in the university's digital learning platform and allows teachers to design custom AI agents for their courses. This might be an AI tutor for a science module, or an AI tool that expands brief marking comments into more specific student feedback. Cogniti is being used by more than 1,000 educators at his university, Liu says, and has been shared with more than 100 universities worldwide.

At Tsinghua University, academics also quickly started experimenting with ChatGPT after its release in November 2022, says Shuaiguo Wang, who directs the Center for Online Education there. Within a year, about 100 different AI teaching assistants had sprung up, and the university sought a more systematic approach. It also didn't want to be reliant on any one AI model - such as the large language models (LLMs) that underlie ChatGPT and related tools - because "a new model emerges almost every day", Wang says. Another key goal was to correct hallucinations, the inaccurate content that LLMs sometimes spout.

In April last year, the university led the development of a central, three-layer 'architecture' to incorporate AI into teaching. The bottom layer is where AI models are plugged in: the system draws on about 30 models from companies including DeepSeek, Alibaba Cloud, OpenAI and Google, Wang says. The middle layer contains 'knowledge engines' that hold accurate, up-to-date information for different academic fields. The top layer consists of various student-facing platforms, including an educational one that hosts teaching assistants, as well as the AI bot to help students starting at Tsinghua.

As part of this system, for instance, students can review slides after a class and click a button saying 'I'm confused'. This allows them to ask a pop-up AI agent questions. The intermediary layer analyses the question and directs it to the appropriate AI model, as well as searching its own databases; it then compiles the information to provide an accurate answer. Wang says the system has been adopted by hundreds of universities across China.

This year, Wang is changing tack and launching studies to ask a crucial and outstanding question: do students using AI tools actually learn more? Early results - as yet unpublished - have raised some red flags. Wang and his colleagues found that students who use an AI tutor often score higher in tests immediately after a class than do those who don't use the agent. But two to three weeks later, the AI users scored lower than their peers. "Students might have a false sense of understanding," he says.

Classroom concerns

Wang is not the only one worried about the impact of AI on students' learning - as Nataliya Kosmyna discovered when, in June, she and her colleagues released a preprint suggesting that students who use ChatGPT to help write an essay use less of their brains than do students who write it on their own. She has since received some 4,000 e-mails from school and university teachers worldwide who are concerned that the result reflects what is happening to students in their classrooms who are using AI. In a lot of cases, "they actually sound very desperate", she says.

Kosmyna, a research scientist at the Massachusetts Institute of Technology in Cambridge, and her team used electro-encephalography (EEG) to study the patterns of electrical activity in 54 students' brains as they wrote 20-minute essays on questions such as "Do works of art have the power to change people's lives?" The students were split into three groups: one with access to an LLM; another that could use websites and search engines but not LLMs; and a third that relied only on their own knowledge. The team used EEG to measure 'connectivity' between brain regions - the extent to which areas were communicating with each other.

Students using only their brains showed the strongest and widest pattern of connectivity and were usually able to later recall what they'd written. The LLM-using group showed the weakest connectivity - suggesting that there was less cross-brain communication - and could sometimes barely remember a word of their prose.

Kosmyna acknowledges that the study involved only a small number of students and did not look at the long-term impacts of using AI, among other limitations. (She also found it ironic that some commentators misinterpreted the study because they used AI to generate incorrect summaries of her 200-page preprint.) But the study fuelled a widespread concern that students who rely on AI in their studies are failing to develop valuable skills such as critical thinking - the ability to analyse and critique information and to reach considered conclusions. Anthropic's analysis of one million student-AI conversations in Claude showed that nearly half of them involved individuals seeking direct answers or content with little engagement on their part.

Olivia Guest, a computational cognitive scientist at Radboud University Nijmegen in the Netherlands, who led the writing of the June protest letter, says she has observed related problems with students in the past couple of years. "Their skills have dramatically changed," she says: some struggle to reference a source or write an essay.

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Universities are embracing AI: will students get smarter or stop thinking?

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Millions of students arriving at campuses are now using artificial intelligence. Worries abound. Millions of students arriving at campuses are now using artificial intelligence. Worries abound.

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[October] Don’t use AI to over-profile students, warns Spence

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[October] Don’t use AI to over-profile students, warns Spence

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Don’t use AI to over-profile students, warns Spence

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Promises of more personalised education may stifle students’ ability to grow and change, according to UCL provost

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[October] AI could ‘help democratise access to science careers’

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[October] AI could ‘help democratise access to science careers’

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AI could ‘help democratise access to science careers’

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Technology can be used to offer vital research experience and create inclusive curricula, according to physicist Jess Wade

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[October] Building the transatlantic cyber bridge

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[October] Building the transatlantic cyber bridge

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This blog was kindly authored by Professor Paul Marshall, Vice-President (Global Campus) and Pro-Vice-Chancellor (Careers & Enterprise), University of East London.
As the UK Government prepares its long-awaited White Paper on the future of higher education, it is timely to reflect on the purpose and impact of our universities. At their best, they are not simply sites of knowledge creation - they are instruments of national capability. Few challenges illustrate that more vividly than cybersecurity.

When I joined a panel at the CyberBay Cybersecurity Conference in Tampa earlier this month, Dr Richard Munassi, Managing Director of Tampa Bay Wave, opened with a warning that set the tone for the discussion:

We are in a cyber war - a war waged by well-financed, state-backed criminal organisations so sophisticated that they have their own HR divisions.

He was right. Earlier this year, Jaguar Land Rover was forced to suspend production after a ransomware attack that rippled across its global supply chain. The UK Government's intervention - with a support package approaching £1.5 billion - made clear that cybersecurity is not an IT issue; it is economic infrastructure.

As the sector awaits the Government's vision for the future, one truth already stands out: higher education must not only prepare individuals for work - it must prepare the nation for risk.

At the University of East London (UEL), that challenge sits at the heart of our institutional strategy, Vision 2028, which seeks to transform lives through education, innovation, and enterprise. The strategy's organising principle - Careers-First - redefines employability as capability.

Rather than positioning careers as an outcome of study, it embeds professional practice, enterprise, and resilience into every degree and partnership. The test for every programme is simple: does it equip our students to adapt, contribute, and lead in industries defined by constant change?

Nowhere is this approach more tangible than in cybersecurity. Our BSc Cyber Security & Networks, MSc Information Security & Digital Forensics, and Cyber Security Technical Professional Degree Apprenticeship all combine rigorous academic study with live, industry-based application.

Students work directly with BT, IBM, Fujitsu, and Ford, tackling real-time challenges in threat analysis, data forensics, and network defence. By the time they graduate, they are not simply work-ready - they are work-proven, having contributed to the resilience of the very sectors they will soon join.

The results speak for themselves:

With Siemens UK, students tested firmware vulnerabilities in industrial systems, informing Siemens' internal training programmes.

With Barclays Eagle Labs, they created a fraud-analysis dashboard now in pilot testing.

With NHS Digital, they developed a ransomware-simulation tool to train hospital teams in incident response.

Each collaboration demonstrates a single idea: learning is most powerful when it changes the world beyond the classroom.

UEL's Institute for Connected Communities (ICC), led by Professor Julia Davidson OBE, anchors this model in research excellence and policy leadership. The ICC brings together computing, criminology, psychology, and social science to examine the human, technical, and organisational dimensions of online safety.

Its research informs the UK Council for Internet Safety, Ofcom, UNICEF, and multiple international governments. Through projects such as Global Kids Online, ICC research directly shapes teaching, ensuring that our graduates understand not only how to secure systems, but why digital trust matters to society.

As policymakers consider the future role of universities in the forthcoming White Paper, the ICC already provides a working example of how academic research translates into practical and regulatory impact.

The White Paper will also need to consider how global collaboration strengthens national capability. UEL's Global Campus model demonstrates how this can work in practice - connecting students and employers across India, Greece, Egypt, and the United States to create shared pathways for study, innovation, and employment.

Our developing partnership with Tampa Bay Wave, framed within the UK-Florida Memorandum of Understanding (2023), offers one illustration. We are building both virtual and physical experiences that will enable UEL students to engage with Florida's growing cybersecurity and fintech ecosystem through mentoring, live projects, and placements, while providing a London base for US start-ups entering the UK market.

A genuine transatlantic bridge is being constructed - designed for movement in both directions, connecting students, researchers, and entrepreneurs to co-create secure-by-design technologies and governance frameworks. It is the Careers-First model, scaled globally.

The next phase of cybersecurity will occur where AI, data, and physical systems converge. Attacks will target intelligent infrastructure - transport grids, hospitals and manufacturing. UEL is already embedding these challenges into its curriculum, guided by ICC research. Students design adversarial-AI tests, examine supply-chain vulnerabilities, and develop frameworks for organisational resilience.

This approach recognises that technology evolves faster than any static syllabus. Students are therefore treated as co-creators, working alongside academics and employers to design the solutions industry will need next.

As the UK Government prepares its White Paper, one principle should underpin the national conversation: universities are not peripheral to resilience - they are central to it. They educate the workforce, generate the research, and sustain the partnerships that keep the nation secure.

UEL's Careers-First model, aligned to Vision 2028, embodies that principle. It fuses employability, enterprise, and global engagement into one coherent system of capability. Our collaboration with Tampa Bay Wave is a single, tangible expression of this - connecting East London's lecture theatres to innovation ecosystems across the Atlantic.

In a global cyber war, the question is not whether universities should respond, but how fast they can. At UEL, that response is already underway - this is what Careers-First looks like.

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WEEKEND READING: Building the transatlantic cyber bridge: what 'Careers-First' really means for the future workforce - HEPI

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This blog was kindly authored by Professor Paul Marshall, Vice-President (Global Campus) and Pro-Vice-Chancellor (Careers & Enterprise), University of East London. As the UK Government prepares its long-awaited White Paper on the future of higher education, it is timely to reflect on the purpose and impact of our universities.

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[October] Low-quality and AI-generated papers ‘could overwhelm publishing’

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[October] Low-quality and AI-generated papers ‘could overwhelm publishing’

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Low-quality and AI-generated papers ‘could overwhelm publishing’

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Survey of more than 3,000 researchers by Cambridge University Press reveals widespread concerns over the rise of ChatGPT-assisted papers

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[October] ‘Painful job losses likely’ as AI fills university admin roles

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[October] ‘Painful job losses likely’ as AI fills university admin roles

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‘Painful job losses likely’ as AI fills university admin roles

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Institutions should retrain staff affected now rather than ‘sustain jobs which machines can do better and cheaper’, expert argues

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[October] AI and the Future of Universities

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[October] AI and the Future of Universities

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AI and the Future of Universities - HEPI

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A new collection of essays, AI and the Future of Universities (HEPI Report 193) edited by Dr Giles Carden and Josh Freeman, brings together leading voices from universities, industry and policy. The collection comes at a point when Artificial Intelligence (AI) is projected to have a profound and transformative impact on virtually every sector of society and […]

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[October] What the data says about the UK's AI sector

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[October] What the data says about the UK's AI sector

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3 key facts - what the data says about the UK AI sector - The Data City

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But how big is the UK AI sector? Who owns the companies? And where in the UK are these companies based? The Data City’s data has the answers.

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Slowing Down AI in Higher Education - HEPI

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This blog was kindly authored by Sam Illingworth, Professor of Creative Pedagogies at Edinburgh Napier University. Debates about Artificial Intelligence (AI) in higher education tend to fall into two extremes. On one side, the snake-oil salespeople promise it will save us: automated tutors, frictionless research, instant grading. On the other hand, the doomers say it […]

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The Universities UK Annual Conference always serves as a vital barometer for the higher education sector, and this year, few topics were as prominent as the role of Generative Artificial Intelligence (GenAI). A packed session, Ethical AI in Higher Education for improving learning outcomes: A policy and leadership discussion, provided a refreshing and pragmatic perspective, moving the conversation beyond academic integrity fears and towards genuine educational innovation.

Based on early findings from new independent research commissioned by Studiosity, the session's panellists offered crucial insights and a clear path forward.

A new focus: from policing to pedagogy

For months, the discussion around Gen-AI has been dominated by concerns over academic misconduct and the development of detection tools. However, as HEPI Director Nick Hillman OBE highlighted, this new report takes a different tack. Its unique focus is on how AI can support active learning, rather than just how students are using it.

The findings, presented by independent researcher Rebecca Mace, show a direct correlation between the ethical use of AI for learning and improved student attainment and retention. Crucially, these positive effects were particularly noticeable among students often described as 'non-traditional'. This reframes the conversation, positioning AI not as a threat to learning but as a powerful tool to enhance it, especially for those who need it most.

The analogy that works

The ferocious pace of AI's introduction to the sector has undoubtedly caught many off guard. Professor Marc Griffiths, Pro-Vice Chancellor for Regional Partnerships, Engagement & Innovation at UWE Bristol, acknowledged this head-on, advocating for a dual approach of governance and '​​​​sand-boxing' (the security practice of isolating and testing to make sure an application, system or platform is safe) of new technologies. Instead of simply denying access, he argued, we must test new tools and develop clear guardrails for their use.

In a welcome departure from ​​​​​​​​the widely used but ultimately flawed calculator analogy (​​read more here Generative AI is not a 'calculator for words'. 5 reasons why this idea is misleading), Professor Griffiths offered a more fitting one: the overhead projector. Like PowerPoint today, the projector was a new technology that was a conduit for content, but it never replaced the core act of teaching and learning itself. AI, he posited, is simply another conduit. It is what we put into it, and what we get out of it, that matters.

Evidenced insights and reframing the conversation

The panel also grappled with the core questions leaders must ask themselves. Stephanie Harris, Director of Policy at Universities UK posed two fundamental challenges:

How can I safeguard my key product that I am offering to students?

​​​​​How can I prepare my students for the workforce if I don't yet know how AI will be used in the future?

She stressed the importance of protecting the integrity of the educational experience to prevent an 'erosion of trust' between students and institutions. In response to the second question, both Steph and Marc emphasised the answer lies not in specific tech skills, but in timeless critical thinking skills that will prepare students not just for the next three years, but for the next 15. The conversation also touched upon the need for universities to consider students under 16 as the future pipeline, ensuring our policies and frameworks are future-proof. Steph mentioned further prompts for leaders to think about as listed in a UUK-authored, OfS blog Embracing innovation in high education: our approach to artificial intelligence- which was given a commonsense shorthand by Steph as 'have fun, don't be stupid!'.

The session drove home the importance of evidence-based insights. Dr David Pike, Head of Digital Learning at the University of Bedfordshire, shared key findings from his own research comparing student outcomes for Studiosity users versus those of non-Studiosity users, stating that the results were 'very clear' that students did improve at scale. He provided powerful data showing significant measurable academic progress, along with a large positive correlational impact on retention and progression. Dr. Pike concluded that, given this demonstrated positive impact, we should be calling the technology 'Assisted Intelligence,' because when used correctly, that is exactly what it is.

A guiding framework of values

Academic integrity: Supporting learning, not replacing it.

Equity of access: Addressing the real challenge of paywalls.

Transparency: Clearly communicating how students will be supported.

Ethical Responsibility

Empowerment and Capability Building

To navigate this new landscape, Professor Griffiths laid out seven core values that must underpin institutional policy on AI:

These values offer a robust framework for leaders looking to create policies that are both consistent and fair, ensuring that AI use aligns with a university's mission.

The policy challenge of digital inequality

The issue of equity of access was explored in greater detail by Nick Hillman, who connected the digital divide to the broader student funding landscape. He pointed out that no government had commissioned a proper review on the actual cost of being a student since 1958. With modern student life costing upwards of £20,000 annually if a student wants to involve themselves fully in student life. He made a powerful case for increased maintenance support to match an increased tuition fee, which would also help prevent further disparity between those who can afford premium tech tools and those who cannot. This highlights that addressing digital inequality is not just a technical challenge; it is a fundamental policy one too.

In closing

The session's core message was clear: while the rise of AI has been rapid, the sector's response does not have to be only reactive. By embracing a proactive, values-led approach that prioritises ethical development, equity and human-centric learning, universities can turn what was once seen as a threat into a powerful catalyst for positive change.

Studiosity is AI-for-Learning, not corrections - to scale student success, empower educators, and improve retention with a proven , while ensuring integrity and reducing institutional risk.

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From Detection to Development: How Universities Are Ethically Embedding AI for Learning - HEPI

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This HEPI blog was authored by Isabelle Bambury, Managing Director UK and Europe at Studiosity, a HEPI Partner. The Universities UK Annual Conference always serves as a vital barometer for the higher education sector, and this year, few topics were as prominent as the role of Generative Artificial Intelligence (GenAI).

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[September] When AI Meets Engineering Education

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[September] When AI Meets Engineering Education

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This HEPI blog was kindly authored by James Atuonwu, Assistant Professor at the New Model Institute for Technology and Engineering (NMITE).

Where machines of the past multiplied the strength of our hands, AI multiplies the power of our minds - drawing on the knowledge of all history, bounded only by its training data.

We are living through a moment of profound transition. The steam engine redefined labour, the computer redefined calculation, and now AI is redefining thought itself. Unlike earlier technologies that multiplied individual workers' power, AI, particularly large language models (LLMs), multiplies the collective intelligence of humanity.

For engineering practice and universities alike, this shift is existential.

AI as Servant, Not Master

The old adage is apt: AI is a very good servant, but a very bad master.

As a servant, AI supports engineers in simulation, design exploration, and predictive maintenance. For students, it provides on-demand access to resources, enables rapid testing of ideas, and helps them reframe problems.

As a master, AI risks entrenching bias, undermining judgment, and reshaping educational systems around efficiency rather than values.

The challenge is not whether AI will change engineering education, but whether we can train engineers who command AI wisely, rather than being commanded by it.

This logic resonates with the emerging vision of Industry 5.0: a paradigm where technology is designed not to replace humans, but to collaborate with them, enhance their creativity and serve societal needs. If Industry 4.0 was about automation and efficiency, Industry 5.0 is about restoring human agency, ethics, and resilience at the heart of engineering practice. In this sense, AI in engineering education is not just a technical challenge, but a cultural one: how do we prepare engineers to thrive as co-creators with intelligent systems, rather than their servants

Beyond 'AI Will Take Your Job'

The phrase 'AI won't take your job, but a person using AI will' has become a cliché. It captures the competitive edge of AI literacy but misses the deeper truth: AI reshapes the jobs themselves.

In engineering practice, repetitive calculations, drafting, and coding are already being automated. What remains - and grows in importance - are those tasks requiring creativity, ethical judgment, interdisciplinary reasoning, and decision-making under uncertainty. Engineering workflows are being reorganised around AI-enabled systems, rather than human bottlenecks.

Universities, therefore, face a central question: Are we preparing students merely to compete with each other using AI, or to thrive in a world where the very structure of engineering work has changed?

Rethinking Assessment

This question leads directly to assessment - perhaps the most urgent pressure point for universities in the age of AI.

Assessment must shift from recalling knowledge to demonstrating judgment.

Students should be evaluated on their ability to frame problems, critique AI-generated answers, work with incomplete data, and integrate ethical, social, and environmental perspectives.

If LLMs can generate essays, solve textbook problems, and produce 'good enough' designs, then traditional forms of assessment risk becoming obsolete. Yet, this is an opportunity, not just a threat.

A further challenge lies in the generational difference in how AI is encountered. Mature scholars and professionals, who developed their intellectual depth before AI, can often lead AI, using it as a servant, because they already possess the breadth and critical capacity to judge its outputs. But students entering higher education today face a different reality: they arrive at a time when the horse has already bolted. Without prior habits of deep engagement and cognitive struggle, there is a danger that learners will be led by AI rather than leading it.

This is why universities cannot afford to treat AI as a mere technical add-on. They must actively design curricula and assessments that force students to wrestle with complexity, ambiguity, and values - to cultivate the intellectual independence required to keep AI in its rightful place: a servant, not a master.

Rediscovering Values and Ethics

Literature and history develop narrative imagination, allowing engineers to consider the human stories behind data.

Philosophy and ethics cultivate moral reasoning, helping engineers weigh competing goods.

Social sciences illuminate the systems in which technologies operate, from environmental feedback loops to economic inequities.

AI forces a rediscovery of what makes us human. If algorithms can generate correct answers, then the distinctive contribution of engineers lies not only in technical mastery but in judgment grounded in values, ethics, and social responsibility.

Challenge-based learning, replacing rote lectures with inquiry-driven engagement in authentic problems.

Industry and community co-designed projects, giving students opportunities to apply knowledge in practical contexts

Interdisciplinary integration across engineering, business, and social perspectives.

Block learning, enabling sustained immersion in complex challenges - a counterbalance to the fragmenting tendencies of AI-enabled multitasking.

Professional skills and civic engagement, preparing graduates to collaborate effectively with both people and intelligent systems.

Assessment through projects and portfolios, rather than traditional exams, pushing learners to demonstrate the judgment, creativity, teamwork and contextual awareness that AI can only imitate but not authentically embody.

Here the liberal arts are not a luxury, but a necessity.

In this light, AI does not diminish the need for a broad education - it intensifies it.

Reimagining the University

Yet, values alone are not enough. If universities are to remain relevant in the AI era, they must reimagine their structures of teaching, learning, and assessment. Several approaches stand out as particularly future-proof:

These approaches anticipate what the AI era now demands of universities: to become sites of creation, collaboration, and critique, not simply repositories of content that AI can reproduce at scale. Some newer institutions, such as NMITE, have already experimented with many of these practices, offering a glimpse of how higher education can be reimagined for an AI-enabled world.

Closing Reflection

AI may be the greatest machine humanity has ever built - not because it moves steel, but because it moves minds. Yet, with that power comes a reckoning.

Do we let AI master our universities, eroding integrity?
Or do we make it serve as a co-creator, multiplier of human intelligence, and a tool for cultivating wise, ethical, creative engineers?

The answer will define not just the future of engineering training and practice, but the very shape of university education itself.

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When AI Meets Engineering Education: Rethinking the University - HEPI

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This HEPI blog was kindly authored by James Atuonwu, Assistant Professor at the New Model Institute for Technology and Engineering (NMITE). Where machines of the past multiplied the strength of our hands, AI multiplies the power of our minds - drawing on the knowledge of all history, bounded only by its training data.

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Thousands of jobs on the horizon with new training and apprenticeship routes expected to be created as new taskforce announced.

Universities across the region, skills providers and industry leaders to fast-track planning, investment and site development for the North East's AI Growth Zone.

Spades expected in the ground within weeks as Technology Secretary brings together local businesses to spearhead efforts and drive growth through Plan for Change.

Thousands of jobs are set to be unlocked as a powerhouse coalition including British AI firm Nscale, local universities and leading skills providers join forces to supercharge planning, new opportunities and the build of the North East's new AI Growth Zone.

With new roles expected including in construction through to energy to cutting-edge AI - the new taskforce will work hand-in-hand with local organisations to create training and apprenticeship routes that ensure the benefits are felt across the region.

With members spanning the AI sector - from data centre builders to local training providers and world-class researchers - the taskforce kicks off work today. Technology Secretary Liz Kendall and North East Mayor Kim McGuinness will later today bring together businesses to focus minds on getting sites in Cambois near Blyth and Cobalt Park up and running fast.

Spades are expected in the ground within weeks, with the taskforce laser-focused on unblocking planning issues, securing further investment and ensuring energy access.

Today, the Technology Secretary met apprentices already working at Cobalt Park to hear first-hand how the AI Growth Zone is creating opportunities and boosting growth as part of the government's Plan for Change.

Technology Secretary Liz Kendall said:

The North East is once again leading the charge - this time in the AI revolution.

We're backing local talent with the skills and training they need to thrive in the jobs of tomorrow.

From laying the physical foundations and building data centres to launching careers in AI, this Growth Zone is about creating real opportunities for people right across the region.

North East Mayor Kim McGuinness said:

We know AI can be transformational for people and the local economy. This investment will mean thousands of jobs for local people and the North East leading innovations in tech.

Now we need to work with businesses to develop the talent right here, and make sure the benefits of AI reach the people of the region. We've already mobilised a taskforce of business leaders, industry experts and skills providers to deliver exactly that.

I want kids in school here today to see their place in an AI-driven future, and this is how we make sure it provides a new future for our young people.

The Taskforce will initially prioritise getting the AI Growth Zone up and running, before maximising the impact of AI for the local economy, jobs and opportunities for young people.

The North East - birthplace of the industrial revolution and home to a wealth of leading universities including Newcastle, Durham, Sunderland and Northumbria - is set to lead the UK's AI revolution.

The UK government has designated the region as an  AI Growth Zone, unlocking up to £30 billion in investment to transform its tech landscape, create long-term AI careers, and boost public services like healthcare.

Sage Chief of Staff Ben Aung said:

Today's visit is a vote of confidence in the North East - our new AI Growth Zone puts the region's innovation on the map and, with early careers in focus, will inspire and upskill the next generation of AI talent.

To turn that potential into tangible results, businesses and our colleges and universities must work more closely than ever - aligning curricula with real roles, sharing expertise and opening pathways into industry.

From our global Cobalt Park HQ, Sage is turning that momentum into practical, trusted AI for small businesses - freeing people from time-sapping admin so the whole region can be more productive and prosper.

With access to the UK's largest source of low-carbon energy, the North East is primed for sustainable AI deployment.

Blackstone has already committed £10 billion to the Cambois near Blyth site, with the new Growth Zone status further boosting opportunities for an extra £20 billion from future partners.

Meanwhile, Nscale, OpenAI and NVIDIA are launching Stargate UK - a sovereign AI infrastructure platform. Phase one will see up to 8,000 NVIDIA GPUs deployed early next year, with the potential to scale to 31,000 NVIDIA GPUs  over time, with key sites including Cobalt Park in the North East.

Guy Marsden, Director of Cobalt Park said:

Cobalt Park is proud to be part of the North East AI Growth Zone and to host part of the Stargate UK project with OpenAI, Nscale and NVIDIA.

As one of the UK's largest out-of-town business parks, we bring together ready-to-use data centres, direct subsea fibre connections, 2.2 million square feet of office space and a clear pathway to expand power to around 500 MVA.

This unique mix means AI companies can start here today and scale for the future. This is a landmark milestone for our region, unlocking jobs, investment and innovation, and we look forward to working with the government and our partners to deliver it.

This partnership will supercharge AI adoption, transform public services, and drive economic growth - supporting the government's Plan for Change.

It follows a landmark UK-US deal on AI, Quantum and Nuclear technologies, announced by the Prime Minister and President Trump, to boost jobs and innovation.

As part of this, X-Energy and Centrica announced plans to help power the UK's clean energy future - starting with up to 12 advanced modular reactors in Hartlepool. The project could supply power to up to 1.5 million homes, generate £12 billion in value for the North East and create around 2,500 high-quality jobs in another major boost for the area.

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North East AI Growth Zone taskforce launched to accelerate jobs, skills and growth

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Thousands of jobs on the horizon with new training and apprenticeship routes expected to be created as new taskforce announced.

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[September] North East England boost in AI Investment

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[September] North East England boost in AI Investment

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Department for Science, Innovation and Technology

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North East England set for billions in investment and thousands of jobs as UK and US ink tech partnership

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The government has announced the creation of an AI Growth Zone in the North East, set to generate more than 5,000 new jobs and attract up to £30 billion in private investment.

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[September] It is going to be a life skill.. Educators on AI

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[September] It is going to be a life skill.. Educators on AI

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‘It’s going to be a life skill’: educators discuss the impact of AI on university education

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Artificial intelligence is changing how students learn and the world they’ll graduate into. Experts reveal how applicants can get ahead

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[September] We cannot address the AI challenge by acting as though assessment is a standalone activity

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[September] We cannot address the AI challenge by acting as though assessment is a standalone activity

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We cannot address the AI challenge by acting as though assessment is a standalone activity

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Rather than an assessment arms race, Duna Sabri calls for a response to artificial intelligence grounded in subject knowledge Rather than an assessment arms race, Duna Sabri calls for a response to artificial intelligence grounded in subject knowledge

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[September] Nvidia and OpenAI Investment in UK

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[September] Nvidia and OpenAI Investment in UK

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A required part of this site couldn’t load. This may be due to a browser extension, network issues, or browser settings. Please check your connection, disable any ad blockers, or try using a different browser.

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[September] Pause for REFlection: Time to review the role of generative AI in REF2029

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[September] Pause for REFlection: Time to review the role of generative AI in REF2029

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Pause for REFlection: Time to review the role of generative AI in REF2029 - HEPI

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For as long as there has been national research assessment exercises (REF, RAE or otherwise), there have been efforts to improve the way with which research is evaluated and Quality Related (QR) research funding consequently distributed. Where REF2014 stands out for its introduction of impact as a measure of what counts as research excellence, for […]

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UN report on AI to launch at global conference in Bradford

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UN report on AI to launch at global conference in Bradford

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UN report on AI to launch at global conference in Bradford - 2025 - News - University of Bradford

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A major new United Nations report calling for artificial intelligence (AI) to be used to support people, not take their place, will be launched at a global conference hosted by the University of Bradford.

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[September] Shaping AI design through the humanities

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[September] Shaping AI design through the humanities

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Drew HemmentProfessor of Data Arts and Society at The University of Edinburgh

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Shaping AI design through the humanities

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A new research funding opportunity for interpretive technologies aims to harness the humanities to overcome the limitations of existing AI technologies.

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[AUGUST] Turing turmoil: UK’s hope to lead world in AI is at war with itself

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[AUGUST] Turing turmoil: UK’s hope to lead world in AI is at war with itself

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Turing turmoil: UK’s hope to lead world in AI is at war w...

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Ministers are worried, partners are mulling legal action, and top managers are accused of losing their grip

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[AUGUST] Co-curricular space is where students can find human experience outside the AI bubble

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[AUGUST] Co-curricular space is where students can find human experience outside the AI bubble

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Co-curricular space is where students can find human experience outside the AI bubble

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In an AI-saturated digital landscape, co-curricular spaces where students can learn by doing are more important than ever, argues John Lean

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[AUGUST] Student Wellbeing in the AI Era: Stress, Confidence, and Connection – A Global Snapshot

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[AUGUST] Student Wellbeing in the AI Era: Stress, Confidence, and Connection – A Global Snapshot

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Student Wellbeing in the AI Era: Stress, Confidence, and Connection – A Global Snapshot - HEPI

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Studiosity’s ninth annual Student Wellbeing Survey, conducted by YouGov in November 2024, gathered insights from university students on their experiences and concerns, and made recommendations to senior leaders. This global research included panels from Australia, Canada, New Zealand, Saudi Arabia, Singapore, the UAE, the UK and the US (see below for the country sample size […]

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[AUGUST] America’s plan to use AI as a political weapon

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[AUGUST] America’s plan to use AI as a political weapon

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America’s plan to use AI as a political weapon

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The US blueprint for AI dominance is a global gamechanger that explains why the Silicon Valley crowd turned against the Democrats

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Connect with Mark Sewards, your AI assistant, anytime.

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[AUGUST] Harnessing AI to advance translational research and impact

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[AUGUST] Harnessing AI to advance translational research and impact

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Harnessing AI to advance translational research and impact - HEPI

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In July, HEPI, with support from the publisher Taylor & Francis, hosted a roundtable dinner to discuss harnessing AI to advance translational research and impact. This blog considers some of the themes that emerged from the discussion Travel through a major railway station in the near future and you may see, alongside the boards giving […]

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[JULY] ChatGPT ‘study mode’ feature aims to encourage critical thinking

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[JULY] ChatGPT ‘study mode’ feature aims to encourage critical thinking

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ChatGPT ‘study mode’ feature aims to encourage critical thinking

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OpenAI builds tool to combat student reliance on AI answers, but insists that cheating requires ‘holistic’ approach from universities and tech firms

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[JULY] Careers services can help students avoid making decisions based on AI fears

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[JULY] Careers services can help students avoid making decisions based on AI fears

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Careers services can help students avoid making decisions based on AI fears

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There’s evidence that artificial intelligence is causing students and graduates to switch up career choices. Chris Rea and Kirstin Burke think about automation anxiety and how to move past it There’s evidence that artificial intelligence is causing students and graduates to switch up career choices. Chris Rea and Kirstin Burke think about automation anxiety and how to move past it

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[JULY] Universities need to reckon with how AI is being used in professional practice

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[JULY] Universities need to reckon with how AI is being used in professional practice

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Universities need to reckon with how AI is being used in professional practice

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Artificial intelligence is upending many different areas of work. As Simon Sneddon explains, this has big consequences for those involved in teaching, assessing and accrediting future professionals Artificial intelligence is upending many different areas of work. As Simon Sneddon explains, this has big consequences for those involved in teaching, assessing and accrediting future professionals

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[July] What should the higher education sector do about AI fatigue?

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[July] What should the higher education sector do about AI fatigue?

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What should the higher education sector do about AI fatigue?

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The AI conversation is breaking down into silos, with many staff and students switching off or pushing back. Josh Thorpe calls for everyone to take a breath – and then come together and respond The AI conversation is breaking down into silos, with many staff and students switching off – or pushing back. Josh Thorpe calls for everyone to take a breath and then keep moving

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World Artificial Intelligence Conference (WAIC) 2025

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World Artificial Intelligence Conference (WAIC) 2025

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WAIC 2025 – Artificial Intelligence International Institute (AIII)

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World Artificial Intelligence Conference (WAIC) 2025 2025世界人工智能大会

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[July] From AI prohibition to integration – or why universities must pick up the pace

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[July] From AI prohibition to integration – or why universities must pick up the pace

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This HEPI guest blog was kindly authored by Mary Curnock Cook CBE, who chairs the Dyson Institute and is a Trustee at HEPI, and Bess Brennan, Chief of University Partnerships with Cadmus, which is running a series of collaborative events with UK university leaders about the challenges and opportunities of generative AI in higher education.

Are universities super tankers, drifting slowly through the ocean while students are speedboats, zipping around them? That was one of the most arresting images from the recent Kings x Cadmus Teaching and Learning Forum and captured a central theme running through the Forum: the mismatch between the pace of technological and social change facing universities and the slow speed of institutional adaptation when it comes to AI.

Yet the forum also highlighted a fundamental change in how higher education institutions are approaching AI in assessment - moving from a reactive, punitive stance to one of proactive partnership, a shift from AI prohibition to integration. As speaker after speaker acknowledged, the sector's initial approach of trying to detect and prevent AI use has been shown to be both futile and counterproductive. As one speaker noted, ' we cannot stop students using AI. We cannot detect it. So we have to redefine assessment.'

From left to right: Mary Curnock Cook, Professor Andrew Turner, Professor Parama Chaudhury, Professor Timothy Thompson. Source: Cadmus

This reality has forced some institutions to completely reconceptualise their relationship with AI technology in order to work with the tide rather than against it. Where AI was viewed as a threat to academic integrity, educators are beginning to see it as an inevitable part of the learning landscape that calls for thoughtful integration, not least so that students are equipped for change and the AI-driven workplace. For example, Coventry University has responded by moving its assessment entirely to a coursework-based approach, except where there are Professional, Statutory and Regulatory Body (PSRB) requirements, and explicitly allows the use of AI, in most cases, to assist.

Imperial College's approach exemplifies this new thinking with its principle of using AI "to think with you and not for you." This approach recognises AI as a thinking partner rather than a replacement for human cognition, fundamentally changing how universities structure learning experiences. The shift requires moving from output-focused assessment to process-based evaluation, where students must demonstrate their thinking journey alongside their final products.

Like many universities, Imperial is also concerned about equity of access to AI. As a baseline it offers enterprise access to a foundational LLM with firewalled data, Copilot, which ringfences the data within the institution. But it also has a multi-LLM portal pilot, which includes ChatGPT, Claude, Copilot, Gemini and DeepSeek, acting as an AI sandpit to help instil a culture of thinking of the LLMs as different tools to be experimented with - users can switch between them and ask them the same question to see the variation in results. Meanwhile, LSE has partnered with Anthropic to offer all students free access to Anthropic's Claude for Education, which helps students by guiding their reasoning process, rather than simply providing answers.

Practical implementation challenges

This transition to integration requires practical frameworks that many institutions are still developing. A speaker voiced the sector's uncertainty as: 'We do not know the next development - we didn't see this one coming.' This unpredictability leads to what was termed 'seeking safety in policy' - a tendency to over-regulate when the real need is for adaptive frameworks.

The challenge of moving beyond traffic light systems (red/amber/green classifications for AI use) emerged repeatedly. These systems, while intuitive, often leave educators and students in the ambiguous amber zone without clear guidance: 'everyone falls in the middle. You cannot do the red stuff but how do you enforce that? What do we really mean by the green stuff?' Instead, some institutions are moving towards assessment-specific guidance that explicitly states when and how AI can be used for each task.

Cultural and systemic transformation

This technological shift demands profound cultural change within institutions. As one participant observed, 'Culture change is being driven by students. Academics may not want to change but they no longer have a choice if they're getting assessments written by AI - or they don't know if the assessments are written by AI'. The pace of student adoption is outstripping institutional adaptation, creating tension between established academic practices and emerging student behaviours - those speedboats and super tankers again.

However, while the magnitude of the challenge calls for institutional-scale change and moving beyond individual innovations to systemic transformation, super tankers don't turn quickly.

Strategic approaches to change at scale

Several institutions shared their successful strategies for managing large-scale change. The key appears to be starting with early adopters and building momentum through demonstrated success. As Cadmus founder Herk Kailis noted, change champions are: 'the best people who are keeping the sector evolving and growing - and we need to get behind them as there aren't that many of them.' Imperial's approach of appointing 'AI futurists' in each faculty demonstrates how institutions can systematically seed innovation while maintaining a connection to disciplinary expertise.

Another speaker observed that successful change requires 'recognising the challenges and concerns of academic colleagues, bringing them together, supporting colleagues in making the changes they want.' At Maynooth University, incentives for staff, such as fellowships and promotion pathway changes, rather than mandates, draws on the notion that 'you can't herd cats but you can move their food'.

Cross-institutional collaboration

The forum emphasised that institutional change cannot happen in isolation. The complexity of stakeholder groups - from faculty leads to central teams to students and student-facing services - requires sophisticated engagement strategies. As one participant noted about successful technology implementation: 'Pilots don't work if they are isolated with one stakeholder group. You need buy-in from all the groups.'

The call for sector-wide collaboration extends beyond individual institutions to include professional bodies, regulatory frameworks and quality assurance processes - QA must also keep up with the pace of change. PSRBs, in particular, were singled out as a blocker to change.

International networking is also important. For example, UCL is working with Digital Intelligence International Development Education Alliance (DI-IDEA) from Peking University, which is experimenting with AI in education in innovative and accelerated ways.

Building sustainable change

Perhaps most importantly, the forum recognised that sustainable institutional change requires long-term commitment and resource allocation, and this imperative could arguably not have come at a more difficult time for many HE institutions. The observation that 'There's never been a greater need and appetite from staff to engage with this at a time when resourcing in the sector is a real problem' highlights the tension between ambition and capacity that many institutions face.

However, the success stories shared - such as Birmingham City University's Cadmus implementation saving 735.2 hours of academic staff time while improving student outcomes - demonstrate that institutional change, while challenging, can deliver measurable benefits for both educators and learners when implemented thoughtfully and systematically.

Three recommended actions from the forum
1. Address systemic inequalities, not just assessment design
Research from the University of Manchester shared at the conference showed that 95% of differential attainment stems from factors beyond assessment itself - cultural awareness, digital poverty, caring responsibilities and lack of representation.

Action: Take a holistic approach to student success that addresses the whole student experience, implements universal design principles, and recognises that some students are 'rolling loaded dice' in the academic game of privilege. Don't assume assessment reform alone will solve equity issues.
2. Reduce high-stakes assessment
Traditional exam-heavy models risk perpetuating inequalities and don't reflect workplace realities. Multiple lower-stakes assessments can support deeper learning and may be more equitable.

Action: Systematically reduce reliance on high-stakes exams in favour of diverse and more authentic assessment methods. This helps to address both AI challenges and equity concerns while better preparing students for their futures.

3. Co-create with students as partners

Students are driving the pace of change - they are already using AI. They need to be partners in designing solutions, not just recipients of policies.

Action: Involve students in co-designing assessments, rubrics and AI policies. Create bi-directional dialogue about learning experiences and empower students to share learning strategies. Build trust through transparency and genuine partnership.

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Weekend Reading: From AI prohibition to integration - or why universities must pick up the pace - HEPI

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Are universities super tankers, drifting slowly through the ocean while students are speedboats, zipping around them? That was one of the most arresting images from the recent Kings x Cadmus Teaching and Learning Forum and captured a central theme running through the Forum: the mismatch between the pace of technological and social change facing universities and [...]

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[JULY] AI Growth Zones Hull and East Yorkshire

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[JULY] AI Growth Zones Hull and East Yorkshire

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Hull and East Yorkshire Combined Authority

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Hull and East Yorkshire's new Mayor has fired up ambitions for the wider region to become a national leader in AI expertise, adoption and sector growth.

Mayor Luke Campbell MBE has formally written to the Government supporting two neighbouring areas' bids to become AI Growth Zones. This includes hosting powerful new Data Centres, further leveraging the existing local strengths.

Luke said -

"We're already powering ahead in AI - with the University of Hull at the forefront. Joining up across our work and the wider region can only benefit our wider economy, including our powerhouse industries who are pioneering its use in manufacturing, research and development."

The Mayor has backed York and North Yorkshire Combined Authority's AI Growth Zone bid centred around Drax Power Station. East Riding of Yorkshire Council and Hull University are key partners in the bid. The Mayor has also written to the Secretary of State for Innovation, Science and Technology (DSIT) to support North Lincolnshire's bid.

There is already a strong record of research, education and professional training through the University, plus cross-sector adoption in many of Hull and East Yorkshire's key industrial sectors. As well as establishing itself as the leading provider of AI education in the UK, Hull University's DAIM facility (Centre of Excellence of Data Science, Artificial Intelligence and Modelling) has established a national reputation, educating the next generation of data scientists and artificial intelligence practitioners and conducting major research.

Mayor Luke added -

"We can maximise these strengths through more partnership working across the private and public sector and wider region, to benefits local people here. That's why I am actively supporting both bids.

"Government investment in AI would drive further investment, innovation and creation of high-skilled jobs, also supporting our goals of developing and retaining talent locally.

"We have the people, skills and drive to make Hull and East Yorkshire even more of a national leader in harnessing the power and opportunities from AI, nationally and internationally."

Key local sectors including Land and Sea Logistics, Bioeconomy, Advanced Manufacturing, Life Sciences and MedTech, and New Energy and Energy Security are already developing the capabilities to maximise data, research and development opportunities from AI.

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Mayor backs bids to make region an AI leader - Hull and East Yorkshire Combined Authority

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Hull and East Yorkshire's new Mayor has fired up ambitions for the wider region to become a national leader in AI expertise, adoption and sector growth

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[JULY] How Smart Technology Can Align University Programmes with Real-World Skills

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[JULY] How Smart Technology Can Align University Programmes with Real-World Skills

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Bridging the Gap: How Smart Technology Can Align University Programmes with Real-World Skills - HEPI

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‘Pouvez-vous s’il vous plaît me dire où se trouve la gare?’ – this is the extent that a colleague of mine can remember from his Introductory French module that he completed as part of a computing degree in the late 90s. That institution’s attempt at the time to embed flexibility and cross-curriculum choice to help […]

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[JULY] 
OIA publishes a casework note and case summaries relating to AI and academic misconduct

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[JULY] 
OIA publishes a casework note and case summaries relating to AI and academic misconduct

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Poster information Published on 15 Jul 2025

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OIA publishes a casework note and case summaries about artificial intelligence (AI) and academic misconduct

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We have today published a casework note and some case summaries of complaints about the use of artificial intelligence (AI).

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[JULY] It’s time we moved the generative AI conversation on

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[JULY] It’s time we moved the generative AI conversation on

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By Michael Grove, Professor of Mathematics and Mathematics Education and Deputy Pro-Vice-Chancellor (Education Policy and Academic Standards) at the University of Birmingham.

We are well beyond the tipping point. Students are using generative AI - at scale. According to HEPI's Student Generative AI Survey 2025, 92% of undergraduates report using AI tools, and 88% say they've used them in assessments. Yet only a third say their institution has supported them to use these tools well. For many, the message appears to be: "you're on your own".

The sector's focus has largely been on mitigating risk: rewriting assessment guidance, updating misconduct policies, and publishing tool-specific statements. These are necessary steps, but alone they're not enough.

Students use generative AI not to cheat, but to learn. But this use is uneven. Some know how to prompt effectively, evaluate outputs, and integrate AI into their learning with confidence and control. Others don't. Confidence, access, and prior exposure all vary, by discipline, gender, and background. If left unaddressed, these disparities risk becoming embedded. The answer is not restriction, but thoughtful design that helps all students develop the skills to use AI critically, ethically, and with growing independence.

If generative AI is already reshaping how students learn, we must design for that reality and start treating it as a literacy to be developed. This means moving beyond module-level inconsistency and toward programme-level curriculum thinking. Not everywhere, not all at once - but with intent, clarity, and care.

We need programme-level thinking, not piecemeal policy

Most universities now have institutional policies on AI use, and many have updated assessment regulations. But module-by-module variation remains the norm. Students report receiving mixed messages - encouraged to use AI in one context, forbidden in another, ignored in a third, and unsure in a fourth. This inconsistency leads to uncertainty and undermines both engagement and academic integrity.

A more sustainable approach requires programme-level design. This means mapping where and how generative AI is used across a degree, setting consistent expectations and providing scaffolded opportunities for students to understand how these tools work, including how to use them ethically and responsibly. One practical method is to adopt a ' traffic light' or five-level framework to indicate what kinds of AI use are acceptable for each assessment - for example, preparing, editing, or co-creating content. These frameworks need not be rigid, but they must be clear and transparent for all.

Such frameworks can provide consistency, but they are no silver bullet. In practice, students may interpret guidance differently or misjudge the boundaries between levels. A traffic-light system risks oversimplifying a complex space, particularly when 'amber' spans such a broad and subjective spectrum. Though helpful for transparency, they cannot reliably show whether guidance has been followed. Their value lies in prompting discussion and supporting reflective use.

Design matters more than detection

Rather than relying on unreliable detection tools or vague prohibitions, we must design assessments and learning experiences that either incorporate AI intentionally or make its misuse educationally irrelevant.

This doesn't mean lowering standards. It means doubling down on what matters in a higher education learning experience: critical thinking, explanation, problem-solving, and the ability to apply knowledge in unfamiliar contexts. In my own discipline of mathematics, students might critique AI-generated proofs, identify errors, or reflect on how AI tools influenced their thinking. In other disciplines, students might compare AI outputs with academic sources, or use AI to explore ideas before developing their own arguments.

We must also protect space for unaided work. One model is to designate a proportion of each programme as 'Assured' - learning and assessment designed to demonstrate independent capability, through in-person, oral, or carefully structured formats. While some may raise concerns that this conflicts with the sector's move toward more authentic, applied assessment, these approaches are not mutually exclusive. The challenge is to balance assured tasks with more flexible, creative, or AI-enabled formats. The rest of the curriculum can then be 'Exploratory', allowing students to explore AI more openly, and in doing so, broaden their skills and graduate attributes.

Curriculum design should reflect disciplinary values

Not all uses of AI are appropriate for all subjects. In mathematics, symbolic reasoning and proof can't simply be outsourced. But that should not mean AI has no role. It can help students build glossaries, explore variants of standard problems, or compare different solution strategies. It can provoke discussion, encourage more interactive forms of learning, and surface misconceptions.

These are not abstract concerns; they are design-led questions. Every discipline must ask:

What kind of skills, thinking and communication do we value?

How might AI support, or undermine, those aims?

How can we help students understand the difference?

These reflections play out differently across subject areas. As recent contributions by Nick Hillman and Josh Freeman underline, generative AI is prompting us to reconsider not just how students learn, but what now actually counts as knowledge, memory, or understanding.

Without a design-led approach, AI use will default to convenience, putting the depth, rigour, and authenticity of the higher education learning experience at risk for all.

Students need to be partners in shaping this future. Many already have deep, practical experience with generative AI and can offer valuable insight into how these tools support, or disrupt, real learning. Involving students in curriculum design, guidance, and assessment policy will help ensure our responses are relevant, authentic, and grounded in the realities of how they now learn.

A call to action

The presence of generative AI in higher education is not a future scenario, it is the present reality. Students are already using these tools, for better and for worse. If we leave them to navigate this alone, we risk widening divides, losing trust, and missing the opportunity to improve how we teach, assess, and support student learning.

What's needed now is a shift in narrative:

From panic to pedagogy

From detection to design

From institutional policy to consistent programme-level practice.

Generative AI won't replace teaching. But it will reshape how students learn. It's now time we help them do so with confidence and purpose, through thoughtful programme-level design.

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It's time we moved the generative AI conversation on - HEPI

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We are well beyond the tipping point. Students are using generative AI - at scale. According to HEPI's Student Generative AI Survey 2025, 92% of undergraduates report using AI tools, and 88% say they've used them in assessments. Yet only a third say their institution has supported them to use these tools well.

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[JULY] Transforming higher education learning, assessment and engagement in the AI revolution: the how

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[JULY] Transforming higher education learning, assessment and engagement in the AI revolution: the how

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Transforming higher education learning, assessment and engagement in the AI revolution: the how - HEPI

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AI demands a radical shift in education, fostering adaptability, creativity, ethics, and collaboration to thrive in a transformed world.

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[JULY] Beyond Hype and Fluff: Lessons for AI from 25 Years of EdTech

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[JULY] Beyond Hype and Fluff: Lessons for AI from 25 Years of EdTech

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Beyond Hype and Fluff: Lessons for AI from 25 Years of EdTech - HEPI

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I am an advocate for education technology. It is a growing force for good, providing great solutions to real problems: Products and services that solve these problems will continue to take root. All that said, we have not seen the widespread transformation in education that technology promised to deliver, and investors have had their fingers […]

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[July] Embracing AI with integrity: A practical guide for researchers

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[July] Embracing AI with integrity: A practical guide for researchers

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Embracing AI with integrity - UK Research Integrity Office

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Embracing AI with integrity A practical guide for researchers The UK Research Integrity Office (UKRIO) has launched new guidance to help researchers navigate the research integrity challenges posed by Artificial Intelligence (AI). Last updated: 27 June 2025 Media enquiries: Jasper Scott UK Research Integrity Office jasper.scott@ukrio.org

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[JULY 2025] Beyond Rankings: Redefining University Success in the AI-Era

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[JULY 2025] Beyond Rankings: Redefining University Success in the AI-Era

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Beyond Rankings: Redefining University Success in the AI-Era - HEPI

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University rankings have long been a trusted, if controversial, proxy for quality. Students use them to decide where to study. Policymakers use them to shape funding. Universities use them to benchmark themselves against competitors. But in an AI-powered world, are these rankings still measuring what matters? If we’ve learned anything from the world of business […]

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[JULY] Helping professional services get confident with data

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[JULY] Helping professional services get confident with data

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Helping professional services get confident with data

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Sophie Crouchman and Laura Jackson introduce a programme to develop data-confident staff to add value to everything universities do

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[JUNE] The Creative Industries and GenAI

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[JUNE] The Creative Industries and GenAI

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[JULY] The AI balancing act: universities, innovation and the art of not losing the plot

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[JULY] The AI balancing act: universities, innovation and the art of not losing the plot

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The AI balancing act: universities, innovation and the art of not losing the plot - HEPI

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Universities must harness AI’s promise with caution, ensuring innovation enhances rather than undermines education, equity and academic integrity.

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[JUNE] How much energy and water does AI use?

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[JUNE] How much energy and water does AI use?

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How much energy and water does AI use? | The Observer

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Artificial intelligence is changing our lives and our world at an astonishing pace, but its hunger for data is matched by its need for energy and water.

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Digital literacy and inclusion critical to skilling individuals and businesses for success in the age of AI.To ensure individuals, businesses, and communities can thrive in the age of AI, government policy must place inclusion, digital skills, and adaptive regulation at the heart of a thriving, future-ready society.

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[JULY] Memorandum of Understanding between UK and OpenAI on AI opportunities

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[JULY] Memorandum of Understanding between UK and OpenAI on AI opportunities

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Department for Science, Innovation and Technology

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Memorandum of Understanding between UK and OpenAI on AI opportunities

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[JUNE] The role of AI in decision-making: a business leader’s guide

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[JUNE] The role of AI in decision-making: a business leader’s guide

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The role of AI in decision-making: a business leader’s guide - Quantexa

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Artificial intelligence (AI) once seemed like the stuff of science fiction. Find out how it is now being leveraged by business leaders across the globe.

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[June 2025] University at forefront of pioneering AI Growth Zone Bid

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[June 2025] University at forefront of pioneering AI Growth Zone Bid

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University at forefront of pioneering AI Growth Zone Bid

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The University of York is a key partner in a groundbreaking bid for York and North Yorkshire to become one of the UK’s new AI Growth Zones.

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[JUNE 2025] Evasys and Student Voice AI commissioned to deliver machine-learning analysis for PTES and PRES 2025 Open Comments

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[JUNE 2025] Evasys and Student Voice AI commissioned to deliver machine-learning analysis for PTES and PRES 2025 Open Comments

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evasys and Student Voice AI to deliver machine-learning analysis for PTES and PRES 2025

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Commissioned by Advance HE to deliver thematic coding, evasys and Student Voice AI to deliver machine-learning analysis for PTES and PRES 2025

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By Dr Andrew Woon, Senior Lecturer in Strategic Management at Queen Mary, University of London.

In recent years, universities around the world have been moving away from traditional exams in an effort to improve assessment practices, address equity concerns and adapt to the evolving educational landscape.

Top institutions such as Oxford and Cambridge are also shifting towards more inclusive forms of assessment to reduce awarding gaps. This raises the question: Do inclusive assessments omit certain opportunities for students' learning and development?

Critics argue that exams often measure test-taking ability rather than genuine understanding and may fail to assess all students' knowledge and skills accurately. Others contend that exams do little to prepare students for their future careers.

As a result, universities are adopting a range of assessment methods to support student learning and ensure inclusivity. These approaches aim to recognise a broader range of skills and attributes essential for future success.

I believe the key argument for retaining exams is not that they are more ' secure ', but that their true value lies in ensuring students, especially those in high-stakes professions like Medicine and Education, could develop a deep, internalised understanding of their subject matter.

Take Medicine, for example. We wouldn't want to be treated by a doctor who relies on ChatGPT to make clinical decisions. While AI can assist with diagnostics and offer suggestions, it lacks the human ability to assess a patient holistically, considering subtle symptoms, emotional cues and contextual factors. These are skills that must be trained, practiced and tested rigorously.

This is why accounting professional bodies such as ACCA and ICAEW continue to use exams as a primary assessment method, as they provide a standardised and objective measure of knowledge and skills. Exams help ensure that future professionals are not just good at looking up answers, but are also able to think critically, connect the dots, and make informed decisions independently.

Moreover, the purpose of exams extends beyond evaluating academic knowledge; they also help students identify their weaknesses and develop critical skills. While in-person exams can be stressful and inflexible, they offer benefits beyond knowledge testing. They cultivate vital skills such as time management, organisation and resilience under pressure, key competencies for both interpersonal growth and employability.

Many students shy away from exams because they struggle to perform well in a high-pressure, time-constrained environment with limited support. This may indicate a need to develop key competencies, such as time management and stress regulation, rather than suggesting that exams are inherently flawed.

Traditionally, students were trained to master literacy skills - to read, write, and think critically. While exams may miss opportunities to develop other important skills such as teamwork, communication and leadership, removing exams altogether may also reduce opportunities for students to improve their academic literacy. Writing, in particular, requires a good understanding of content and the ability to think critically, skills that are often cultivated through exam preparation and performance.

One of the most common arguments against exams is that they are not authentic and offer limited opportunities for skill development. According to Newman et al. (1998), assessment is considered authentic when it evaluates products or performances that hold meaning or value beyond mere academic success. Similarly, Wiggins (1989) defines authentic assessment as involving tasks that represent real-world challenges within a given discipline. These tasks are designed to mirror complexity, requiring depth over breadth.

Clearly, a well-designed exam can be authentic when it provides opportunities for meaningful skill development. For example, incorporating case studies into exams allows students to engage in deeper learning, apply knowledge to real-world contexts, and strengthen academic literacy at the same time.

After all, traditional exams, when thoughtfully designed, can play a valuable role in developing traditional literacy skills, including reading, writing and critical thinking, which remain essential in the age of AI.

As AI tools increasingly streamline reading and writing tasks across the education system, reinforcing these foundational skills becomes even more important. Promoting reading and writing through assessment can help combat aliteracy - a growing issue where students are capable of reading but choose not to, often due to the influence of social media and digital distractions.

I believe exams offer a rare opportunity and space for students to engage in undivided intellectual immersion, because exams create structured, distraction-free environments that demand focused engagement. They encourage deep reading, critical analysis and articulation without reliance on external assistance. Over time, such training can help counter aliteracy by reinforcing the value and interest of independent intellectual engagement.

I embrace new technologies and innovative forms of assessment, but that doesn't mean we should completely abandon traditional exams. Exams can and should coexist with other assessment methods, including those supported by AI, to enrich student learning. The purpose of assessment goes beyond assigning grades or preparing students for their first job; it is about equipping them for life in all its dimensions.

While technologies will continue to evolve, essential skills such as reading, writing, and critical thinking will remain foundational. Therefore, students must be literate to effectively process information and use AI intelligently.

Importantly, every assessment method has its advantages and disadvantages. Therefore, educators need to take a holistic approach to assessment design and policymaking. For example, using low-stakes, open-book exams can help reduce student stress while still supporting skill development. Additionally, providing tailored support for students with ADHD during exams can enhance their learning experience and ensure they are not placed at a disadvantage.

Lastly, I hope this opinion piece encourages fellow colleagues to reflect before the new academic year begins. Rather than simply jumping on the AI bandwagon, we must remember the critical role we play in safeguarding our students' futures. Our responsibility is to equip them with the life skills they need to thrive in an ever-evolving world.

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Rethinking Exams in the Age of AI: Should We Abandon Them Completely? - HEPI

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In recent years, universities around the world have been moving away from traditional exams in an effort to improve assessment practices, address equity concerns and adapt to the evolving educational landscape. Top institutions such as Oxford and Cambridge are also shifting towards more inclusive forms of assessment to reduce awarding gaps.

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[JUNE 2025] Funding challenges mean UK ‘risks falling behind’ on AI education

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[JUNE 2025] Funding challenges mean UK ‘risks falling behind’ on AI education

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Funding challenges mean UK ‘risks falling behind’ on AI education

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University finances and ‘painfully slow’ accreditation processes contributing to UK lagging behind neighbours on AI training, experts warn

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[JUNE 2025] All civil servants in England and Wales to get AI training

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[JUNE 2025] All civil servants in England and Wales to get AI training

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All civil servants in England and Wales to get AI training

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Officials are piloting package of AI tools called Humphrey – named after character in TV sitcom Yes, Minister

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[JUNE 2025] Quarter of UK’s AI research collaborations done with China

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[JUNE 2025] Quarter of UK’s AI research collaborations done with China

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China's ambition to lead the world in AI is far from unique. Yet its resources, talent and coordination of higher education and industry could just give it the edge, especially as Donald Trump's policies diminish the US' allure to top talent. Helen Packer and Futao Huang report

Now that the framework programme's integrity appears assured, it's time to address the details - together, says Jan Palmowski

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Quarter of UK's AI research collaborations done with China

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East-West partnerships playing an 'increasingly substantial role' in development of critical technologies but geopolitics puts trend under threat, finds British Council

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[JUNE 2025] Empowering Mature Students through Inclusive AI Literacy

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[JUNE 2025] Empowering Mature Students through Inclusive AI Literacy

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Empowering Mature Students through Inclusive AI Literacy: Advancing Digital Equity and Social Justice in Higher Education - HEPI

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Without inclusive design, AI in higher education risks deepening digital divides and sidelining mature learners’ unique contributions.

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[FEB 2025] Yorkshire Universities AI in Communications Session 

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[FEB 2025] Yorkshire Universities AI in Communications Session 

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Yorkshire Universities AI in Communications Session - Yorkshire Universities

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Lauren Hunter, YU Communications Assistant In February 2025, Yorkshire Universities (YU) hosted an informative and interactive session on Artificial Intelligence (AI). This session aimed to foster discussion around AI and its use in the higher education (HE) sector. Specifically, how AI could or should be used by communication colleagues working in HE and policy. The ... Find out more

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[JUNE 2025] Four potential positive uses of AI for government policymaking

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[JUNE 2025] Four potential positive uses of AI for government policymaking

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Four potential positive uses of AI for government policymaking - Yorkshire Universities

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Dr James Ransom James is an independent researcher and Honorary Senior Research Fellow at UCL Institute of Education. He was previously a Research Associate at Yorkshire Universities. More information on his work on universities and AI can be found on his website. My recent work has been helping universities to think about how they should respond to AI. ... Find out more

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[JUNE 2025] £187 million government “TechFirst” programme to bring digital skills and AI learning into classrooms and communities

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[JUNE 2025] £187 million government “TechFirst” programme to bring digital skills and AI learning into classrooms and communities

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1 million students in secondary school to be given an unprecedented chance to learn and develop their skills in tech and AI

£187 million investment in national skills programme to bring digital skills and AI learning into classrooms and communities

7.5 million UK workers to gain essential AI skills by 2030 through industry partnership as major tech players including NVIDIA, Google and Microsoft back the Government's skills drive

Skills drive to break down barriers to opportunity, drive growth and put more money in people's pockets through skilled jobs as part of the Plan for Change and the forthcoming modern Industrial Strategy

Pupils across the country will be given the skills and tools needed to get the AI-powered jobs of the future thanks to a new skills programme launched by the Prime Minister today (Monday 9 June).

At the heart of the skills drive, and as part of the upcoming modern Industrial Strategy, is a new £187 million government "TechFirst" programme to bring digital skills and AI learning into classrooms and communities and train up people of all ages and backgrounds for the tech careers of the future.

Today's announcements show this government is laser focused on investing in the futures of young people across Britain, knocking down barriers to opportunities, regardless of where they grow up.

It comes as research commissioned by the Department for Science, Innovation and Technology (DSIT) shows that by 2035, around 10 million workers will be in roles where AI will be part of their role or responsibilities in some form, with a further 3.9 million in roles directly in AI.

The flagship strand of this programme "TechYouth" - backed by £24 million of government funding - will give 1 million students over three years across every secondary school in the UK the chance to learn about technology and gain access to new skills training and career opportunities.

There will also be an online platform to inspire and educate students about the potential of computing and tech careers - building on CyberFirst's Explorers which has access to most secondary schools in the UK with 100,000 students registered already. This will bring together learning tools and training opportunities in a streamlined accessible space.

In each of the UK's regions and nations, a local delivery partner will be selected by DSIT to run the programme and deliver activities to schools and colleges in local areas.

The AI sector alone is valued at £72.3 billion and is projected to exceed £800 billion by 2035. It is growing 30 times faster than the rest of the economy, employing over 64,000 people across more than 3,700 companies.

But despite these strengths, access to AI skills in the UK remains one of the biggest barriers to growth-especially for startups, scaleups, and regions outside London. According to a TechNation report released today, one in three UK tech founders say the availability of top talent is their biggest barrier to growth.

That's why the government is backing young people and investing in skills as an engine of economic growth-putting more money in people's pockets and breaking down barriers to opportunity as part of the Plan for Change.

This package underpins the upcoming industrial strategy and also delivers on the government's manifesto commitment to create higher-quality training and employment paths by empowering local communities to develop the skills people need and putting employers at the heart of our skills system.

Prime Minister Keir Starmer said:

"We are putting the power of AI into the hands of the next generation - so they can shape the future, not be shaped by it.

"This training programme will unlock opportunity in every classroom - and lays the foundations for a new era of growth.

"Too many children from working families like the one I grew up in are written off. I am determined to end that.

"This programme is the Plan for Change in action - breaking down barriers, driving innovation, and giving every young person the chance of a good, well paid job and a bright future."

TechFirst will also support over 4,000 graduates, researchers, and innovators through three additional strands:

TechGrad (£96.8m) - will support 1,000 exceptional domestic students a year with undergraduate scholarships in areas like AI, cyber security, and computer science. This will also go towards 100 Research MSc places in key tech sectors, and 100 elite AI scholarships. Applicants will be able to apply to the scheme online and those successful will have their bursaries paid from a central fund.

TechExpert (£48.4m) - will give up to £10,000 in additional funding to 500 domestic PhD students conducting research in tech with the aim of accelerating cutting-edge innovation, strengthen the UK's research pipeline in strategic technology sectors, and ensure that emerging talent is supported to contribute to national tech leadership.

TechLocal (£18m) - will offer seed funding to help regional innovators and small businesses develop new tech products and adopt AI. A panel made up of local tech businesses will be established in each region to decide which applications have merit, with the necessary checks then done centrally by Innovate UK.

Major industry players including IBM, BAE Systems, QinetiQ, BT, Microsoft and the Careers & Enterprise Company - the national body for careers education - have backed the initiative.

TechFirst builds on the success of the CyberFirst programme, which has already helped hundreds of thousands of young people gain cyber security skills.

Science, Innovation and Technology Secretary Peter Kyle said:

"We are getting Brits ready for jobs of the future by helping millions across the country gain vital digital skills in AI and beyond.

"Embedding these skills into our education system and local communities will help people of all backgrounds and ensure tech talent flourishes in every corner of our nation.

"These partnerships with industry will translate skills into real jobs and economic growth, putting more money in people's pockets and breaking down barriers to opportunity. This is our Plan for Change in action - investing in the skills that will power our economy and deliver prosperity for working people across the country."

Jensen Huang, Founder and CEO, NVIDIA said:

"AI developers power the next industrial revolution. AI talent, skills and research are crucial ingredients in the UK's mission to become an AI maker, not an AI taker. We're delighted to partner with the government to train the next generation of AI developers, capable of finding new cures for diseases, discovering new materials and building word-class AI companies."

Google EMEA President, Debbie Weinstein, said:

"Our AI Works report revealed that £400bn worth of economic growth awaits the UK, but half of this depends on workers embracing and using AI. That's precisely why we're thrilled to join this crucial initiative, essential for supercharging AI upskilling, unlocking AI-powered growth and cementing the UK's position as an AI leader."

Carolyn Dawson OBE, CEO of Founders Forum Group and Tech Nation, said:

"AI will transform every industry - but we can only unlock its full potential if we ensure the UK's workforce has the skills to keep pace. This national upskilling programme is an ambitious and necessary step - not just to boost productivity, but to make sure we're equipping the UK to participate in and benefit from the AI-driven economy. At Tech Nation, we've long championed the power of both homegrown talent and global expertise - whether that's through supporting founders to scale or endorsing the UK's Global Talent Visa. We're proud to support initiatives that help the UK remain globally competitive".

Leon Butler Chief Executive of IBM UK and Ireland said:

"Boosting technology skills across the economy is key to the UK maintaining its leadership position in AI. Having helped millions globally to develop new AI skills with our IBM SkillsBuild programme, we are delighted to partner with the UK government to help equip workers with vital tech skills. This complements our long-standing commitment to programmes such as CyberFirst, which we are excited to see expand. We look forward to continuing our support as the programme grows."

Darren Hardman CEO of Microsoft UK said:

"Artificial Intelligence represents a generational opportunity, already transforming the way we live, work, and innovate. For the UK to remain globally competitive, we have to equip people with the skills they need to be successful in an AI-powered economy. Microsoft is proud to be playing its part, by training one million people with AI skills this year, and by supporting millions more through this new initiative."

Intuit EMEA General Manager Leigh Thomas said:

"AI is a growth enabler for small and medium-sized businesses, levelling the playing field, by giving them the opportunity to access the sort of technology solutions that larger businesses have access to. The announcement today is a great step forward in improving their bottom line, and we look forward to collaborating with Government and other private sector partners to accelerate knowledge, understanding and adoption of AI tools by the businesses that need it most."

Alongside TechFirst, the Prime Minister also announced a new government-industry partnership to train 7.5 million UK workers in essential skills to use AI by 2030-equivalent to around 20% of the UK workforce.

Leading technology companies including Google, Microsoft, IBM, SAS, Accenture, Sage, Barclays, BT, Amazon, Intuit, and Salesforce have signed up to the partnership. They have committed to making high-quality training materials widely available to workers in businesses - large and small - up and down the country free of charge, over the next five years.

Training will focus on enabling workers to use and interact with AI systems such as chatbots and large language models to boost productivity across a wide range of roles. Sector-specific training will also be developed to meet the needs of industries from healthcare to finance to manufacturing.

These companies will meet the Technology Secretary Peter Kyle this week to discuss how to meet the 2030 target, agree a terms of reference and will convene regularly to track progress.

Following his speech, the Prime Minister will join NVIDIA CEO Jensen Huang for an "in conversation" event to discuss the challenges of closing the AI skills gap and the potential of AI to transform public services and drive economic growth.

This comes as the government and NVIDIA today signed two Memorandums of Understanding, supporting the development of a nationwide AI talent pipeline and accelerating critical university-led research into the role of AI in advanced connectivity technologies. In addition, NVIDIA will expand its AI lab in Bristol to other areas of the UK to accelerate UK research in AI.

Today's package follows the Department for Education's announcement of the board members for Skills England, a new body which will work with employers and local leaders to shape training policy and delivery. Skills England will identify and tackle skills shortage in key Industrial Strategy sectors such as digital, creating more opportunities for young people.

Yesterday, the Prime Minister hosted a private reception at Chequers with leading tech CEOs and investors-including Eric Schmidt (Former CEO & Chairman of Google), Angie Ma (Faculty AI) Demis Hassabis (Google DeepMind), and Alex Wang (Scale) -to reaffirm the UK's position as a global tech leader.

Tomorrow, he will welcome business leaders and entrepreneurs to Downing Street, including 16-year-old AI entrepreneur Toby Brown, who recently secured $1 million in Silicon Valley funding for his startup, Beem.

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PM launches national skills drive to unlock opportunities for young people in tech

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Pupils across the country will be given the skills and tools needed to get the AI-powered jobs of the future thanks to a new skills programme launched by the Prime Minister.

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[JUNE 2025] Embracing innovation in higher education: our approach to artificial intelligence

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[JUNE 2025] Embracing innovation in higher education: our approach to artificial intelligence

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As the independent regulator for higher education in England, our role is to ensure that students from all backgrounds benefit from high quality higher education, delivered by a diverse, sustainable sector that continues to improve. We do this against a backdrop of social, economic and technological changes, perhaps none of which have captured the public imagination more than the growth of artificial intelligence (AI).

Use of AI among students is fast increasing, pointing to a future in which AI is an everyday part of the academic experience; for some students it may already be. Beyond higher education, AI has the potential to reshape the working lives of today's graduates.

Used well, there is a huge opportunity for AI to change students' experiences of higher education for the better. Keeping pace with this change is an important part of our role as a regulator.

What we have heard so far

During our strategy engagement, AI listening sessions and the May student debrief, students and student representatives spoke enthusiastically about the possibilities of AI. Students recognise the importance of AI literacy to their future careers and are excited about the responsive support AI may facilitate.

We heard about the diverse ways AI is being used by institutions to meet students' needs in different academic contexts. AI can enable immersive training that replicates real-world scenarios and provide real-time feedback on students' work. The expectation for institutions to embrace these opportunities is widespread. Students are using AI in a range of ways to support their learning and prompt creativity, such as to break down complex information, language and tasks, visualise data and write computer code. To help them make best use of AI, students emphasised the importance of clear, consistent and accessible guidance which considers varying access to AI technologies.

Our discussions with policy experts, sector representatives and institutions highlighted an appetite to integrate AI in ways that enhance students' experiences and outcomes. We also heard concerns that uncertainty about regulatory implications could deter institutions from fully leveraging AI opportunities. Institutions and sector representatives underlined the importance of collaborative working to identify, explore and resolve shared challenges and exploit joint opportunities.

Our position on AI

We take a principles-based approach to regulation, in part to support and encourage innovation and experimentation, whether in AI or beyond. There's no requirement for any particular scale or form of AI use. Indeed, AI may even help institutions meet and surpass the student-focused teaching and learning requirements we set in our regulation. By focusing on principles and outcomes rather than prescribing specific AI applications, we seek to foster an environment conducive to innovation while safeguarding the interests of students. Where AI integration has the potential to enhance the student experience, boost educational outcomes and equip students with the skills needed to serve labour market demand, we want as many students as possible to benefit.

The possibilities that AI brings are huge, but we recognise that AI comes with risks, particularly with regard to academic integrity, the meaning and credibility of assessment and qualifications, and dynamic effects on access and participation for underrepresented groups. Resisting AI integration, however, would be a temporary solution with significant opportunity costs for both institutions and students. The HEPI research cited above shows rapid acceleration in uptake among students, pointing to a future (and for some, a present) in which AI is an everyday part of students' higher education experiences. And with AI literacy increasingly important to employers, it's critical students develop the skills they need for their future careers.

The answer will instead involve thinking creatively about when, where and how AI can be integrated in ways that protect and improve students' educational outcomes. This will involve experimentation and will look different for different institutions. Likely applications will range across teaching, learning and student support. We recognise experimentation isn't linear and that mistakes are likely to be made by well-intentioned practitioners and institutions as they grapple with emerging and rapidly changing technologies.

The following prompts are intended to facilitate experimentation by sketching out broad parameters within which institutions can develop their approach to AI. These questions won't have definitive answers, but they highlight the issues we would expect institutions to have considered.

Good guidance

In our discussions with students, clear and accessible guidance about acceptable AI use was their number one ask. A lack of clear guidance can lead to inconsistencies, confusion and anxiety, deterring uptake. Many individuals and institutions across the sector are taking steps towards addressing these concerns. We've seen examples of principles-based frameworks, of checklists for students to help ensure good academic practice, and of efforts to more fundamentally rethink assessment methods and pedagogy.

If you've seen or implemented effective AI policies, please let us know! You can share examples by emailing [email protected] . We're especially keen to hear from students.

What next?

There's a lot of uncertainty about the future of AI, and I won't attempt any concrete predictions about the future of the technology. Within this uncertainty, at least three things seem likely: AI will get better, its use will become more prevalent, and its influence on higher education will increase. In this context, we encourage universities and colleges in England to experiment with how they can best support students, to try new and interesting things, and to be mindful of the risks as they do so.

Notes

HEPI, 26 February 2025, Student Generative AI Survey 2025. 92 per cent of students use AI in some form - up from 66 per cent in 2024. 88 per cent have used generative AI (GenAI) for assessments, up from 53 per cent in 2024.

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Embracing innovation in higher education: our approach to artificial intelligence

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Our Director of Strategy and Delivery, Josh Fleming, explains our position on artificial intelligence.