Category: Blog

  • Generative Engine Optimization & GEO Keywords

    Generative Engine Optimization & GEO Keywords

    Reading Time: 18 minutes

    Search behaviour among prospective students is evolving fast. Instead of scrolling through pages of search results, many now turn to AI-powered tools for instant, conversational answers. This shift has introduced a new layer to traditional SEO: Generative Engine Optimization (GEO).

    GEO focuses on optimizing content so that generative AI search engines like ChatGPT or Google’s AI Overview can find, interpret, and feature it in their responses. In essence, GEO ensures your institution’s information is selected, summarized, or referenced in AI-generated answers, rather than simply ranking in a list of links.

    Higher education marketers in Canada and beyond must pay attention to this trend. Recent global studies indicate that nearly two-thirds of prospective students use AI tools such as ChatGPT at some stage of their research process, with usage highest during early discovery and comparison phases. 

    These tools pull content from across the web and present synthesized answers, often eliminating the need for users to click. This “zero-click” trend reduces opportunities for organic traffic, raising the stakes for visibility within AI systems.

    This guide explores GEO’s role in education marketing, how it differs from traditional SEO, and why it matters for student recruitment in the age of AI. You’ll find practical guidance on aligning your content with generative AI, from keyword strategy to page prioritization. We’ll also look at how to measure GEO’s impact on inquiries and enrolment, and share examples from institutions leading the way.

    AI is rewriting how students discover institutions.

    Partner with HEM to stay visible in the age of generative search.

    What Is Generative Engine Optimization (GEO) in Higher Education Marketing?

    Generative Engine Optimization (GEO) is the practice of tailoring university content for AI-driven search tools like ChatGPT and Google’s AI Overview. Unlike traditional SEO, which targets search engine rankings, GEO focuses on making content readable, reliable, and retrievable by generative AI.

    In higher ed, this means structuring key program details, admissions information, and differentiators so that AI tools can easily surface and cite them in responses. GEO builds on classic SEO principles but adapts them for a zero-click, conversational environment, ensuring your institution appears in AI-generated answers to prospective student queries.

    How Is GEO Different from Traditional SEO for Universities and Colleges?

    While both SEO and GEO aim to make your institution’s content visible, their approaches diverge in method and target. Traditional SEO is designed for search engine rankings. GEO, on the other hand, prepares content for selection and citation by AI tools that deliver instant answers rather than search results.

    Let’s break it down.

    Search Results vs. AI Answers
    SEO optimizes for clicks on a search results page. GEO optimizes for inclusion in a conversational answer. Instead of showing up as a blue link, your institution may be quoted or named by the AI itself.

    Keyword Strategy
    SEO prioritizes high-volume keywords. GEO relies on semantic relevance. Instead of “MBA program Canada,” think “How long is the MBA at [University]?” or “What are the admission requirements?”

    Content Structure
    Traditional SEO values user navigation. GEO values clarity for AI parsing. Bullet points, Q&A formatting, and schema markup make it easier for AI to extract information. Summary boxes and tables work better than long paragraphs.

    Authority Signals
    E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) still matters. But for GEO, authority is inferred from citation style, accuracy, and consistency, not design or branding. Highlighting faculty credentials or linking to research enhances AI credibility scoring.

    Technical Approach
    Both SEO and GEO require clean, crawlable websites. But GEO adds machine-readable formatting. Schema.org markups, downloadable data files, and clean internal linking increase your chances of being selected by AI.

    Measuring Success
    SEO measures traffic, rankings, and form fills. GEO measures citations in AI responses, brand mentions, and voice assistant visibility. You might not get the click, but you still win visibility if the AI says your name.

    In practice, this means layering GEO on top of existing SEO. A strong program page might combine narrative storytelling with a quick facts section. An admissions page should include both persuasive copy and an FAQ schema.

    Bottom line: SEO helps you get found. GEO helps you get cited. And in the age of AI, both are essential to capturing attention at every stage of the student search journey.

    Why GEO Matters for Student Recruitment in the Age of AI Search

    Why is GEO important for student recruitment in the age of AI search? Generative AI search is already reshaping how prospective students discover, evaluate, and select postsecondary institutions. GEO (Generative Engine Optimization) equips institutions to remain visible and competitive in this changing environment. Here’s why it matters now more than ever:

    1. Widespread Adoption by Gen Z

    Today’s students are early adopters of generative AI. A 2024 global survey found that approximately 70% of prospective students have used AI tools like ChatGPT to search for information, and more than 60% report using chatbots during the early phases of their college research. This shift means fewer students are navigating university websites as a first step. 

    Instead, they’re posing detailed questions to AI, questions about programs, financial aid, campus life, and more. GEO ensures your institution’s information is accessible, machine-readable, and accurate in this discovery environment. Without it, you risk being excluded from the initial consideration set.

    1. The Rise of Zero-Click Search Behavior

    AI-generated responses often satisfy a query without requiring a website visit. This zero-click trend is accelerating, as nearly 60% of searches now end without a click. If a student asks, “What are the top universities in Canada for engineering?” and an AI tool responds with a synthesized answer that names three schools, those schools have won visibility without needing a traditional click-through. 

    GEO is your institution’s strategy for occupying that limited space in the answer. It’s how you shape perceptions in a search landscape where attention is won before a student reaches your homepage.

    1. AI Is Becoming a College Advisor

    Though current data shows AI has limited direct influence on final enrollment decisions, that influence is growing. As AI tools become more trusted, students will increasingly rely on them for shortlisting programs or comparing institutions. GEO ensures your content is part of those suggestions and comparisons. 

    For example, a prospective student might ask, “Which is better for computer science, [Competitor] or [Your University]?” Without well-structured, AI-optimized content, your institution may be left out or misrepresented. GEO levels the playing field, ensuring that when AI generates side-by-side evaluations, your offerings are accurate, current, and competitive.

    1. Fewer Chances to Impress

    Traditional SEO offered multiple entry points: page one, page two, featured snippets, and ads. AI-generated answers are far more concise, often limited to a single paragraph or a brief list of citations. That means your institution must compete for a narrower spotlight. 

    GEO increases your odds of selection by helping AI tools find and cite the most relevant, structured, and authoritative content. When students ask about tuition, deadlines, or international scholarships, you want the answer to come from your website, not a third-party aggregator or a competing institution.

    1. Boosting Brand Trust and Authority

    Being cited in AI responses lends credibility. Much like appearing at the top of Google results used to signal trustworthiness, consistent AI mentions confer authority. If ChatGPT, Google SGE, or Bing AI repeatedly reference your institution in educational queries, students begin to perceive your brand as reliable. 

    This builds long-term recognition, resulting in some students visiting your site simply because they’ve encountered your name often in AI responses. GEO helps position your institution as a trusted source across AI-driven search platforms, reinforcing brand equity and enhancing recruitment outcomes.

    In Summary

    GEO is rapidly becoming a critical component of modern higher education student recruitment marketing strategies. It ensures your institution is visible in the conversational, AI-driven search experiences that are now shaping student decisions. Just as universities once adjusted to mobile-first web browsing, they must now adapt to AI-first discovery. 

    GEO helps your institution appear in AI answers, influence prospective students early in their journey, and remain top of mind even when clicks don’t happen. For institutions navigating declining enrollments and intensifying competition, GEO is a forward-facing strategy that keeps you in the conversation and in the race for the next generation of learners.

    How Can a University Website Be Optimized for AI Tools like ChatGPT and Google AI Overviews?

    Optimizing a university website for generative AI search requires a blend of updated content strategy, technical precision, and practical SEO thinking. The goal is to ensure your institution’s content is not only findable but also understandable and usable by AI models such as ChatGPT or Google’s AI Overviews. Here are two key strategies to implement:

    1. Embrace a Question-First Content Strategy Using GEO Keywords

    Begin by identifying the natural-language queries prospective students are likely to ask. Instead of traditional keyword stuffing, build your content around direct, conversational questions with what we call “geo keywords.” For example: “What is the tuition for [University]’s nursing program?” “Does [University] require standardized tests?”, or “What scholarships are available for international students?”

    Structure content using Q&A formats, headings, and short paragraphs. Include these questions and their answers prominently on program, admissions, or financial aid pages. FAQ sections are particularly effective since AI tools are trained on question-based formats and favor content with semantic clarity.

    Audit your current site to uncover missing or buried answers. Use data from tools like Google Search Console or internal search analytics to surface frequent queries. Then, present responses in clear formats that both users and AI systems can digest.

    2. Create Clear, Canonical Fact Pages for Key Information

    AI tools rely on consistency. If your website offers multiple versions of key facts, such as tuition, deadlines, or admission requirements, AI may dismiss your content entirely. To avoid this, create canonical pages that serve as the single source of truth for essential topics.

    For example, maintain a central “Admissions Deadlines” page with clearly formatted lists or tables for each intake period. Similarly, your “Tuition and Fees” page should break down costs by program, year, and student type.

    Avoid duplicating this information across many pages in slightly different wording. Instead, link other content back to these canonical pages to reinforce credibility and reduce confusion for both users and AI. By prioritizing clarity, structure, and authority, your website becomes significantly more AI-compatible.

    3. Structure Your Content for AI (and Human) Readability

    Generative AI reads websites the way humans skim for quick answers, only faster and more literal. For your institution to show up in AI-generated results, your site must be structured clearly and logically. Here are six modern content strategies that improve readability for both users and machines:

    1. Put Important Information Up Front

    AI tools often extract the first one or two sentences from a page when forming answers. Lead with essential facts: program type, duration, location, or unique rankings. For example:
    A four-year BSc Nursing program ranked top 5 in Canada for clinical placements.

    Avoid burying key points deep in your content. Assume the AI won’t read past the opening paragraph, and prioritize clarity early.

    2. Use Headings, Lists, and Tables

    Break up long content blocks using headings (H2s and H3s), bullet points, and numbered lists. These structures improve scanning and help AI identify and categorize information correctly.

    Instead of a paragraph on how to apply, write:

    How to Apply:

    1. Submit your online application
    2. Pay the $100 application fee
    3. Upload transcripts and supporting documents

    For data or comparisons, use simple tables. A table of admissions stats or tuition breakdowns is easier for AI to interpret than buried prose.

    3. Standardize Terminology Across Your Site

    Inconsistent language can confuse both users and AI. Choose one label for each concept and use it site-wide. For example, if your deadline page says “Application Deadline,” don’t refer to it elsewhere as “Closing Date” or “Due Date.”

    Uniform terminology supports clearer AI parsing and reinforces credibility.

    4. Implement Schema Markup

    Schema markup is structured metadata added to your HTML that explicitly communicates the purpose of your content. It is critical to make content machine-readable.

    Use JSON-LD and schema types like:

    • FAQPage for question-answer sections
    • EducationalOccupationalProgram for program details
    • Organization for your institution’s info
    • Event for admissions deadlines or open houses

    Google and other AI systems rely heavily on this data. Schema also helps with traditional SEO by enabling rich snippets in search results.

    5. Offer Machine-Readable Data Files

    Forward-looking universities are experimenting with downloadable data files (JSON, CSV) that list key facts, such as program offerings or tuition. These can be made available through a hidden “data hub” on your site.

    AI systems may ingest this structured content directly, improving the likelihood of accurate citations. For example, the University of Florida’s digital team reported that their structured content significantly improved the accuracy of Google AI Overviews summarizing their programs.

    4. Keep Content Fresh and Consistent Across Platforms

    AI tools favor accurate and current information. Outdated or conflicting content can lead to mistrust or exclusion. Best practices include:

    • Timestamping pages with “Last updated [Month, Year]”
    • Conducting regular audits to eliminate conflicting data
    • Using canonical tags to point AI toward the primary source when duplicate content is necessary
    • Aligning off-site sources like Wikipedia or school directory listings with your website’s data

    For instance, if your homepage says 40,000 students and Wikipedia says 38,000, the AI may average the two or cite the incorrect one. Keep external sources accurate and consistent with your site.

    5. Optimize for Specific AI Platforms (ChatGPT, Google SGE, etc.)

    Each AI platform has different behaviors. Here is how to tailor your content for them:

    ChatGPT (OpenAI)

    Free ChatGPT may not browse the web, but ChatGPT Enterprise and Bing Chat do. These versions often rely on training data that includes popular and high-authority content.

    To increase visibility:

    • Publish long-form, high-quality content that gets cited by others
    • Use backlink strategies to improve domain authority
    • Create blog posts or guides that answer common student questions clearly

    Even if your content isn’t accessed in real time, if it has been crawled or cited enough, it may be paraphrased or referenced in AI answers.

    Google AI Overview (formerly SGE)

    Google’s AI Overviews (formerly Search Generative Experience, or SGE draws from top-ranking search results. So, traditional SEO performance directly influences GEO success.

    Best practices include:

    • Use concise, answer-oriented snippets early in content (e.g., “General admissions require a 75% average and two references.”)
    • Ensure pages are crawlable and not blocked by scripts or logins
    • Reinforce AI clarity with schema and consistent internal linking

    Voice Assistants (Siri, Alexa, Google Assistant)

    These tools favor featured snippets and structured content. A direct response like: “Yes, we offer a co-op program as part of our Bachelor of Computer Science” is more likely to be read aloud than a paragraph with buried details.

    Emerging Tools (Perplexity.ai, Bing Chat)

    These newer AI search tools cite sources like Wikipedia and high-authority sites. To prepare:

    • Keep your institution’s Wikipedia page accurate and updated
    • Monitor and correct public conversations (e.g., Reddit, Quora) with official clarifications on your website
    • Consider publishing myth-busting content to preempt misinformation

    Structuring your content for AI doesn’t mean abandoning human readers. In fact, the best practices that help machines, clarity, structure, and accuracy, also create better experiences for prospective students. By aligning your strategy with the expectations of both audiences, your university remains visible, credible, and competitive in the evolving search landscape.

    6. Leverage Institutional Authority and Unique Content

    Your organization holds content assets that AI deems both authoritative and distinctive, be sure to leverage them strategically. Showcase faculty research, student success outcomes, and institutional data on your site in clear, extractable formats. For instance:
    “Over 95% of our graduates secure employment within six months (2024 survey).”

    Include program differentiators, accolades, and unique offerings that set your institution apart. AI-generated comparisons often cite such features. Strengthen content credibility with E-E-A-T principles:

    • Add author bylines and bios to expert-led blog posts
    • Cite trusted third-party sources and rankings
    • Present information factually while still engaging human readers

    For example, pair promotional language (“modern dorms”) with direct answers (“First-year students are required to live on campus”). This dual-purpose approach ensures your content feeds both AI responses and prospective student curiosity.
    In short, AI rewards clear, credible, question-first content. Make sure yours leads the conversation.

    Which Higher Education Pages Should Be Prioritized for GEO?

    Not all web pages carry equal weight when it comes to generative engine optimization (GEO). To improve visibility in AI-generated search responses, universities should prioritize content that addresses high-intent queries and critical decision-making touchpoints.

    1. Academic Program Pages
      These are foundational. When users ask, “Does [University] offer a data science degree?”, AI tools pull from program pages. Each page should clearly outline program type, duration, delivery mode, concentrations, accreditations, rankings, and outcomes. Include key facts in the opening paragraph and use structured Q&A to address specifics like “Is co-op required?” or “Can I study part-time?”
    2. Admissions Pages
      AI queries often focus on application requirements. Structure admissions pages by applicant type and use clear subheadings and bullet points to list requirements, deadlines, and steps. Include canonical deadline pages with visible timestamps, and FAQ-style answers such as “What GPA is required for [University]?”
    3. Tuition, Scholarships, and Financial Aid
      Cost-related questions are among the most common. Ensure tuition and fee data are presented in clear tables, by program and student type. Scholarship and aid pages should state eligibility, values, and how to apply in plain language, e.g., “All applicants are automatically considered for entrance scholarships up to $5,000.”
    4. Program Finders and Academic Overview Pages
      Ensure your program catalog and A–Z listings are crawlable, up-to-date, and use official program names. Pages summarizing academic strengths should highlight standout offerings: “Our business school is triple-accredited and ranked top 5 in Canada.”
    5. Student Life and Support Services
      AI often fields questions like “Is housing guaranteed?” or “What mental health resources are available?” Answer these directly: “All first-year students are guaranteed on-campus housing.” Showcase specific services for key demographics (e.g., international students, veterans) with quantifiable benefits.
    6. Career Outcomes and Alumni Success
      Publish recent stats and highlight notable alumni. Statements like “93% of our grads are employed within 6 months” or “Alumni have gone on to roles at Google and Shopify” provide AI with strong content to surface in answers.

    How Can Institutions Measure the Impact of GEO on Inquiries and Enrolment?

    Measuring the impact of Generative Engine Optimization (GEO) requires a mix of analytics, qualitative monitoring, and attribution strategies. Since GEO outcomes don’t always show up in traditional SEO metrics, institutions must adopt creative, AI-aware approaches to track effectiveness.

    1. Monitor AI Referral Traffic
      Check Google Analytics 4 (GA4) or similar platforms for referral traffic from AI tools like Bing Chat or Google SGE. While not all AI sources report referrals, look for domains like bard.google.com or bing.com and configure dashboards to track them. Even small traffic volumes from these sources can indicate growing visibility.
    2. Track AI Mentions and Citations
      Manually query AI tools using prompts like “Tell me about [University]” or “How do I apply to [University]?” and log whether your institution is cited. Note if AIs reference your site, Wikipedia, or other sources. Track frequency and improvements over time, especially following content updates. Screenshots and logs can serve as powerful internal evidence.
    3. Use Multi-Touch Attribution
      Students may not click AI links, but still recall your brand. Add “How did you hear about us?” options in inquiry forms, including “ChatGPT” or “AI chatbot.” Monitor brand search volume and direct traffic following GEO updates. Qualitative survey insights and CRM notes from admissions teams can help reveal hidden AI touchpoints.
    4. Analyze GEO-Optimized Page Engagement
      Watch how the pages you optimize for GEO perform. Increased pageviews, lower bounce rates, and higher conversion (e.g., info form fills) may indicate better alignment with AI outputs and human queries alike, even if AI is only part of the traffic source.
    5. Observe Funnel Shifts and Segment Trends
      Notice any spikes in inquiries for certain programs or demographics that align with AI visibility. For example, a rise in international applications after enhanced program content could suggest AI exposure.
    6. Build a GEO Dashboard
      Create simple internal dashboards showing AI referrals, engagement trends, citation screenshots, and timelines of GEO initiatives. Correlate those with enrollment movement when possible.
    7. Test, Refine, Repeat
      Experiment continuously. A/B test content formats, restructure FAQs, and see which phrasing AI picks up. Treat AI outputs as your new SEO testbed.

    While GEO analytics are still evolving, early movers gain visibility and mindshare. Measuring what’s possible now ensures institutions are positioned to lead as AI search reshapes student discovery.

    10 Global Examples of GEO in Practice (Higher Ed Institutions)

    1. Harvard University: Harvard College Admissions “Apply” Page

    Harvard’s undergraduate admissions Apply page (Harvard College) is a model of clear, structured content. The page is organized with intuitive section headings (e.g., Application Requirements, Timeline) and even an on-page table of contents for easy navigation.

    It provides a bullet-point list of all required application components (from forms and fees to test scores and recommendations), ensuring that key information is presented succinctly.

    HEM Blog Post Image 2HEM Blog Post Image 2

    Source: Harvard University

    2. Stanford University: First-Year Applicants “Requirements and Deadlines” Page

    Stanford’s first-year admission page stands out for its semantic, structured presentation of information. It opens with a clearly labeled checklist of Required Application Components, presented as bullet points (e.g., Common Application, application fee, test scores, transcripts, etc.). Following this, Stanford provides a well-organized Requirements and Deadlines table that outlines key dates for Restrictive Early Action and Regular Decision side by side.

    In this table, each milestone, from application submission deadlines (e.g., November 1 for early, January 5 for regular) to notification dates and reply deadlines, is neatly aligned, which is both user-friendly and easy for AI to parse.

    HEM Blog Post Image 3HEM Blog Post Image 3

    Source: Stanford University

    3. Massachusetts Institute of Technology (MIT): “About MIT: Basic Facts” Page

    MIT Admissions offers an About MIT: Basic Facts page that is essentially a treasure trove of quick facts and figures presented in bullet form. This page exemplifies GEO best practices by curating the institute’s key data points (e.g., campus size, number of students, faculty count, notable honors) as concise bullet lists under intuitive subheadings.

    For instance, the page lists campus details like acreage and facilities, student demographics, and academic offerings in an extremely scannable format. Each bullet is a self-contained fact (such as “Undergraduates: 4,576” or “Campus: 168 acres in Cambridge, MA”), making it ideal for AI summarization or direct answers. Because the content is broken down into digestible nuggets, an AI-powered search can easily extract specific information (like *“How many undergraduate students does MIT have?”) from this page.

    HEM Blog Post Image 4HEM Blog Post Image 4

    Source: MIT

    4. University of Toronto: Undergraduate “Dates & Deadlines” Page

    The University of Toronto’s Dates & Deadlines page for future undergraduates is a great example of structured scheduling information. It presents application deadlines in a highly structured list, broken down by program/faculty and campus. The page is organized into expandable sections (for full-time, part-time, and non-degree studies), each containing tables of deadlines.

    For example, under full-time undergraduate applications, the table clearly lists each faculty or campus (Engineering, Arts & Science – St. George, U of T Mississauga, U of T Scarborough, etc.) alongside two key dates: the recommended early application date and the final deadline. This means a prospective student can quickly find, say, the deadline for Engineering (January 15) and see that applying by November 7 is recommended.

    Such a format is not only user-friendly but also easy for AI to interpret. The consistency and labeling (e.g., “Applied Science & Engineering, November 7 (recommended) / January 15 (deadline)”) ensure that an AI answer to “What’s the application deadline for U of T Engineering?” will be accurate.

    HEM Blog Post Image 5HEM Blog Post Image 5

    Source: University of Toronto

    5. University of Oxford: English Language and Literature Course Page

    Oxford’s course page for English Language and Literature showcases GEO-friendly content right at the top with a concise Overview box. This section acts as a quick-reference summary of the course, listing crucial facts in a compact form. It includes the UCAS course code (Q300), the entrance requirements (AAA at A-level), and the course duration (3 years, BA) clearly on separate lines. Immediately below, it outlines subject requirements (e.g., Required: English Literature or English Lang/Lit) and other admission details like whether there’s an admissions test or written work, all in the same straightforward list format.

    This means a prospective student (or an AI summarizing Oxford’s offerings) can get all the key info about the English course at a glance – from how long it lasts to what grades are needed.

    HEM Blog Post Image 6HEM Blog Post Image 6

    Source: Oxford University

    6. University of Cambridge: Application Dates and Deadlines Page

    Cambridge’s admissions website provides a dedicated Application Dates and Deadlines page that reads like a detailed timeline of the entire admissions process. This page lays out, in chronological order, all the key steps and dates for applying to Cambridge, with each date accompanied by a short explanation of what happens or what is due.

    For example, it starts as early as the spring of the year before entry, noting when UCAS course search opens and when you can begin your UCAS application. Critically, it flags the famous 15 October UCAS deadline with emphasis: “15 October 2025 – Deadline to submit your UCAS application (6 pm UK time)”. Other entries include deadlines for supplemental forms like the My Cambridge Application (22 October), dates for admissions tests, and notes about interview invitations in November and December.

    HEM Blog Post Image 7HEM Blog Post Image 7

    Source: University of Cambridge

    Staying Discoverable in the Age of Generative Search

    Generative Engine Optimization (GEO) is rapidly shifting from trend to necessity in higher education marketing. As AI-driven platforms like ChatGPT, Google SGE, and voice assistants reshape how students seek information, institutions must adapt their content strategies accordingly. 

    By aligning with modern GEO practices, universities enhance both discoverability and user experience, meeting students where they are and ensuring their narratives are accurately represented. In today’s competitive enrolment landscape, GEO is not optional; it is foundational. The strategies outlined above provide a roadmap for sustainable visibility in the age of generative search. Continue refining your approach, and your institution will not just appear in AI responses; it will lead them. In this new era, the goal is simple: be cited, not sidelined.

    AI is rewriting how students discover institutions.

    Partner with HEM to stay visible in the age of generative search.

    FAQs

    Q: What is generative engine optimization (GEO) in higher education marketing?

    A: Generative Engine Optimization (GEO) is the practice of tailoring university content for AI-driven search tools like ChatGPT and Google’s AI Overview. Unlike traditional SEO, which targets search engine rankings, GEO focuses on making content readable, reliable, and retrievable by generative AI.

    Q: How is GEO different from traditional SEO for universities and colleges?

    A: While both SEO and GEO aim to make your institution’s content visible, their approaches diverge in method and target. Traditional SEO is designed for search engine rankings. GEO, on the other hand, prepares content for selection and citation by AI tools that deliver instant answers rather than search results.

    Q: Why is GEO important for student recruitment in the age of AI search?

    A: Generative AI search is already reshaping how prospective students discover, evaluate, and select postsecondary institutions. GEO (Generative Engine Optimization) equips institutions to remain visible and competitive in this changing environment.

    Source link

  • Student renters deserve more support

    Student renters deserve more support

    Author:
    Graham Hayward

    Published:

    Join HEPI for a webinar on Thursday 29 January from 1.30pm to 2.30pm examining the findings of Student Working Lives (HEPI Report 195), a landmark study on how paid work is reshaping the student experience in UK higher education amid rising living costs and inadequate maintenance support. View our speakers and sign up here.

    This blog was kindly authored by Graham Hayward, Managing Director, Housing Hand.

    Much support is (quite rightly) given to young people in relation to choosing the right course at the right university. They are supported with reams of information on how to settle in at university, how to study independently, where to turn for advice on their course and how to develop essential life skills such as self-care and self-sufficiency. Universities also do much to support young people as they get used to living in halls during their first year. However, those who look to the wider private rented sector for accommodation in their second year often feel quite overwhelmed by the experience, finding a sudden dearth of information, not just from universities, but from the entire rental housing sector.

    Diving into the details

    Housing Hand surveyed over 1,700 private renters in early 2025, including 932 student renters. A staggering 76% of those student renters reported negative feelings about finding their first property. 24% felt overwhelmed, 20% uncertain, 19% anxious, 8% scared and 5% out of their depth. Concerns ranged from an inability to find a suitable or affordable property to not being accepted by the landlord if they did manage to find one. Just 6% reported feeling excited about finding their first property, and 6% happy.

    Going away to university can have a hugely positive impact on young people as they grow their independence, acquire essential life skills and develop a plentiful social life, as well as further their education. However, while universities provide a range of support for young people, they can’t (and shouldn’t) be expected to do it all. Our research suggests that the information provided to young people currently, by both the education and housing sectors, isn’t hitting the mark in terms of preparing students for renting.

    Students told us they typically get information on how to manage housing-related finances from family (37%), websites (29%), friends (15%) and social media (9%). 82% of the renters we surveyed wished there had been more financial education in school.

    Students feel the strain

    Finding suitable accommodation for university, as well as the pressure of being accepted by the landlord is, in the words of one student survey respondent, “exhausting”. It’s a challenge that many students face as they approach their second year of study – a far cry from the protection that living in university halls affords during their first year typically. It signals that there is much more that partnerships across the higher education and rental sectors could do to prepare young people for the experience of finding a first home.

    Doing so would not only support them to enjoy the process more, due to their increased confidence, but could also reduce the potential for student renters to make costly mistakes. Our research found that only 30% of student renters knew about deposit-less rental schemes, while just 47% knew about deposit protection schemes. We also found that 38% of students didn’t know what a guarantor was at the point they were asked to provide one.

    Students’ lack of rental sector experience puts them at a disadvantage compared to other renters and can result in them feeling overwhelmed. It is exacerbated by the fact that many of their parents also lack recent knowledge or experience of today’s rental market. This makes the process of finding a rental home stressful and can result in some student renters missing out on the property they want.

    Solving students’ rental stresses

    The passing of the Renters’ Rights Act, which marks the biggest shakeup to the rental sector in a generation, presents the ideal opportunity to address students’ knowledge gap. With both renters and accommodation providers needing to understand the changes that the Act is introducing, there is an opportunity to communicate clearly and effectively.

    The rental sector has the chance to work with educational establishments to help achieve this, ensuring the newest generation of renters has all the knowledge needed to move ahead with confidence. Preparing young people to rent a home shouldn’t be yet another burden for universities to carry; instead, the rental and education sectors must work in partnership to ensure they provide information in an easily digestible format to help empower young people from the very outset of their rental journey. Together, we have an opportunity to educate and empower, delivering a game-changing experience for young renters.

    Source link

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

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

    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.

    Source link

  • Breaking barriers: what the data tells us about care experienced and estranged applicants

    Breaking barriers: what the data tells us about care experienced and estranged applicants

    Over the weekend, HEPI published blogs considering whether we are fixing or unmaking universities, and asking why there haven’t been more institution mergers.

    This blog was kindly authored by Fiona Ellison, Co-Director, Unite Foundation.

    It is the fourth blog in HEPI’s series with The Unite Foundation on how to best support care experienced and estranged students. You can find the first blog here, the second here, and the third here.

    Higher education is often described as a transformative experience, but for some students the journey begins with significant barriers. Care experienced and estranged applicants – those who have spent time in care or are studying without family support – face unique challenges that impact their access, retention and success. At the Unite Foundation, we believe that the first step toward creating a more inclusive sector is to understand the data that underpins these challenges. We are grateful to do this in partnership with our long-term donor – Unite Students.

    In 2022, Unite Students first undertook their annual applicant index, which sheds light on the experiences of students starting university for the first time. This year, we’ve been able to compare the experiences of ‘traditional’ students with those who identify as either care experienced or estranged. The findings reveal stark differences in financial pressures, mental health, social connection and academic engagement.

    Who are we talking about?

    Across two years of survey data, 370 respondents identified as care experienced or estranged, compared to 2,981 who did not. These students are not a homogenous group, but patterns emerge: they also reflect a diverse demographic profile. For example, 17.8% of care experienced and estranged applicants identified as transgender, compared to just 2.3% of other applicants. Similarly, 3.8% identified as non-binary (vs 1.6%). These figures highlight the intersectionality of working with this group of students – we know that if you can get it right for care experienced and estranged students you can get it right for all students.

    The impact of financial pressure on mental health

    Financial insecurity is a recurring theme. Over a quarter (27.3%) of care experienced and estranged applicants reported that financial issues affect their mental health, compared to 19.6% of their peers. This is not surprising. Without family support, these students often navigate university life without the safety net others take for granted. We know from HEPI, TechnologyOne and Loughborough University’s Minimum Income Standard for Students that those studying without financial support – e.g. care experienced & estranged students – even with the full maintenance loan, would still need to work over 20 hours at minimum wage to achieve the minimum income standard needed to survive at university.  

    The implications are clear. Financial stress compounds mental health challenges, which in turn affect academic performance and retention. This was clear from the analysis we did of the Student Academic Experience Survey, which found that 43% of care experienced students and 44% of estranged students have considered withdrawing from university, compared to 28% of their peers. 

    Whilst the survey doesn’t give us insight into the reasons why, it does provide clues. For example, care-experienced students and estranged students work significantly more hours in paid employment, with care experienced students working, on average, 11.3 hours/week, and estranged students working 11.1 hours/week, compared to 8.8 hours/week for other students. 

    Social isolation and belonging

    University is often marketed as a time of social connection, but for many care experienced and estranged students, isolation is the norm. Only 26.8% said they have someone to turn to in a crisis, compared to 42.3% of other applicants. More than one in five (21.8%) expressed little interest in the social side of university life, almost double the proportion of their peers (11.2%).

    Pleasingly, expectations of belonging are similar across groups. 53.2% of care experienced and estranged applicants expected to feel a sense of belonging at university, compared to 54.8% of others. We know through our work supporting the All of Us Community – a space for all care experienced and estranged students to come together and connect with their peers – that creating opportunities for connection to help build that sense of belonging is crucial which is why we offer our ‘Funding for Fun’ small grants pot to facilitate connection between students online and in person across institutions.

    Learning challenges and attendance

    Academic engagement is another area of concern. Care experienced and estranged applicants are twice as likely to have prolonged absences due to mental health:

    • 22.9% missed 5–20 days in the past two years (vs 11.0% of non-care experienced students)
    • 21.9% missed more than 20 days (vs 10.1% of non-care experienced students).

    They are also more likely to struggle to keep up with their course (27.3% vs 18.8%). We know that this group of students are more likely to be working additional hours to fund their studies and this takes a toll on their ability to commit time to studies.

    That’s why, for students who received the Unite Foundation scholarship, we see their progression rates from year 1-2 at the same rate as non-care experienced peers and they graduate at a rate much closer to their non-care experienced peers. They’re not having to work as many hours as their peers, as they have a safe space to live with their rent covered for 365 days a year, for up to 3 years.

    This requirement to work to fund their studies shows up in the data – a striking 38.7% of care experienced and estranged applicants report significant work experience, compared to 27% of their peers.

    What does this all mean?

    The data tells a clear story: care experienced and estranged students face systemic barriers that cannot be solved by goodwill alone. Financial support, mental health provision and inclusive community-building must be embedded in institutional strategies.

    For policymakers, this means recognising these students as a priority group in widening participation agendas – not just paying lip service, but embedding and regulating for action. For universities, it means moving beyond access to focus on retention and success – using evidence based solutions, such as the Unite Foundation scholarship to create the conditions to enable care experienced and estranged students to thrive.

    If you want to explore how action to address accommodation issues can better support you care experienced and estranged students the Unite Foundation Blueprint framework can support your institution in building a safe and stable home for students, improving retention and attainment outcomes.  

    Source link

  • WEEKEND READING: Why not more?

    WEEKEND READING: Why not more?

    This blog was kindly authored by Professor Sir Chris Husbands, Director of Higher Futures and a HEPI Trustee. He was previously the Vice-Chancellor of Sheffield Hallam University.

    When the Times Higher Education considered those who had shaped higher education in 2025, it gave top billing to Jane Harrington, Vice-Chancellor of the University of Greenwich. And understandably so: along with Georgina Randsley de Moura, the Kent vice-chancellor, Jane is leading the merger of Greenwich and Kent to form what will be the UK’s first multi-university group. The new entity won’t necessarily stop at two universities, since it has been set up explicitly to incorporate others later. None of this should be a surprise. English universities continue to face severe challenges. The most recent OfS assessment is downbeat about the sector’s financial prospects. The October 2025 Post-16 white paper proposal to raise tuition fees in line with inflation has not really alleviated the problems: the measure to be used for indexation has not been identified, and for many institutions, that makes a significant difference. Moreover, indexation begins from a fee level which has been eroded in real terms over the last thirteen years. The real-terms value of the base unit of resource for indexation is roughly the same as it was in 1998 when top-up fees were first introduced. And thirdly, what the White Paper gives with one hand – fee indexation – it takes away with another in the form of the international student levy. The challenges remain.

    The White Paper envisages consolidation as one solution, asking ‘institutions to share resources and infrastructure, minimising duplication of effort’.  It wants ‘more consolidation and formal collaboration in the sector, with the result that institutions will be stronger and more financially sustainable.’ The Greenwich-Kent announcement followed a flurry of interest in what KPMG and Mills called ‘radical efficiency’ measures from shared services to deeper collaboration to full merger. The expectation is that the future of English higher education involves fewer institutions, greater specialisation and more consolidation.

    The higher education rumour mill has been spinning: a takeover here, a new group there, a university supposedly absorbing an further education college, a Russell Group member considering merger with its modern neighbour, all of them involving, as a long-running soap opera once put it, ‘neighbours becoming [more than] good friends.’ But repeatedly, rumours are either ill-founded or conversations collapse. Of course, mergers are difficult – and I should know, as I’ve led two of them, merging two higher education institutions and two sector agencies. But given the scale of the challenges, the surprise is that more has not happened. Understanding why this is may be one route to unlocking wider cultural change across the sector.

    Consolidation has been slow for several possible reasons. One, which could date this comment quickly, is that institutions may have been waiting for the White Paper to see the government’s intentions. With the White Paper out, activity may speed up. But this seems unlikely. Although the government’s aspirations for consolidation and specialisation are clear, it offers weak change mechanisms. A reshaping of research funding is the clearest policy shift, but there are few other measures to drive ‘consolidation and formal collaboration’. There’s no transformation fund, no new policy levers, no active market-shaping.

    Other reasons seem more compelling. One is the embedded culture of leadership and governance. Hyper-competitiveness has driven a robustly independent leadership culture, which means few leaders are well-attuned to the way to make collaboration work effectively. Boards are cautious. Universities have a range of governance forms; some are chartered, some are higher education corporations, and more recent foundations have other forms. The overwhelming majority have charitable status, with a board of governors owing fiduciary responsibilities to their own institution. In most cases, governors assume that their responsibility is to ensure that the university survives its current form, perhaps especially when the university bears the name of the place in which it is located: local pride matters. In fact, the responsibility of leaders and governors is to realise the objects of the charity, but the inclination to see their duty as being to the university rather than its objects is a barrier to change.

    A second explanation lies in regulation. The Office for Students’ new Strategy commits it to being collaborative, and it has said that it will not erect unnecessary barriers to consolidation. But the detail is complicated. Mergers between (say) stronger and weaker institutions may nevertheless create concerns about student outcomes (the OfS B3 conditions), whilst mergers between two struggling institutions are more likely to be problematic for B3 conditions. Without regulatory bridging arrangements, the worry – perhaps especially amongst cautious lawyers advising institutions – is that a merger brings regulatory risk. And the OfS is not the only regulator. Chartered institutions require Privy Council approval for governance changes. Cross-sector ‘vertical’ mergers, such as between higher education and further education institutions, which have potential in a more ‘tertiary’ world, involve overlapping and different regulatory regimes. Charity Commission approval is another potential hurdle

    Thirdly, there is a difference between mergers in for-profit and not-for-profit organisations. In the commercial world, mergers are almost always designed to increase shareholder return. The merger unlocks additional investment, capabilities, assets or routes to market expansion, which means higher financial returns. Even where a successful company takes on distressed assets, there are gains to be realised through intellectual property rights or the value in the distressed company’s assets. The initial costs of the merger – digital and management systems, restructuring – can either be met from reserves and the gains realised later, or by raising equity. Although universities are formally private sector institutions, in this respect, they resemble public institutions: they are not-for-profit and have charitable objectives. In other parts of the public sector, for example, further education colleges or academy trusts, mergers are often forced by the FE Commissioner or the Regional Schools Commissioner. Some public investment is often made available to handle transition costs – essentially performing the function of the financial markets in private sector mergers.

    If this analysis is right, it helps to explain why, despite the challenges, cultural, financial and regulatory concerns are slowing the radical changes– continue the pop culture references here and quote the Spice Girls – ‘when two become one.’ Understandably, universities believe that they need to solve their problems through some combination of restructuring, asset disposal, workforce reform or portfolio reshaping. Of course, mergers can happen, and given the combination of the push of financial pressures and the pull of a new policy framework, 2026 may unlock more activity in both vertical (HE/FE) and horizontal (HE/HE) mergers. But we shouldn’t hold our breath.

    The government could almost certainly have accelerated structural change through some sort of transformation fund. In the absence of that, others may bide their time and watch the Greenwich/Kent experience. It would be a missed opportunity if that is all they do.

    Mergers may be challenging, but the difficulties facing so many universities call for radical cultural and leadership change: collaborative, cross-institutional and, above all, learner-centred thinking. Institutions need the leadership confidence to engage with deep structural collaboration. The elements for that are increasingly clear, involving collaboration to pool elements of strategy and organisation, both across HE and deep into the other elements of post-18 education; and there are valuable steps that can be taken without committing to full merger. 2026 provides a much-needed opportunity to test and shape such different approaches and models. Indeed, without such bold thinking, the opportunity to create a more coherent and effective system will not be realised.

    Get our updates via email

    Enter your email address to subscribe to this blog and receive notifications of new posts by email.

    The post WEEKEND READING: Why not more? appeared first on HEPI.

    Source link

  • WEEKEND READING: What if, in trying to ‘fix’ universities, we are quietly unmaking them?

    WEEKEND READING: What if, in trying to ‘fix’ universities, we are quietly unmaking them?

    Join HEPI and Advance HE for a webinar on Tuesday, 13 January 2026, from 11am to 12pm, exploring what higher education can learn from leadership approaches in other sectors. Key topics will include innovative approaches to recruitment and diversity, and how to ensure future sector stability through effective leadership. Sign up here to hear this and more from our speakers.

    This blog was kindly authored by Dr Monica Franco-Santos, Reader in Organisational Governance and Performance, Cranfield University.

    Across the UK, it is widely recognised that universities are under intense financial pressure. The observable fact is simple enough: there is not enough money coming in to cover the costs of what universities are expected to do. The difficulty begins when leaders, advisers and commentators decide what kind of problem this is.

    How the financial problem is described is not neutral. It reflects and reinforces a particular way of understanding what a university is and how it should function. If the financial situation is framed as a classic demand-and-cost problem (i.e., demand is insufficient, prices are constrained, and unit costs are too high), then the university is, implicitly, being treated as a ‘service provider’ operating in a competitive international education market where students are customers. In that frame, the obvious actions are to emphasise tight cost controls and to strengthen output-focused performance metrics, targets and incentives such as promotions based on publications in highly rated journals, income generation or teaching satisfaction scores.

    If the same financial situation is framed instead as a system-level shock that threatens the conditions under which teaching, research and public service can flourish, then a different picture of the university comes into view: a ‘living knowledge ecosystem’ serving a public mission and facing financial constraints partly beyond its control. Within that frame, the responses appears quite different. Attention turns to protecting core capacities, reducing harm to the most vulnerable parts of the system and working with others to share risks and resources.

    In both cases, the numbers in the spreadsheets are the same. What differs is the story told about the problem, and the underlying image of the university that story presupposes. At present, the former factory-like framing is the most common. With it, the danger is that, under a narrative of financial constraints, universities take actions that emphasise governance practices that reshape behaviour so deeply that, over time, what remains may still be called a ‘university’, but no longer acts like one.

    What makes a university a university?

    Students come to university for far more than a certificate or a set of skills. They expect new knowledge, but also critical thinking, confidence, friendships, networks and the sense that they are part of something bigger than themselves. They hope that a university education will open doors and help them lead more meaningful and fuller lives.

    Academics are drawn to universities not only as workplaces. They want to pursue their passion, make meaningful contributions, explore new ideas, contribute to their disciplines and teach the next generation. Many accept lower pay and higher uncertainty than they might enjoy elsewhere because they believe in the university’s mission.

    Governments and taxpayers fund universities not because they are efficient ‘businesses’, but because they are essential public institutions. They generate research that underpins economic growth and cultural life. They educate professionals on whom society depends. They are meant to be spaces where difficult questions can be asked and discussed. They are fundamental institutions in a democratic society.

    None of this is easily captured by governance practices that focus on performance metrics, targets, incentives or cost controls. These governance practices convey a different message about what is valued and what counts, and over time, these messages have the power to reshape what people do and eventually, what a university is.

    The rise of ‘control-oriented governance practices’ and how they change the rules of the game

    In recent years, universities have increasingly adopted governance practices such as:

    • individual and departmental targets for income, outputs and student metrics;
    • performance indicators used in league tables and regulatory frameworks;
    • workload models that count every task in hours and allocate them through software;
    • performance-related pay and promotion criteria tied closely to measured outputs;
    • cost analysis that evaluates teaching programmes as if they were products or services in their own right.

    These control-oriented governance practices are introduced with good intentions. Leaders demand accountability and transparency. They want to reassure governors and regulators that they are ‘in control’. They want to show staff that decisions are based on objective data. However, these governance practices carry with them implicit assumptions: that performance is controllable, that it can be measured and managed in a hierarchical manner and that those who produce the measurable performance are likely to behave in self-interested, risk-averse, and effort-averse ways. As a result, cost control, monitoring, tight targets, and performance-contingent rewards are seen as necessary to secure results. In our current situation, that means financial results.

    What we tend to forget is that, as this style of governance spreads and becomes institutionalised, it often displaces older, more collegial arrangements in which academics and professional staff had greater discretion, participated in decisions and were trusted to act in line with the institution’s mission. Governance systems can become self-fulfilling. The assumptions on which they are based eventually appear to be true, not because they were accurate to begin with, but because the specific mechanisms introduced steadily guide people to behave as if they were.

    When these governance arrangements take hold, several things tend to happen:

    • academics who value autonomy, curiosity and public service may leave, or never enter, university life as they notice these values are no longer upheld. Others may be made redundant as part of cost-saving measures;
    • those who remain may adapt by focusing on what is measured rather than what matters. They learn to hit targets, manage their ‘scores’, and protect themselves. They eventually behave as the practices assume them to behave;
    • new entrants may be selected partly for their comfort with this environment. The population slowly changes.

    In this way, the market logic remakes the institution in its own image. At that point, the university may perform respectably in league tables and may have returned to healthy financial levels. But something more fundamental has shifted. The pattern of behaviour that governance practices value, reward and punish no longer aligns with the traditional mission of the university as a community of scholars serving the public good. The question then is not just “Are we financially sustainable?” It is “What kind of institution are we sustaining?”

    Questions for leaders and policymakers

    Policy work should offer alternatives, not only criticism. So what might it mean to protect the ’university-ness’ of universities under financial pressure?

    For governing bodies:

    • when you review performance information, ask not only “are we on target?” but also “what behaviours are these indicators encouraging or discouraging?”;
    • consider whether the balance between control and collegial governance is appropriate for different roles, especially for academic work.

    For vice-chancellors and senior teams:

    • before introducing new dashboards, workload systems or performance schemes, ask a simple question: “If this mechanism were the only thing staff knew about what we value, what would they infer?”;
    • involve staff from different groups in the design and review of governance mechanisms, and be open to evidence about unintended consequences, including effects on stress, trust and identity.

    For government and regulators:

    • recognise that the way funding and accountability regimes are structured shapes internal governance. If external frameworks reward narrow indicators, it is unsurprising that institutions pass that logic on to individuals;
    • consider how policy can support forms of governance that sustain academic stewardship, not only short-term performance.

    When do universities stop being universities?

    Universities can and must adapt. They have evolved many times in response to political, economic and technological shifts. No one is arguing for a return to a mythical golden age. However, if we allow a narrow, factory-style logic of control to dominate and we frame all our problems through that lens, we risk changing not only processes and structures, but the very rules of the game. When the values and behaviours that are made salient are those that undermine curiosity, critical thought and public service, the term ‘university’ begins to lose its substance.

    In my view, this is the core issue that staff, students, governors and policymakers should be debating. The question is not only how to keep universities solvent, but how to ensure that, in ten or twenty years’ time, they are still universities. And by that I mean: places where the pursuit of knowledge, the formation of judgement and the service to society remain at the heart of what they do.

    Source link

  • The boat is leaking: why is the change to admissions at one of the oldest Cambridge colleges a problem?

    The boat is leaking: why is the change to admissions at one of the oldest Cambridge colleges a problem?

    Author:
    Charlotte Gleed

    Published:

    This blog was kindly authored by Charlotte Gleed, former HEPI intern and current MPhil student at the University of Cambridge.

    A Guardian article revealing that Trinity Hall College at the University of Cambridge will target elite private schools for student recruitment has ignited a fierce debate this week. The article reveals how Fellows at one of the oldest Cambridge colleges voted to change their admissions strategy to approach a select group of 50 independent schools. The intention is to improve the ‘quality’ of applicants, following concerns that ‘reverse discrimination’ is the cause of this quality issue.

    But this diagnosis is a problematic one. And more concerning, it is a move which risks not only an interruption of access and widening participation efforts, but a radical setback.

    Why has Trinity Hall, Cambridge made this move?

    Trinity Hall claims that the change to their admissions policy is a ‘targeted recruitment strategy’. Their objective is to encourage students from the selected private schools to apply for undergraduate courses in a select list of subjects including languages, music, and classics. But this puts a – large and potentially destructive – spanner in the works for access to higher education.

    Not only does this strategy support a small minority of a privileged few, given that 7% of the population in the United Kingdom is privately educated. It also focuses on subjects, like music, which state schools have long struggled to maintain at equal levels to their independent counterparts. There has been a 25% drop in pupils studying GCSE Music in England over the last 15 years, and Parliament debated the issue in July 2025 over cuts and underfunding to musical education.

    A HEPI report from July 2025 raised concerns about the language crisis and the decline in uptake of students studying languages at school. So Trinity Hall are valid in their efforts to find ways to increase applications for languages, in particular. But their strategy of targeting the most – economically – selective schools is flawed.

    If this policy is implemented in the 2026 / 2027 admissions cycle and beyond the gap between outcomes for state and privately educated students in higher education will widen. Not only could this decision reverse sustained efforts to widen participation to higher education, but it will ultimately mean that ‘privileged pasts become privileged futures’, as the Dearing Report warned almost thirty years ago in 1997.

    Change to admissions policies is not always a bad thing. Back in 1965, Hertford College, Oxford devised, what is still a little known access programme called ‘The Tanner Scheme’. The programme was the first outreach initiative across Oxford and Cambridge: a revolutionary step for increasing accessibility to the most selective universities in the country – and the world. But its initial motivation was less egalitarian and philanthropic.

    The first version of the scheme was targeted at a select few boys’ grammar schools in the north of England, whose students the college admissions tutors believed were untapped talent. But the hidden goal was neither to widen participation nor improve access for these talented students, but to improve the academic record of the college within Oxford. Having exhausted the pool of privately educated talent, the next best option was academic students with ability and potential, not wealth.

    Sound familiar…? Only now potential and wealth are being combined.

    There is a real concern that a new precedent could be set within Oxbridge colleges, which threatens the long-established practice of widening participation. Colleges at both Oxford and Cambridge have a degree of independence unrivalled compared to most other higher education institutions. The Office for Students requires all higher education institutions to have an Access and Participation Plan (APPs) which identifies access and participation gaps unique to their student cohorts. APPs have not only held these institutions accountable but taken the sector in a positive direction towards increased access.

    But the Trinity Hall revelations show there is a loophole. Despite the Office for Students’ requirement, it appears that colleges can target what is an already overrepresented cohort without regulatory intervention. 29.0% of undergraduates accepted for the 2024/25 admissions cycle were privately educated, even though only 7% of the population is. While the majority of Cambridge acceptances come from ‘maintained’ schools (comprehensive and grammar schools, as well as sixth form and further education colleges) the disproportionate gap between the number of students attending independent schools and their acceptance of a place is troubling for access.

    That loophole needs closing. The ramifications for access to higher education could be catastrophic if a new trend begins. The Guardian reports that one member of staff at Trinity Hall, Cambridge called the policy ‘a slap in the face’ for state-educated undergraduates. But there is an even higher stake than this. It could mean that higher education becomes more inaccessible for those whose life it could transform most.

    The boat is not sinking – yet. But there is a risk it could.

    Source link

  • Data: turning insights into action at Teesside University

    Data: turning insights into action at Teesside University

    This blog was kindly authored by Professor Mark Simpson, Deputy Vice-Chancellor at Teesside University.

    Data is everywhere, but how do we turn it into insights that actually change outcomes for students and graduates?

    At Teesside University, this question underpins strategies that have helped us achieve sector-leading recognition: TEF Gold for teaching excellence, Ofsted Outstanding, and Times Higher Education University of the Year 2025.

    Did the predictions hold true?

    In earlier blogs, we anticipated major shifts: the rise of AI in learning and assessment, deeper collaboration between institutions, and the growing importance of data-driven decision-making. So, did they happen?

    AI adoption: far from being banned, AI is now embedded in teaching and assessment strategies, guided by ethic-focussed user principles.

    Collaboration: regional partnerships have strengthened, particularly around employability and mental health, though mergers remain rare.

    Data-driven action: the sector has moved beyond dashboards to interventions that improve student success, though capability gaps in data literacy persist.

    These trends confirm what we argued – universities that embrace innovation and ethical data use are better positioned to deliver outcomes that matter: graduate success, employer confidence, and sector-leading recognition.

    This blog moves the conversation from trends to action: the principles and practices that turn data into decisions, and decisions into impact for students, graduates, and employers.

    Why actionable insights matter

    Data tells us what happened. Insight explains why it happened and what to do next. In a sector where TEF narratives, OfS outcomes, and B3 metrics are under constant scrutiny, insight must be decision-ready: clear, timely, and connected to actions that improve student success.

    One example from Teesside University: analysis of engagement and wellbeing data revealed predictable spikes in anxiety before assessments. That’s an insight, but the real value lies in what changes next: assessment tweaks, targeted comms, coaching, or extended mental health support. Without action, insight is just noise.

    Principles for turning data into action

    Insights only create impact when they lead to meaningful change. These five principles, proven in practice, help ensure your data works for you:

    1) Clarity of purpose

    Start with a precise aim: Which outcome will we improve, by how much, and by when? Clear goals turn data into a roadmap rather than a report.

    2) Integration, not isolation

    Data should flow across curriculum design, student support, careers, and employer partnerships into one coherent picture. Bringing in the student perspective ensures this integration is authentic, connecting learning experiences to aspirations, not just administrative targets.

    3) Student voice driving decision-making

    Students should shape decisions about data use. Co-design privacy, transparency, and wellbeing safeguards with them. Explain the why, what, and how in clear language, and make opting in meaningful by showing how their input drives change.

    4) Timely intervention

    Move beyond annual reviews to real-time decisions that matter most: before assessments, during placements, and at key transition points. Use student feedback to set the rhythm for dashboards, reviews, and action cycles so insight lands when it counts.

    5) Collaboration and ownership

    Insight should be co-owned across academics, student services, and employers – with students as equal partners. Involve them in approval panels, curriculum reviews, and evaluation loops. Their lived experience transforms data into stories that resonate and drive action.

    Teesside University in practice

    Teesside’s approach offers a concrete model for turning principles into practice.

    Future Facing Learning (FFL) embeds digital empowerment, global citizenship, and entrepreneurial thinking – making employability part of the learning experience, not an add-on.

    Learning & Teaching Framework (LTF)ensures course-first design, authentic assessment, and industry engagement, supported by staff CPD.

    Laser-focused strategy & KPIs link performance to TEF and B3, with regular reviews and targeted improvement plans.

    Breaking down silos brings employers onto panels and integrates meaningful student voice – feedback that leads to visible change.

    Pragmatic AI strategy encourages innovation and future skills, adapting quickly to a world where 65% of today’s primary school children will work in jobs that don’t yet exist.

    The challenge ahead (and how to navigate it)

    We all face familiar constraints: full curricula and professional body frameworks, budget and time pressures, and capability gaps in data literacy and change management. Progress depends on:

    • Course-first trade-offs: deciding what comes out when new skills go in; aligning assessments with employability outcomes.
    • Authentic assessment: using live briefs, micro-placements, and employer co-designed tasks.
    • Partnership by default: involve employers in approval events and reviews; move beyond advisory boards to co-production of learning.
    • Data fluency for staff: providing CPD focussed on interpreting and acting on data.
    • Targeted pilots: start small where the impact is highest (e.g. first-year transition), measure rigorously, and scale.

    Turning data into action isn’t about having more dashboards, it’s about better decisions, made faster, with students and employers at the centre.

    Teesside University’s experience shows that when strategy, frameworks, and student voice align, employability becomes a lived experience in the curriculum, not a promise on a prospectus.

    Professor Mark Simpson is speaking at Kortext LIVE on 11 February 2026 in London. Join Mark at this free event as he dives deep into the strategic impact of data alongside Dr Rachel Maxwell. Find out more and secure your seat here.

    Source link

  • What’s going to happen in international education in 2026?

    What’s going to happen in international education in 2026?

    Join HEPI and Advance HE for a webinar on Tuesday, 13 January 2026, from 11am to 12pm, exploring what higher education can learn from leadership approaches in other sectors. Key topics will include innovative approaches to recruitment and diversity, and how to ensure future sector stability through effective leadership. Sign up here to hear this and more from our speakers

    This blog was kindly authored by Viggo Stacey, International Education & Policy Writer at QS Quacquarelli Symonds.

    If 2026 is anything like last year, international education is in for another unpredictable 12 months.

    Much of 2025 was interspersed with speculation in the press about whether degrees were no longer of value for graduate, in a new world of work. There was also recurring discussion about higher education in key study destinations losing reputational ground to emerging education hubs. Despite this, rumours of higher education’s decline have been exaggerated.

    Across the global education landscape, competition for outstanding students continues to heat up. Despite policy changes in key study destinations designed to reduce the number of  international students from arriving onshore; universities and governments continue to vie for the best international talent.

    India

    Canada’s longstanding diplomatic rift with India began to thaw in 2025, with Mark Carney and Narendra Modi agreeing to enhance diplomatic staffing levels and to strengthen people-to-people linkages when they met late last year.

    Australia is already there. The country’s education minister, Jason Clare, has visited India three times in the three and a half years he has held the education portfolio. The latest visit in December saw him invited to dine privately with his counterpart, Minister Dharmendra Pradhan, at his home in New Delhi.

    India is also top of mind for UK universities, with several announcing branch campuses, and many seeking dual degrees or research partnerships with Indian counterparts. Kier Starmer’s trade mission to Mumbai in 2025 focused on business and trade, with India’s demand for 70 million university places needed by 2035 noted as a ‘huge opportunity for UK universities seeking new funding streams’.

    However, official government figures from the end of last year suggested that the numbers of higher education students from India studying abroad overall fell in 2025.

    Beyond India

    At QS, our projections for the total number of internationally mobile students globally are expected to hit 8.5 million by the end of the decade.

    QS has already spoken about the Big four evolving into the Big 14, as the predicted growth rate in global international student numbers over the next five years rises by 4 per cent.

    We also anticipate that the combined market share of the US, UK, Australia and Canada will continue to drop slowly in the next years, from the current 40 per cent towards the projected 35 per cent by the end of the decade.

    If the current US administration continues on its unpredictable path (student visa appointments were paused for an extended period in 2025, before expanded social media vetting for students was announced in June), the UK, Australia, Canada, along with an array of places seeking to become international study hubs, could benefit.

    The US’ new partial bans on student visas from countries such as Nigeria may also prove advantageous for the UK.

    Figures from IIE in late 2025 showed that overall new international student numbers in the US fell by 7 per cent to 277,118. The picture is complicated however. While the number of new graduate students fell by 15 per cent, figures for new undergraduates actually grew by 5 per cent.

    Our own analysis suggested that, if OPT (Optional Practical Training) numbers are outstripped from the total US numbers, international student figures in the US could decline to such an extent that the UK would become the number one destination for international students in the world by 2030.

    In December 2025, the federal government in Canada announced more details of its $1.7 billion Canada Global Impact+ Research Talent Initiative. It follows European initiatives in seeking to recruit scientists, particularly from the US, in the face of funding cuts at home. China has also launched its own visa, seeking to attract talented scientists. This visa (the K-visa) gives applicants with a bachelor’s degree in a STEM field or those engaged in STEM research or education at a recognised institution flexible entry into the country, without the need for employer sponsorship.

    Policies like these are designed to win talent that would otherwise be in the US, and the UK might also benefit among students and scholars who would previously have opted for the US.

    A cap on numbers?

    Canada’s new cap on international students, announced in November 2025, has seen cap numbers reduced from around 300,000 last year to 155,000 in 2026, but notably, it will not include master’s students. In Australia, some two dozen providers are already over the 80 per cent threshold of their New Overseas Student Commencement allocations for 2026.

    Policies such as this could also end up benefiting the UK.

    This all being said, the final impact of the international student levy, as well as the likely boost from re-association with Erasmus+ could alter the overall result for the UK in varied ways.

    Ahead of rejoining the Erasmus+ programme by 2027, the new Basic Compliance Assessment rules on international applications in the UK could see universities punished for high visa refusal and completion rates. This is likely to damage the diversity of international cohorts on UK campuses – some institutions have already publicly said they will not recruit from ‘high risk’ countries in the next year in order to protect the integrity of the sector.

    Australia’s minister Clare repeatedly decried the ‘shonks’ taking advantage of international students during Anthony Albanese and the Labor Party’s first term in Australia. Subsequently, the government brought in changes to ensure that prospective students are genuine students, avoiding those who are supposedly seeking ‘to cheat the system in order to enter Australia’. Clare’s speeches since the re-election in 2025 have been much more supportive.

    International education advocates in other countries will hope that language such as this will be tempered in 2026, as the systems that study destinations have put in place begin to see results.

    This year could well be the year that international education bounces back.

    Source link

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

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

    Author:
    Rose Stephenson and Lan Murdock

    Published:

    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.

    Source link