Sector research
64 UK institutions · audited August 2026
The state of AI discoverability in UK higher education
Applicants ask ChatGPT, Gemini and Perplexity about universities before they visit a university website. We tested whether 64 UK institutions can be read by those systems. Most cannot, and almost none of them chose that.
Independent research, produced without vendor sponsorship. Every finding is reproducible, and the method is published in full.
Executive summary
What this means for your institution
The checks are technical, but the implications are operational. Here is what leaders, marketers and digital teams should take from the audit.
92%
have no stated AI crawler policy
Without a robots.txt position, answer engines and training crawlers apply their own defaults. Your visibility is effectively outsourced.
45%
hide course fees from AI readers
Fees sit in tabs, PDFs or client-side renders. Assistants answer anyway, often from a third-party site.
46%
publish no structured data
No JSON-LD means no machine-readable course facts, FAQs, or entry requirements. You cannot be cited if you cannot be parsed.
11%
block AI crawlers at the edge
A small group has made an active decision. The rest are not blocking AI; they are simply not configured for it.
For the Vice-Chancellor
AI discoverability is becoming a proxy for institutional openness. The first question is not budget; it is ownership. Somebody needs to own the robots.txt file.
For the Marketing Director
Your content is already being used to answer questions about you. The question is whether the answers are accurate, current, and attributable.
For the Digital Director
Most fixes are small, fast and low-risk: add JSON-LD, move fees into HTML, write a dated AI crawler policy. The barrier is coordination, not engineering.
Twelve chapters
Read it through, or jump to the chapter you own
- 00A decision nobody madeWe looked for universities blocking AI. We found almost no decisions at all.
- 01The policy that was never written92% name no AI crawler. Four institutions have stated a position.
- 02The invisible block11% turn answer engines away at the edge, and nothing in the CMS says so.
- 03Nothing to extract46% publish no structured data. Only 6% publish FAQPage.
- 04The questions applicants ask45% keep fees out of rendered HTML. Nobody publishes room prices.
- 05Early, and mostly done badlySeven publish one. One shipped unedited model output.
- 06Platform is context, not causeTerminalFour leads. Higher-ed platforms fail at the same rate.
- 07One institution got this rightHand-written, commented, dated. Ownership, not budget.
- 08What it is worth fixingUEL and Regent's University London.
- 09What to do about itSeven actions in order, with effort and an owner for each.
- 10All 64 institutionsAnonymized results across seven checks, filterable by region.
- 11What we did, and could not doSeven read-only checks, stated denominators, and our coverage gap.
- Names no AI crawler in robots.txt
- 92%
- Publishes no structured data
- 46%
- No course fees in rendered HTML
- 45%
- No entry requirements in rendered HTML
- 34%
- Blocks AI crawlers at the edge
- 11%
- Publishes an llms.txt
- 11%
- Has FAQPage schema
- 6%
- Blocks AI search crawlers in robots.txt
- 0%
Each figure is calculated against the institutions where that check returned a verifiable answer. Orange bars are gaps, grey bars are positive or deliberate states. Source: Axelerant audit, August 2026.
This report is about a decision that nobody made.
The first place a prospective student asks about a university has quietly moved. They ask an assistant what they can study, what the entry requirements are, what it costs, and whether they will be sharing a bathroom. The assistant answers, whether or not the university is in a position to supply that answer.
We ran the same seven technical checks against 64 UK institutions to see how well the sector is positioned for that.
The result was not the one we expected. We went looking for institutions blocking AI. We found something duller and more consequential: almost none of them have made any decision at all.
92%
name no AI crawler in robots.txt, neither to allow one nor to block one.
Forty-eight of the fifty-two readable files mention no AI crawler whatsoever. The file has been left as it was written for a search landscape that no longer exists on its own.
That is not negligence. Nobody in the sector was ever asked to own this, so nobody did.
The policy that was never written
A robots.txt file is where a site states which automated systems may read it. It is public, trivially editable, and takes minutes to change.
Of the 52 files we could read, four name an AI crawler. The other 48 do not, which means every AI vendor applies its own default, and those defaults change without notice or consultation.
| Approach | What they did | Our read |
|---|---|---|
| Deliberate and correct | Blocks seven training crawlers, leaves all five answer-engine and user-fetch agents reachable. Commented and dated. | The model. Protects training data without sacrificing discoverability. |
| Partial | Blocks ClaudeBot and anthropic-ai only. | A decision about one vendor, not a policy. |
| Path restrictions only | Names three AI agents, restricts specific directories. | Reasonable. Content remains reachable. |
| Training blocked, search untouched | Blocks six training crawlers plus a Content-Signal: ai-train=no header. | Defensible, but the pattern suggests a vendor dashboard toggle rather than an authored policy. |
Why this file goes unowned
Marketing assumes robots.txt is technical, so IT's. IT assumes it is about content, so marketing's. It sits in a repository marketing cannot edit and IT has no reason to open, so it ages in place. The fix is organizational before it is technical: somebody has to be told this is theirs.
The distinction almost nobody is making
Training crawlers
GPTBot, ClaudeBot and CCBot collect content to train models. Blocking them is a legitimate position and costs little in recruitment terms.
Answer-engine crawlers
OAI-SearchBot and PerplexityBot put you in a live answer today. Blocking these removes you from the conversation.
User-fetch agents
ChatGPT-User fetches a page because a human asked. Block it and an applicant pasting your own link gets nothing.
One institution in 64 has separated these three categories in writing.
The invisible block
Six institutions, 11% of those we could validly test, return an error to AI crawlers at the network edge while serving ordinary browsers and Googlebot perfectly normally. None of it appears in their robots.txt. Four of the six block every major agent we tested.
These sites look completely healthy in Google. Their marketing teams have no way of knowing.
This is almost always a bot-management setting: a firewall rule, or a "block AI scrapers" toggle in a CDN dashboard, enabled by somebody acting reasonably on the information available to them. Nobody involved was thinking about student recruitment, because that was not their job.
A crawler disallowed in robots.txt goes away cleanly. A crawler served a JavaScript challenge receives a valid response containing no content, and answer engines do not solve challenges or retry. The result is not "this university declined to participate", it is "this university appears to have nothing to say". The assistant then answers from league tables and forum posts instead.
What fixing it involves
Do not switch bot management off, because it is doing real work. Move the answer-engine agents onto the verified-bot allow list, with rate limits rather than challenges. A rate limit slows a crawler down, a challenge costs you the answer. Then replay the request with each user-agent to confirm the rule behaves as intended: we have seen allow-list entries silently fail on a mismatched agent string.
The bigger problem: nothing to extract
Blocking is dramatic but rare. The structural gap is neither, and it affects far more institutions.
Structured data is how a page states unambiguously that it describes a course, who provides it, when it runs and what it costs. Nearly half of the institutions we could read publish none. Only three, 6%, publish FAQPage, the type answer engines draw on most directly for question-shaped queries. Student queries are almost entirely question-shaped.
One institution publishes course schema that is invalid, because the type is written in lowercase and Schema.org types are case-sensitive. The markup exists, someone built it, and it validates as nothing.
- No structured data at all
- 25 · 46%
- Some schema, no Course type
- 18 · 34%
- Course schema present
- 11 · 20%
Only three institutions in the whole sample, 6%, publish FAQPage. Source: Axelerant audit, August 2026.
Why schema decays, and where to put it
Schema added page by page does not survive. Schema added to a course template does, because it inherits from the content model rather than an editor remembering. The step almost everyone skips is validation after deployment, and the invalid lowercase type we found is exactly what that omission produces: markup that exists, was paid for, and does nothing.
The questions applicants actually ask
We took one representative course page per institution and checked whether the facts an applicant needs are present in the server-rendered HTML, meaning the version a crawler sees.
- Weekly accommodation prices
- 0 institutions publish them
- Course fees
- 24 of 53 · 45%
- Entry requirements
- 18 of 53 · 34%
- Course estate on a second domain
- 7 institutions
Bars show the share of institutions where the fact is not readable by a crawler. Source: Axelerant audit, August 2026.
Where fees do appear, they are frequently in a tabbed component, a PDF, or a fees table on an entirely separate page from the course. Accommodation is worse: we could not find one institution publishing weekly room prices in crawlable HTML. Whether a room is en-suite, a question that genuinely decides offers, is routinely locked in a PDF or behind a booking portal login.
A student can ask an assistant whether they will be sharing a bathroom. No institution in this sample can have that answered from their own website.
Why fees in particular go missing
Fees are usually owned by finance or admissions rather than marketing, and they change on a different cycle, so they get published where their owner can control them. Each of those decisions is locally sensible and collectively fatal.
This is where discoverability and conversion stop being separate problems. The same fact that is unreachable by a crawler is unreachable by a hesitant applicant at 11pm. One change addresses both.
llms.txt, early and mostly done badly
An llms.txt file is a short curated map of the pages you would like an assistant to rely on. Seven institutions in 64 publish one, which for a convention this young is arguably ahead of the curve.
One of the seven pasted theirs directly out of ChatGPT without reading it. The file is live, in production, and still contains the model's own citation artefacts, eight times over. We are not naming them, and we do not think it is embarrassing: somebody understood the problem and moved faster than their review process. That is a better failure than not trying.
The reason we recommend it has little to do with the vendors. Writing one forces an institution to decide which thirty pages actually represent it. Every time we have run that argument, something surfaced that nobody realized was still live.
Platform is context, not cause
Every finding in this report is fixable on the platform the institution already runs. The distribution still matters, because it tells you where fixes have to land.
TerminalFour leads by a wide margin and is built specifically for universities. Institutions running it fail these checks at broadly the same rate as institutions running general-purpose systems. That is not a criticism of a vendor. It is evidence that no platform ships this switched on.
- TerminalFour
- 16 · 30%
- Not identifiable from public markers
- 15 · 28%
- Contensis
- 8 · 15%
- WordPress
- 4 · 7%
- Sitecore
- 3 · 6%
- Acquia Cloud
- 3 · 6%
- Craft CMS
- 2 · 4%
- Squiz Matrix, Drupal, Optimizely
- 3 · 6%
One primary platform per institution. A further 10 institutions could not be read at all.
When modernization is the answer, and when it is not
We do platform migrations for a living, so it is worth being straight: none of these findings require one. Modernization earns its place when the constraint is structural, a content model that cannot express a course as a course, an estate fragmented across a dozen systems, or a vendor that has stopped shipping the standards the work depends on. Those are a different conversation.
If somebody answers a discoverability problem with a replatform quote, ask which of the seven checks the new platform fixes on its own. The honest answer is none.
One institution got this right
Their robots.txt is hand-written and commented, and it carries a review date. It separates AI crawlers into categories and states a position on each: training crawlers blocked, answer-engine and user-fetch crawlers allowed, traditional search engines allowed, analytics tools named individually.
Somebody worked out what the categories were, decided what the institution wanted, and wrote it down where it takes effect. That is the entire intervention. It is not expensive and not technically difficult. It is simply owned.
The gap between the best institution in this sample and the median is not budget, headcount or platform.
What it is worth fixing
We do not hold conversion data for the 64 institutions in this study, and nobody does. What follows is evidence from our own delivery work in the sector, offered as an indication of scale. Both institutions agreed to be named.
Delivery evidence
University of East London
UEL was losing students and their website was working against them. We moved the platform from Sitecore to Drupal on Acquia Cloud, then ran a structured conversion programme across the admissions funnel: every drop-off point mapped and baselined, every candidate fix scored by impact, effort and value before anyone wrote code.
- +73%
- applications
- +32%
- enrolments
- +30%
- relevant traffic
- 3x
- faster publishing
Accessibility moved to WCAG 2.1 AA across the estate. The full case study is published here.
On attribution: those numbers came from platform work, funnel optimization and content governance together, not from discoverability work alone.
Delivery evidence
Regent's University London
Regent's engaged us to move its website from an informational estate to a conversion-focused one, with SEO and generative-engine optimization designed into the content migration rather than bolted on: a full information architecture rebuild, a content framework differentiated by course type, and a post-launch conversion programme contracted from the outset.
During the engagement, Regent's was observed ranking on Gemini and Google AI for a competitive undergraduate business course query. Not from a separate AI project or a platform change, but from structured content work done by a handful of people.
It is still in flight, and early is the honest way to present it. The rebuild launches as an MVP ahead of the January 2027 intake and the conversion programme has not yet run. What is demonstrated is narrower and still worth saying: content migration with generative-engine optimization designed in produced measurable answer-engine visibility inside a single project cycle.
Generative search results are volatile in a way traditional rankings are not. We report that ranking as an observation made during the engagement. Anyone is welcome to check it, including us.
What to do about it
| # | Action | Effort | Who owns it |
|---|---|---|---|
| 1 | Request your own homepage with an OAI-SearchBot user-agent and compare it to a browser. If it differs, a recruitment decision is being made inside a security tool. | Minutes | IT |
| 2 | Write an AI crawler policy. Decide separately on training, answer-engine and user-fetch crawlers. Put it in robots.txt, comment it, date it. | Hours | Marketing, with IT |
| 3 | Put fees and entry requirements in server-rendered HTML. Not a tab, not a PDF, not a table three clicks away. | Weeks | Web and content |
| 4 | Add Course, CourseInstance, Offer and FAQPage schema to course templates, then validate after deployment. | Weeks | Development |
| 5 | Get accommodation detail out of PDFs. Room types, bathrooms and weekly prices as HTML. | Weeks | Accommodation, with web |
| 6 | Publish an llms.txt. Cheap, and it forces a useful argument about which thirty pages represent you. Read it before you ship it. | Hours | Marketing |
| 7 | Give it an owner. The largest differentiator in this research was not budget or platform. It was accountability. | One conversation | Executive |
One note on sequence
Do 1 and 2 first, and not because they are quickest. Adding schema to a site whose edge is challenging answer engines produces no measurable change, and the team that did the work concludes structured data does not matter. Clear the blocking first, so everything after it can be attributed.
The reassuring part
Items 1, 2, 6 and 7 are minutes to hours of work with no procurement, no replatform and no capital expenditure. In a sector where roughly 45% of English providers project deficits for 2025-26, the highest-severity findings here are also the cheapest to fix.
Ask Foyer about this report
Questions about the method, what a finding means for your own estate, or what we have done for other UK institutions. Foyer answers from the report and from our higher education work.
Appendix · Seven checks
Per-check findings
Check 1
AI crawler policy
- robots.txt readable
- 52 of 64 · 81%
- Names no AI crawler at all
- 48 of 52 · 92%
- Names at least one
- 4 of 52
- Blocks AI search crawlers
- 0 of 52
Of the four who stated a position: one blocked seven training crawlers and deliberately left all five answer-engine and user-fetch agents reachable. One named two Anthropic agents only. One used path restrictions across three named agents. One blocked six training crawlers plus a Content-Signal ai-train=no header.
Check 2
Edge and WAF behavior
- Valid browser control obtained
- 54 of 64 · 84%
- No valid control, cannot conclude
- 10 of 64 · 16%
- Edge-blocking at least one AI crawler
- 6 of 54 · 11%
- Blocking all five agents tested
- 4 institutions
Agents blocked across the six affected institutions: GPTBot, OAI-SearchBot, ChatGPT-User, PerplexityBot, ClaudeBot. One blocks ClaudeBot only. One blocks OAI-SearchBot, PerplexityBot and ClaudeBot but serves GPTBot.
Check 3
Structured data
- No JSON-LD at all
- 25 of 54 · 46%
- Schema present but no Course type
- 18 of 54 · 34%
- Course schema present
- 11 of 54 · 20%
- FAQPage present
- 3 of 54 · 6%
- Structurally invalid schema
- 1 institution
The invalid case used a lowercase @type, which validates as nothing at all.
Check 4
Course page extractability
- Course page identified
- 53 of 54 · 98%
- No fees in raw HTML
- 24 of 53 · 45%
- No entry requirements in raw HTML
- 18 of 53 · 34%
- Course content JavaScript-injected
- 0 of 53
- Course estate on a second domain
- 7 institutions
JavaScript is not the villain here. The facts are simply not on the page.
Check 5
PDF dependency
- Weekly accommodation prices in crawlable HTML
- 0 institutions
Key applicant information is routinely held in PDFs rather than HTML.
Check 6
llms.txt
- Publishing one
- 7 of 64 · 11%
- Of those, containing unedited AI output
- 1
Check 7
Platform fingerprint
- TerminalFour
- 16 · 30%
- Not identifiable from public markers
- 15 · 28%
- Contensis
- 8 · 15%
- WordPress
- 4 · 7%
- Sitecore
- 3 · 6%
- Acquia Cloud
- 3 · 6%
- Craft CMS
- 2 · 4%
- Squiz Matrix, Drupal, Optimizely
- 3 · 6%
Primary platform, one per institution, across the readable sample of 54.
Appendix · All 64
Findings distribution
- Critical
- 7
- High
- 120
- Medium
- 46
- Low
- 47
220 findings across 64 institutions.
- No AI crawler policy stated
- 48 institutions
- No llms.txt
- 47 institutions
- Course facts missing from server-rendered HTML
- 28 institutions
- No FAQPage schema
- 26 institutions
- No structured data at all
- 25 institutions
- Schema present but no Course type
- 18 institutions
- Could not be read, reported unverified
- 10 institutions
- Course estate split across domains
- 7 institutions
Base: 64 institutions.
Chapter 10 · Anonymized, 64 rows
The sample, region by region
Pick a group to see what the audit found there. Every figure is computed from the same 64 anonymized rows, and the full table is below if you would rather read it directly.
All regions
64 institutions · 54 fully readable
Across all 64 institutions the pattern is the same in every region. Nobody blocks answer engines on purpose, almost nobody states a policy, and the facts an applicant asks for are the facts most often missing from the HTML.
- TerminalFour
- 16
- Contensis
- 8
- WordPress
- 4
- Acquia Cloud
- 3
- Sitecore
- 3
- Craft CMS
- 2
- Drupal
- 1
- Optimizely
- 1
- Squiz Matrix
- 1
Primary platform, one per institution. Estates we could not read are excluded.
- Name no AI crawler in robots.txt
- 48 of 52 · 92%
- Block AI agents at the network edge
- 6 of 54 · 11%
- Publish no JSON-LD at all
- 25 of 54 · 46%
- Course schema present
- 11 of 54 · 20%
- FAQPage present
- 3 of 54 · 6%
- Course facts incomplete in HTML
- 28 of 53 · 53%
- Publish an llms.txt
- 7 of 64 · 11%
Each bar states its own denominator. Unverified checks are excluded from that check only, never counted as a negative finding.
Show the underlying table · 64 rows
| Institution | Region | AI policy | Edge | JSON-LD | Course | FAQPage | Course facts | llms.txt | Platform |
|---|---|---|---|---|---|---|---|---|---|
| Institution 01 | North & Midlands | none stated | clear | yes | no | no | both present | no | not identified |
| Institution 02 | North & Midlands | none stated | BLOCKS 5 | no | no | no | both present | no | Craft CMS |
| Institution 03 | North & Midlands | none stated | clear | yes | no | no | both present | no | not identified |
| Institution 04 | North & Midlands | stated | clear | yes | no | no | missing entry+fees | no | not identified |
| Institution 05 | North & Midlands | none stated | clear | yes | no | no | missing entry+fees | yes | TerminalFour |
| Institution 06 | North & Midlands | none stated | clear | yes | yes | no | both present | yes | not identified |
| Institution 07 | North & Midlands | none stated | BLOCKS 1 | no | no | no | missing fees | no | TerminalFour |
| Institution 08 | North & Midlands | none stated | clear | yes | no | no | missing entry | no | Contensis |
| Institution 09 | North & Midlands | unverified | clear | yes | no | no | both present | no | WordPress |
| Institution 10 | North & Midlands | none stated | clear | no | no | no | missing fees | no | not identified |
| Institution 11 | North & Midlands | none stated | clear | no | no | no | missing entry+fees | no | not identified |
| Institution 12 | North & Midlands | none stated | clear | no | no | no | missing entry+fees | no | TerminalFour |
| Institution 13 | North & Midlands | none stated | clear | yes | yes | no | both present | no | Contensis |
| Institution 14 | North & Midlands | none stated | clear | no | no | no | missing entry | no | TerminalFour |
| Institution 15 | North & Midlands | none stated | clear | yes | no | no | missing fees | no | Sitecore |
| Institution 16 | North & Midlands | unverified | clear | no | no | no | both present | no | TerminalFour |
| Institution 17 | North & Midlands | none stated | clear | no | no | no | missing entry+fees | yes | TerminalFour |
| Institution 18 | North & Midlands | stated | clear | yes | no | no | both present | yes | Sitecore |
| Institution 19 | North & Midlands | unverified | unverified | unverified | unverified | unverified | unverified | no | unverified |
| Institution 20 | North & Midlands | none stated | clear | yes | yes | yes | both present | no | WordPress |
| Institution 21 | North & Midlands | none stated | clear | yes | no | no | both present | no | Acquia Cloud |
| Institution 22 | North & Midlands | none stated | clear | no | no | no | both present | no | Contensis |
| Institution 23 | North & Midlands | none stated | clear | yes | no | no | missing entry+fees | no | Contensis |
| Institution 24 | North & Midlands | none stated | clear | no | no | no | both present | no | not identified |
| Institution 25 | North & Midlands | none stated | clear | yes | no | no | both present | no | not identified |
| Institution 26 | North & Midlands | none stated | BLOCKS 5 | yes | yes | no | both present | no | not identified |
| Institution 27 | North & Midlands | none stated | BLOCKS 3 | yes | no | no | missing fees | yes | TerminalFour |
| Institution 28 | North & Midlands | none stated | clear | no | no | no | missing fees | no | Contensis |
| Institution 29 | North & Midlands | none stated | clear | no | no | no | unverified | no | TerminalFour |
| Institution 30 | Scotland, Wales & NI | none stated | clear | no | no | no | missing entry+fees | no | not identified |
| Institution 31 | Scotland, Wales & NI | none stated | clear | yes | no | no | missing entry+fees | no | not identified |
| Institution 32 | Scotland, Wales & NI | none stated | clear | no | no | no | both present | no | Acquia Cloud |
| Institution 33 | Scotland, Wales & NI | none stated | clear | no | no | no | both present | no | TerminalFour |
| Institution 34 | Scotland, Wales & NI | unverified | unverified | unverified | unverified | unverified | unverified | no | unverified |
| Institution 35 | Scotland, Wales & NI | none stated | clear | no | no | no | missing fees | no | not identified |
| Institution 36 | Scotland, Wales & NI | none stated | clear | no | no | no | missing entry+fees | no | Sitecore |
| Institution 37 | Scotland, Wales & NI | none stated | clear | no | no | no | missing entry+fees | no | not identified |
| Institution 38 | Scotland, Wales & NI | none stated | clear | no | no | no | missing entry+fees | no | WordPress |
| Institution 39 | Scotland, Wales & NI | none stated | clear | yes | no | no | missing entry+fees | no | not identified |
| Institution 40 | Scotland, Wales & NI | unverified | unverified | unverified | unverified | unverified | unverified | no | unverified |
| Institution 41 | Scotland, Wales & NI | none stated | clear | yes | no | no | missing fees | no | TerminalFour |
| Institution 42 | Scotland, Wales & NI | unverified | unverified | unverified | unverified | unverified | unverified | no | unverified |
| Institution 43 | Scotland, Wales & NI | none stated | BLOCKS 5 | yes | no | no | both present | no | WordPress |
| Institution 44 | Scotland, Wales & NI | unverified | clear | no | no | no | missing entry+fees | no | TerminalFour |
| Institution 45 | Scotland, Wales & NI | unverified | unverified | unverified | unverified | unverified | unverified | no | unverified |
| Institution 46 | Scotland, Wales & NI | none stated | clear | yes | yes | no | missing entry | no | Squiz Matrix |
| Institution 47 | Scotland, Wales & NI | unverified | unverified | unverified | unverified | unverified | unverified | no | unverified |
| Institution 48 | Scotland, Wales & NI | unverified | clear | no | no | no | both present | no | TerminalFour |
| Institution 49 | South & London | none stated | BLOCKS 4 | yes | yes | no | missing entry+fees | no | Craft CMS |
| Institution 50 | South & London | stated | clear | no | no | no | missing entry | no | TerminalFour |
| Institution 51 | South & London | none stated | clear | yes | yes | no | both present | no | Contensis |
| Institution 52 | South & London | none stated | clear | yes | yes | yes | both present | no | Acquia Cloud |
| Institution 53 | South & London | unverified | unverified | unverified | unverified | unverified | unverified | no | unverified |
| Institution 54 | South & London | unverified | unverified | unverified | unverified | unverified | unverified | no | unverified |
| Institution 55 | South & London | none stated | unverified | unverified | unverified | unverified | unverified | no | unverified |
| Institution 56 | South & London | none stated | clear | no | no | no | missing fees | no | TerminalFour |
| Institution 57 | South & London | none stated | clear | yes | yes | no | both present | yes | TerminalFour |
| Institution 58 | South & London | none stated | clear | no | no | no | missing fees | no | Drupal |
| Institution 59 | South & London | none stated | clear | yes | yes | no | both present | no | Optimizely |
| Institution 60 | South & London | none stated | clear | yes | yes | no | both present | no | Contensis |
| Institution 61 | South & London | none stated | clear | yes | no | no | both present | no | Contensis |
| Institution 62 | South & London | none stated | clear | yes | no | yes | both present | yes | TerminalFour |
| Institution 63 | South & London | stated | unverified | unverified | unverified | unverified | unverified | no | unverified |
| Institution 64 | South & London | none stated | clear | no | no | no | missing fees | no | not identified |
AI policy · "none stated" means robots.txt names no AI crawler at all.
Edge · "BLOCKS n" means n AI agents received an error while a browser control succeeded.
Course facts · whether entry requirements and fees appear in server-rendered HTML.
unverified · the check could not be completed. That is distinct from a negative finding.
What we did, and what we could not do
Sample
64 UK universities, university colleges and specialist institutions across England, London, Scotland, Wales and Northern Ireland. Russell Group and larger civic universities were excluded, because their resourcing and their problems are different.
What we did
Seven read-only checks per institution, all from public endpoints: AI crawler policy from robots.txt; edge behavior by requesting the homepage with each AI crawler user-agent against a browser and Googlebot control; JSON-LD extraction; course discovery via sitemap and homepage links; course extractability from server-rendered HTML; PDF dependency; llms.txt presence.
Coverage
54 of 64 institutions were fully readable. Percentages state their own denominator, and we never infer absence from a request that failed.
The bias in that gap
The ten we could not read are, by definition, the ones with more aggressive bot management. That is a systematic exclusion, so this report likely understates how much AI blocking exists.
Edge probes and their limits
Edge findings are reported only where a browser control returned successfully. Each agent was requested at least twice and reported as blocked only when attempts agreed. Strong evidence of a rule, not proof of one.
Anonymity
No audited institution is named. The two institutions in chapter 08 are our own clients, cited with permission, and were not part of the sample.
Every check here can be run against any domain, by anyone, in about a minute, using the free tool at axelerant.com/check. We would rather you verified this than believed it.
Who produced this
Axelerant
Axelerant is a digital engineering agency of 150+ people, founded in 2005 and remote-first since 2012. Higher education is one of the sectors we work in most.
We kept finding the same problems in individual university engagements, an unowned robots.txt, course facts trapped behind JavaScript, accommodation detail in PDFs, and had no way of knowing whether they were specific to our clients or general to the sector. This report is the answer. They were general.
Our declared interest. We are a commercial agency and we would be pleased to help. We are also an Acquia Elite Partner, and three of the 64 institutions here run Acquia. Nothing in our recommendations requires a change of platform, because the data does not support one.
Your institution is in this report
All 64 institutions have a section: the specific findings for your domain, the evidence behind each one, and what to fix in order. We would rather you had it before anyone else reads this.
The state of AI discoverability in UK higher education, 2026. Published by Axelerant, August 2026. Independent research, produced without vendor sponsorship. Sector financial context from the House of Commons Education Committee and Universities UK. AI search behavior data from EAB, 2026, US sample, cited as a behavioral signal rather than a UK figure. Institutional financial data from published statements, HESA and the Office for Students.