For years, the pursuit of search rankings led to some truly awkward content.
You know the copy. The program page headline that shoehorns “top-ranked nursing program in [city]” into a sentence. Words repeated a strange number of times on the page because you’re hoping to rank for a specific keyword. The outcomes section that’s really just a bulleted list of statistics and keywords with no context or story.
This wasn’t unique to higher education; anyone trying to rank in search often wound up writing keyword-stuffed content that just felt a little “off” due to the repetitive phrasing. The intent was visibility. The result was prose that didn’t serve the reader particularly well.
Now we’re navigating the wild, strange world of AI search and Google’s AI Overviews. We’re still writing for algorithms. But these algorithms work differently—and they reward different qualities. AI visibility belongs to content that scores well on trust, coverage, clarity, specificity, and machine interpretability. Content must be architected to be cited, not just indexed.
The brands and institutions that engineer relevance—that build content designed to be quoted, not just found—will define the winners of AI search. And writing for these systems actually leads us to do a better job: clear, straightforward, authoritative prose that serves readers first.
Two Problems, One Outcome
Higher ed websites often suffer from two distinct content problems that both hurt readers.
Problem one is SEO-driven awkwardness. Keyword-stuffed headlines. Unnatural phrasing designed to match search queries. Meta descriptions that read like they were written for crawlers. This is what happens when people optimize for algorithms they don’t fully understand.
Problem two is institutional blandness. Verbose mission language that could describe any university. Committee-approved copy that’s been sanded down until it says nothing real. Pages organized by org chart instead of by what prospective students actually want to know.
These problems have different causes, one comes from chasing rankings, the other from institutional risk aversion, but they produce the same outcome: content that doesn’t serve readers. And content that doesn’t serve readers now fails the test AI systems use to decide what’s worth citing.
AI search systems are designed to demote both problems. Keyword stuffing doesn’t help when the algorithm evaluates passages for genuine expertise. Bland, generic copy fails when the system is looking for specific, quotable answers. Neither type of content is architected to be cited.
The path forward addresses both problems at once: clear, specific, well-structured content that sounds like humans wrote it for humans, built with enough clarity and specificity that AI systems can confidently quote it.
Still Optimizing, Just Differently
This isn’t a post telling you to ignore search optimization. AI search still uses retrieval. Your content still needs to be found, indexed, and selected. The technical fundamentals of SEO, crawlability, site speed, structured data, internal linking, are very important.
What’s changed is what the systems reward once they find you.
Google’s helpful content guidelines now explicitly favor “people-first” pages, content that demonstrates genuine expertise, satisfies real intent, and feels useful to read. AI answer engines like ChatGPT, Perplexity, and Google’s AI Overviews aren’t just ranking pages. They’re retrieving passages, evaluating them for clarity and credibility, and deciding whether to quote you. The question isn’t just “will this rank?” It’s “is this clear and trustworthy enough to cite?”
A January 2026 Semrush study analyzed 304,000+ URLs cited by ChatGPT, Google AI Mode, and Perplexity. The researchers evaluated 13 different content parameters to see which ones actually correlated with being cited by AI systems.
Only five parameters showed a strong positive correlation. And they all come back to reader-first fundamentals: clarity, structure, and demonstrated expertise. The classic SEO metrics—domain authority, backlink profiles, keyword density—showed only weak correlation with whether a page was actually cited.
As Kyle Morley from Semrush put it when sharing the findings: “Traditional SEO tells you how to rank. AI search is teaching us how to be referenced.”
Traditional rankings meant appearing in a list of ten blue links—you optimized to show up, and then hoped someone clicked. Being referenced by AI means your content was clear and credible enough that the system quoted it directly in the answer. You’re not just visible; you’re the source. That’s a higher bar, and it rewards different qualities: specificity, structure, and genuine expertise over keyword matching and link accumulation.
Why Your SEO Rankings Don't Guarantee AI Visibility
Research from Ziptie, Profound, and Ahrefs shows only 25-39% overlap between pages ranking in Google’s top 10 and pages appearing in AI search results.
How is that possible? The answer is something called query fan out.
When you type a question into ChatGPT or trigger an AI Overview, the system doesn’t just search for your exact query. It breaks your question into 5-10 synthetic sub-queries and pulls results from all of them. Ask about “training for the New York Marathon” and the system might also search “how to run 26.2 miles,” “marathon training checklist,” “beginner marathon programs,” and more—then synthesize passages from across all those results.
The pages that appear in AI answers aren’t necessarily the ones ranking for your original search. They’re the pages that happen to be relevant across a constellation of related queries you never see.
And according to Ahrefs, 28.3% of the queries AI systems use have zero search volume in traditional keyword tools. The synthetic queries these models generate don’t map to what humans actually type into Google. You can’t just “rank for” them in the traditional sense.
So what can you do? Engineer relevance across the topic, not just for one keyword. Create content that’s genuinely comprehensive, well-structured, and clearly authoritative, content that could plausibly be useful for any of the sub-queries an AI might generate. Content architected to be cited no matter which angle the system approaches it from.
The Algorithm Caught Up to the Reader
For the first time, what’s good for readers and what’s good for algorithms are converging.
Mike King at iPullrank frames it this way: “Structured content will always work better for people, search engines, and generative environments.”
The same qualities that make a page scannable and useful to a prospective student, clear headings, focused sections, specific details, logical flow, are exactly what AI systems need to retrieve and cite your content confidently.
Modern AI doesn’t evaluate your page as a monolith. It evaluates it at the passage level—chunk by chunk. Google’s been doing passage-level indexing since around 2018. AI answer engines take this further, retrieving individual sections and deciding whether each one is clear enough, specific enough, and credible enough to include in a synthesized response.
Walls of text underperform; well-structured content performs better on engagement, linking, and time on site. The AI research has now validated what UX practitioners knew all along.
Each section of your page needs to stand on its own as a complete signal. King demonstrates that simply splitting a long paragraph into two focused paragraphs can improve cosine similarity scores by 15%, making that content more likely to be retrieved for relevant queries.
This is what “chunking” actually means: not creating artificially bite-sized content, but structuring your writing so that each section has its own entity, context, and claim. A clear heading. A direct answer in the first sentence or two. Specific supporting details. Each chunk architected to be quotable on its own.
That’s also just good writing. It’s what Cyrus Shepard was advocating back in his classic Moz posts about content structure—walls of text underperform; well-structured content performs better on engagement, linking, and time on site. The AI research has now validated what UX practitioners knew all along.
What Reader-First Content Looks Like on a .edu
What does content architected to be cited actually look like in practice?
Program pages that lead with “why this, why here, why now”, not institutional boilerplate. Content should answer the question a prospective student is actually asking when they land on that page.
Outcomes sections that pair data with stories. Not just “92% employment rate within 6 months”, but who those graduates are, where they work, and what path got them there. AI systems love specificity because it’s verifiable and quotable. Readers love it because it’s relatable.
Admissions content that answers hard questions directly. What does this actually cost after aid? What support exists for first-gen students? What do students say about the experience, in their own words?
Content structured in clear, scannable chunks with headings that match real questions. Each section should be able to stand alone as a citable passage, because that’s how AI systems will evaluate and retrieve it.
Authentic voice throughout. Content that sounds like your faculty, your students, your institution, not like generic content that could be swapped across any site with a find-and-replace on the school name.
The Technical Work Still Matters (But It's Different Now)
Reader-first content doesn’t mean ignoring technical optimization. It means the technical work now serves the same goal as the content work—engineering relevance at every level, from prose to infrastructure. Some of the specifics have shifted.
Meta descriptions are now a ranking factor. Not for Google (which rewrites them 80% of the time anyway), but for AI systems. When ChatGPT or Perplexity retrieves search results, your meta description is what they see before deciding whether to request the full page. It’s your advertisement to the LLM. Make it answer the question clearly enough that the system wants to cite you.
URL structure matters more than you think. Research from Profound shows pages with keyword-relevant URL slugs get 11.4% more AI citations. Google stopped caring much about URLs years ago, but AI systems still use them as relevance signals.
Page speed is critical—and the threshold is tighter. AI systems like ChatGPT fetch pages in real-time. If your server takes too long to respond, you get a 499 error (client gave up waiting) and your content isn’t even considered. Mike King’s research shows sites experiencing 499 spikes see significant drops in ChatGPT visibility. This isn’t theoretical—it’s happening now. You can’t be cited if you can’t be reached.
Clear heading hierarchy isn’t just for screen readers—it’s how AI systems understand the structure of your argument and retrieve the right passages. It’s part of machine interpretability.
Schema markup that tells machines what your content is about—Article, FAQ, Organization—helps AI systems cite you accurately and confidently. It makes your content more interpretable.
Internal links between related pages don’t just distribute page authority—they help AI understand the relationships between concepts and build a more complete picture of what you offer. They’re part of how you engineer relevance across your site, not just on individual pages.
The technical fundamentals are still foundational. What’s changed is that some factors matter more for AI than they did for traditional search, and vice versa.
This Is Also Reputation Management
AI search optimization is closer to reputation management than traditional SEO.
AI systems don’t just pull from your site. They synthesize information from across the web—and the consensus wins.
Engineering relevance means engineering it everywhere your institution appears, not just on the pages you control directly. The broader narrative matters.
If outdated descriptions of your institution live on third-party sites, AI might surface those instead of your current messaging. If competitors or directories say something different about your programs, that becomes part of the answer. You’re not just optimizing pages; you’re trying to shape how the entire content ecosystem talks about you.
For higher ed, this means thinking beyond your .edu. Are your program descriptions consistent across your site, third-party directories, and partner pages? Are student success stories being told in places AI systems will find them? Is your messaging about costs, outcomes, and student support showing up in the right places? And Reddit! What is happening on Reddit?
Engineering relevance means engineering it everywhere your institution appears, not just on the pages you control directly. The broader narrative matters.
Evidence Density Matters
Mike King introduces a concept worth adopting: “evidence density”—the ratio of meaningful, verifiable information to total words.
AI systems favor passages with high evidence density. Dense, specific paragraphs beat long, rambling ones for both readers and LLMs. High evidence density is part of what makes content citable—it gives the system something concrete to quote.
Think about your current content. How many words on your program pages are actually doing work? How many are filler, hedge language, or generic claims that don’t add information?
Specificity is the key. “Students gain hands-on experience” is low evidence density. “Nursing students complete 800+ clinical hours across five Boston-area hospital systems before graduation” is high evidence density. The second version serves readers and gives AI systems something concrete to retrieve and cite.
A Reader's Bill of Rights for Higher Ed Websites
So how do you take all of these ideas and apply them to your content? Think about your readers and what they need. If you’re looking for a simple test for your content, consider whether your site honors these reader expectations:
I deserve answers, not just information. Don’t make me hunt through paragraphs to find out what I actually want to know.
I deserve stories, not just statistics. Numbers mean more when I can see myself in them.
I deserve honesty, not just positioning. Tell me what’s hard along with what’s great.
I deserve your real voice, not brand speak. Sound like actual humans at your institution, not a committee.
I deserve navigation that matches my questions. Organize content around what I need, not how your departments are structured.
I deserve content that respects my time. Each section should earn its place on the page.
If your content passes this test, it is probably structured well for AI search too. Content that honors readers is content architected to be cited.
The Alignment You Were Waiting For
The content that genuinely helps prospective students understand your institution: clear, specific, story-driven, honest, well-structured, is now the content that AI systems want to cite and surface.
The awkward SEO copy can finally retire. So can the bland institutional filler. Neither was serving readers.
You’re still optimizing. You’re still thinking about structure, about technical fundamentals, about how systems retrieve and evaluate your content. But now that work aligns with creating genuinely useful pages. Engineering relevance and serving readers are finally the same thing.
Write for humans. Structure it well. Make every section earn its place. Architect your content to be cited.
Where to Start
If you’re ready to move from awkward to aligned, start with your highest-stakes content, probably your top program pages and key admissions entry points.
Ask: Does this answer the real question someone has when they land here? Is every section specific enough to stand alone as a citable passage? Does this sound like us?
Then look at structure: Clear headings? Short paragraphs? Scannable? Each chunk focused on one idea? Linked to related content?
And check the technical foundation: Fast load times? Clean HTML hierarchy? Schema markup? Meta descriptions that actually answer the query? URL slugs that signal relevance?
You don’t need to rebuild your entire site overnight. But the sooner you start treating reader-first quality and technical structure as your primary metrics, the better positioned you’ll be—in traditional search, in AI search, and with the actual humans you’re trying to reach.
The institutions that engineer relevance now will define the winners of AI search. Power to the reader. The algorithms are finally on their side.
