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SEO isn’t dead—it’s just different now
You’ve seen the headlines: SEO is over. But for higher education marketers and digital teams, that’s only half true.
What’s really happening is a shift (yes, quite a big one!).
Search engines are evolving, and AI-powered tools like ChatGPT, Google Gemini, and Perplexity are changing how students and families discover universities.
Instead of scanning a list of links, users now see AI-generated summaries—a little like what Google Snippets used to do. But it’s pulling them from ‘trusted’ content, not just top-ranked websites.
That means your university might not appear as a link, but it could be cited directly in the answer.
Effectively, it means that when AI answers a student's question for them, they don't need to click a link.
So your new SEO goal is to be the sole, trusted source cited in that AI-generated answer.
To be visible in this AI-first search world, higher ed teams need to think differently about SEO.
AI models, like you now, need to prioritize expertise, experience, authoritativeness, and trustworthiness.
Here’s how.
AI search thinks in context—not just keywords
Traditional SEO relied on matching keywords, earning backlinks, and optimizing metadata.
AI search engines, however, take a slightly more nuanced approach.
AI models like Perplexity, ChatGPT, and Gemini are trained to generate answers from authoritative sources that use natural language, structured layouts, and consistent topical depth.
They’ve gone beyond indexing pages to actually parsing meaning.
Here’s what that means for your college or university: your content has to be easy to parse and answer real questions; authority and trust matter more than keyword density; and clear headings, structured answers, and good linking are key to getting found.
So where do you start?
Reworking your content so it's modular is a project, so see if you can start with your top-performing pages: tuition info, program overviews, application steps.
Are they easy to understand? Do they reflect how people ask questions today?
Create modular, structured content that AI can reuse
AI models love structure. They don't read paragraphs; they parse data.
Treat every piece of admissions info as an answer chunk.
So start working towards modular content: short, labeled chunks that can stand alone.
This makes it easier for search engines to extract useful answers.
For example, instead of tucking admissions info deep in a long page, break it into clearly labeled blocks:
- “Minimum GPA for admission for med school”
- “Key dates for Spring 2026 applications”
- “Is this program available online?”
Use consistent heading styles (H2s, H3s), bullet lists, and schema markup where possible.
This content-modeling approach helps AI tools understand your content and allows AI to extract specific answers without needing to process a long narrative, increasing your chances of being cited in search responses.
McMaster University structures its ‘How to Apply’ content into distinct, FAQ-like segments, and breaks down complex information into concise, scannable blocks.
Build topical authority: depth matters
AI search engines want expert sources. That means your content needs to go beyond surface-level summaries.
If your college has standout programs—like cybersecurity, education, or environmental science—build deep, interconnected content around those topics. This could include:
- Detailed program pages
- Faculty profiles
- Student stories and career outcomes
- Linked research or news articles
The more depth and internal linking you provide, the more likely your site is to be recognized as a go-to source.
For example, if a student asks, “Is marine biology a good career path?” and your school has a robust section with outcomes, alumni stories, and faculty research, AI search may highlight your content—even without a direct brand mention.
The Open University effectively uses modular content and schema markup, to structure its extensive course and quaslification pages, which is essential for its status as a distance learning specialist. It allows AI tools to extract details like prerequisites and fees, significantly boosting their visibility and likelihood of citation for high-intent student queries.
Answer questions like a real person (ironic, right?)
Students don’t search with keywords, they ask full questions, like… students.
Your content should reflect that.
Incorporate natural-language queries and answer them directly.
Use FAQ sections, Q&A formats, or conversational copy that aligns with how students actually speak.
Try answering questions like:
- "How much is tuition for international students in Canada?”
- “What’s the difference between a BA and a BSc in psychology?”
- “Can I get credit for work experience?”
Tools like Google Search Console or Answer the Public can help you find the questions people are already asking.
Start there, and build content to match.
University of Michigan structures its complex application and admissions guides using tabbed interfaces and numbered steps, making each requirement a self-contained module, such as Application Plans (Early Action vs. Regular Decision), onto a distinct page.
This content is broken down into clearly labeled, side-by-side modules that detail the key differences, benefits, and required actions for each plan. This clear structure allows AI tools to precisely extract and contrast the two options. (For instance, an AI can easily cite the different decision notification dates for a student question like, "What’s the timeline difference between Early Action and Regular Decision at UM?).
Use schema markup to make your site machine-readable
Structured data (also called schema markup) tells AI and search engines exactly what’s on a page.
It doesn’t affect the user experience directly, but it’s key for discoverability.
The key isn't just if your university uses schema, but where you use it. The best performers apply structured data to their highest-value, high-intent pages:
- Course/Program pages ($Course$)
- Faculty pages ($Person$)
- Admissions/FAQ pages ($FAQ$, $HowTo$)
- Event pages ($Event$)
Adding this behind-the-scenes helps AI tools surface your content more accurately, especially for high-intent searches like 'computer science degree requirements' or 'next campus tour date.'
Arizona State University uses schema markup extensively across its program and event pages, helping boost visibility in AI-driven search summaries.
Not sure where to start?
If you're unsure where to start adapting your strategy for AI search, we suggest focusing on high-impact areas using the table below as your guide.
Start by reviewing your college or university's Admissions, Tuition, and Program FAQ pages to implement modular content, which involves structuring information into labeled, self-contained chunks that make it easier for AI models to parse.
Then, audit your university's high-value pages (like program guides and application steps) to ensure your content reflects natural language questions students are actually asking.
Next, you can work on schema markup for areas like Courses, Events, or About us to help AI precisely define and extract that data, ultimately building the topical authority you need to help establish your higher education institution as an expert source.
| AI SEO tactic | Higher ed focus | Why do it? |
|---|---|---|
| Modular content | Admissions, Tuition, Program FAQs | Facilitates direct citation in AI answers |
| Topical authority | Faculty Research, Alumni outcomes | Establishes your site as an expert source |
| Natural language | Program Q&A, Application guides | Matches how students now search (full questions) |
| Schema markup | Courses, Events, About | Clearly defines high-value data for machine parsing |
University of Central Florida uses a modular, card-based format for its Event Listings (like campus tours and info sessions), standardizing key data points like date, time, and location. This uniformity allows AI tools to precisely extract information using the $Event$ schema, so students can get accurate, up-to-the-minute summaries on upcoming campus activities.
Keep testing and adapting
We’re in the early days of AI search—and things are moving fast. There’s no fixed rulebook, but your university or college can:
- Track how your institution appears in AI-generated answers
- Monitor traffic sources in analytics to catch shifts
- Refresh and restructure older content with better headings, clarity, and links
- Stay on top of new tools and guidelines as Gemini and others evolve
Above all, keep your content focused on clarity, credibility, and usefulness. That’s what AI models are designed to reward.
AI hasn’t killed SEO—it’s just changed some of the rules of the game.
For higher education teams, now is the time to rethink how content is structured and discovered.
That visibility can shape real decisions, from first impressions to final applications.
If you want a deeper dive into this shift, read our previous article, AEO: The Future of Search in Higher Education and AI-writing assistant tips for higher education marketers.
How are you adjusting your SEO strategy for AI search? What results are you seeing so far?