How universities can win in the age of AI search

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Student discovery is changing. But how can higher education marketing teams get ahead of it?

Not that long ago, student search was relatively simple: get found on the Google SERPs, drive traffic to your website, and nurture from there. But it’s safe to say that those days are behind us.

Today’s prospective students are bouncing between TikTok, Reddit threads, university rankings, and AI tools. They’re asking ChatGPT questions like, “What are the best universities to study psychology?” and scanning Google’s AI Overviews without clicking on a single website link, often making decisions without even seeing your homepage.

It’s easy to interpret this as a threat. In reality, it’s a reset. For universities willing to adapt, AI search presents a chance to become more visible, more helpful, and more relevant in the places students actually look.

When we talk about AI search, we mean tools and features that give students a direct answer, not just a list of links. This is often referred to as Answer Engine Optimisation (AEO), and it means we now need to optimise our content strategy to appear inside those AI-generated answers.

Instead of typing “business degree midwest” into Google and scanning ten blue links, a student can now refine their research process by asking: “What are the best affordable business degrees in the Midwest with strong internships?”, and get a synthesised response from an AI assistant. That answer might include a shortlist of universities, a quick comparison of strengths, a rough idea of costs, and maybe a handful of links if they want to dig a little deeper.

Underneath that experience are Large Language Models (LLMs): sophisticated systems trained on huge amounts of text that can understand natural language, spot patterns, and generate fluent replies. They pull from your website, rankings, reviews, news, directories, forums, social content and more.

The new challenge for universities is to get cited in AI answers. That’s where Generative Engine Optimisation (GEO) and Large Language Model Optimisation (LLMO) come in – both part of a broader AI Optimisation (AIO) approach.

Traditional SEO helps you rank in Google; AIO helps you show up across AI models. It isn’t a total reinvention, just an evolution: more answer-focused content, the same high-quality SEO foundations, and a strategy built for how students now search.

For marketing teams, the important shift is that you’re no longer just optimising for a click on a results page. You’re optimising for how AI describes you, ranks you, and recommends you.

So, how is this directly affecting the research, discovery and search habits of your target market: students?

Across recent higher education research, a few trends stand out:

● AI tools are becoming the default starting point for more complex questions about programmes, careers, and study options

● Usage is skewed heavily towards younger audiences. A far higher share of 18–24-year-olds are regular ChatGPT users compared with Google’s user base in the same age band

● Even Google users are changing the way they search from keyword-based research to long-form questions

Students are asking AI tools to shortlist universities that fit their budget and grades, to compare them with competitors, to decode jargon, and to help them decide what to ask at open days. They still use Google, but increasingly, they’re arriving there after AI has already narrowed the field.

And that journey is fragmented. A student might:

See a TikTok about “day in the life at X”
Read Reddit threads about whether that degree is worth it
Ask ChatGPT for similar universities in different cities
See a Google AI Overview summarising three or four options
Then, finally, visit your site (if you made the cut)

AI hasn’t replaced the rest of the journey, but it has reshuffled it. It’s sitting earlier in the decision-making process, shaping who even makes it onto the shortlist.

Three big implications are worth paying attention to.

First, high rankings on Google no longer guarantee visibility. AI Overviews now sit above your organic and paid results, and more users are switching into “AI mode” instead of scrolling traditional SERPs. Research shows click-through rates drop sharply when an AI summary appears, even for pages in the top spots. You can be number one and still be practically invisible if you’re not included in the AI answer.

Second, paid search is under pressure, but it remains essential. When an AI block sits above your ads, it naturally soaks up some of the attention you’ve paid for, so you may see fewer clicks from the same spend. That doesn’t mean you should turn paid off; it means you should use it differently. Paid search becomes one part of a broader mix that keeps your brand visible across multiple touchpoints, reinforces key messages, and captures high-intent demand that AI has helped to create.

Third, attribution is getting messy. A growing share of discovery is happening in places your analytics can’t easily see: inside AI assistants, on social platforms, in private group chats and semi-anonymous communities. By the time a student lands on your site via a branded query, a lot of influence has already happened elsewhere.

This can make performance look worse than it is if you’re only watching last-click metrics.

Here’s the good news: AI doesn’t invent your story. It learns it.

Those AI answers students see are built from the content and signals you and others put into the world via your own site, third-party profiles, rankings, student reviews, media coverage, social content, and more. That means you can influence how you show up in AI search by improving the quality, clarity, and consistency of that ecosystem.

There’s also a strategic opportunity in being human. As generative AI makes it easier to flood the internet with generic content, universities can stand out with the things AI can’t fabricate: lived experience, specific outcomes, local context, real student voices, staff and faculty perspectives. When that material is easy for both students and machines to understand, AI will often amplify it.

Most institutions aren’t there yet. Many are still firmly in “wait and see” mode. That gives early movers a rare chance to set the tone in their subject areas and regions, rather than playing catch-up later.

So what can universities actually do to seize the opportunity that AI presents? Here are some concrete starting points:

1. Audit your AI presence
Search for your institution and priority programmes in tools like ChatGPT, Claude, Gemini and Perplexity, and explore Google results with AI Overviews. Use student-style questions. Note where you appear, how you’re described, who else is mentioned, and which sources are being cited. This gives you a baseline.

2. Make key pages AI and student-friendly
Refresh programme and subject pages so they use clear, natural language and directly answer the questions students are actually asking. Break content into logical sections, add simple explanations, and include FAQ blocks that mirror likely prompts. Work with your web team to improve schema markup so that courses, FAQs and events are easy for machines to interpret.

3. Treat search as an ecosystem, not a single channel
Align your core story across your website, email, social, video, rankings profiles and campaign activity. Think about YouTube as a search engine in its own right. Make sure a student gets the same sense of who you are, whether they meet you in an AI answer, a TikTok, a Reddit thread or an email.

4. Build a searchable video layer
Create videos that genuinely help students make decisions: explainers on finance and applications, honest student Q&As, day-in-the-life clips for different types of learners. Optimise titles, descriptions and captions using the language students use. Remember that AI can and will read your transcripts.

5. Listen and participate in digital communities
Monitor Reddit, Niche and other forums where your brand shows up. Use those conversations as insight into student concerns and misconceptions. Where it’s appropriate, empower staff or student ambassadors to answer questions in a human, helpful way. That peer-to-peer content often feeds directly into how AI tools learn about you.

6. Update your success metrics
Alongside rankings and traffic, track signals of brand demand: direct visits, branded search, open day registrations, and self-reported “How did you hear about us?” data. Over time, keep an eye on how your presence in AI answers evolves as you improve your content and ecosystem.

7. Blocking paid ads in AI platforms
AI tools already feel mostly ad-free to students. When they ask ChatGPT or Gemini for the “best universities for X”, they see answers – not obvious ads. As these platforms mature, it’s likely students will have more ways to limit or block paid messages altogether.

That doesn’t mean paid search no longer matters. It means you can’t rely on it inside AI platforms. Paid should work alongside your organic and AI strategies, amplifying a strong story that’s already showing up in AI answers, not trying to replace it.

AI isn’t a passing phase. It’s now a part of how students think, search and make decisions.

The real question for universities is: will you let AI define you by default, or will you take an active role in shaping how you appear in this new landscape?

If you’d like to understand how AI currently talks about your institution and what it would take to turn that into a competitive advantage, get in touch with the Hybrid team.

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