The Future of Search and What It Means for Universities

Most marketers in higher education will know that search is rapidly evolving, changing from classic webpage rankings to ‑AI-driven‑ assistants and discovery across multiple platforms. Universities find themselves at the crossroads of a fundamental shift in how their audiences are behaving, how they find information, and how they decide to engage.

Drawing on the accumulated insights from our work at Remarkable (on discoverability, accessibility, AI search‑, social search, content and brand visibility) and the latest data in higher education, this article explores what the “future of search” means for educational institutions, from prospective undergraduates to press and research audiences, surfacing actionable guidance for marketing, technology and academic leadership.

Why the future of search matters for universities

Our previous insight articles (such as Staying Discoverable, The Power of Accessibility, Discoverability for Insurance Brands, The Future of Search and AI, AISearch, SEO, AEO, GEO, UGC and Advocacy, Discovery in FSI Social Search, and others) have explored how search and discovery are shifting from linear keyword-to-website models into multisource, AI-enhanced ecosystems.

Now, for universities, this change is not just a peripheral distraction, it’s central.

Here’s why:

• A recent higher ‑education marketing survey found that 67% of people use search engines first when researching colleges and universities.
• In that same data set, 61% of site traffic for higher education stems from organic search – underlining that discoverability still matters.
• At the same time, we know from general digital behaviour that users now draw on multiple search and discovery sources, not just the “10 blue links” of Google but AI assistants, voice search, social media search, app store search, knowledge panels, and more.
• This means that universities need to think not just about “ranking for keywords” but about being visible and cited across multiple platforms and algorithms.

In effect: the era of “just optimise for Google and hope traffic comes” is entering a new phase. For universities, this shift has fairly profound consequences: for student recruitment, for research dissemination, for brand authority, for alumni and donor engagement, for press coverage and more.

Changing user behaviour – audiences at a glance

It’s important we consider the key user‑groups for a university, and how their behaviour is changing (and will continue to change) in the future‑of‑search era.

1. Prospective undergraduate (and postgraduate) students

These are the individuals who may be ages 17–24 (undergraduates) or older for postgraduate/online learners. Their behaviour is shifting in several ways:

• They expect instant answers, often via AI assistants and aggregated sources, rather than drilling through multiple websites.
• They rely on multiple discovery channels: search engines, social media search, YouTube, app‑based search, voice assistants. A stat: 50% of younger applicants use more than five social media sites for college research.
• They expect personalised, relevant content – not generic institutional messaging. Data show 80% of students now expect personalised communication from institutions.
• They increasingly value brand authority and reputation – when an AI or search engine finds a summary of a university’s credentials, rankings, campus life or student outcomes, that may heavily shape their perception before they ever click.
• Because their initial “research” may stop at an answer box or snippet, the concept of “click‑through to the website” becomes less guaranteed. This creates what we might call a “great decoupling” of traffic vs visibility.

2. Researchers, press and academic peers

For this audience of faculty, research collaborators, media and thought‐leaders, discoverability has different dynamics:

• These users often seek specific answers or references, and may interact via more specialised search systems (academic search, Google Scholar, AI assistants summarising academic work).
• They expect structured, high‑quality, citable content (papers, data sets, briefings) that can be easily pulled into knowledge graphs, AI responses and “answer engine” formats.
• They also rely on brand authority and institutional credibility: if an AI assistant is summarising “what university did X research on Y”, the institution’s visibility in that context matters.
• Their behaviour is shifting to less website‑page visits and more knowledge‑panel references, aggregated insights, citation in summary responses – which means institutions must optimise for being cited, not only clicked.

3. Parents, alumni, donors and community stakeholders

Though sometimes overlooked, these audiences matter for institutional reputation, fundraising, retention and word‑of‑mouth. Their search behaviour is increasingly:

• Querying via mobile/voice assistants (“Which universities in London offer environmental science with strong placements?”)
• Looking for trust signals (rankings, alumni testimonials, outcomes).
• Encountering AI‑generated summaries and new discovery sources (e.g. chatbots summarising alumni success, voice assistants delivering institutional facts) – meaning if the university isn’t surfaced, they may never visit the main website.

Summary of behavioural shifts

• More multi‑source discovery (not just Google search).
• The creation of direct answers (via AI assistants) means fewer “click‑throughs” in some cases, leading to visibility without traffic.
• Higher expectations on brand authority and institutional “fame” (being known, referenced, cited).
• Search & discovery are becoming cross‑platform, voice aware, snippet‑aware, knowledge‑graph aware.

Metrics are shifting: being featured, cited, referenced becomes as important as website visits and keyword ranking.

Implications for universities

What does all this mean practically for universities? The implications are profound – and they demand a shift in mindset as well as capability.

The “Great Decoupling”

As mentioned, institutions may find that content is used by AI assistants or aggregated into answer boxes without users ever clicking through. If you rely solely on website traffic as your KPI, you risk missing this phenomenon. Visibility may happen outside your domain.

Your content could be summarised, but the click count may stay flat.

Shift from traditional SEO to AEO/GEO

Traditional SEO (keyword rankings, backlinks) remains important – but increasingly universities must optimise for:

• Answer Engine Optimisation (AEO) – so that a piece of your content becomes the answer the AI assistant serves.
• Generative Engine Optimisation (GEO) – so that your institution’s content is pulled into generative responses.
  In other words: you need to think about structuring content so that LLMs and AI search tools can easily parse, reference and quote it (e.g. clear headings, schema markup, FAQ sections, authoritative citations).

Multi‑source brand “fame”

You can no longer rely solely on ranking for search terms on Google. Because users under 40 now may use “up to 5 search/discovery sources” for decision‑making, you must build presence across platforms: social search, YouTube search, app store discovery, voice assistants, knowledge panels.

Building your brand so that it is recognised and cited is critical. If an AI assistant is generating a summary and your institution is not in the knowledge graph (or your content not structured for extraction), you may be omitted.

Technical optimisation + Information Architecture + UX

Given the above, it’s no longer enough for a university website to load pages and include keywords. To be discoverable in the future of search you must:

• Ensure technical SEO is optimised (site speed, mobile‑first, structured data/schema, crawlability).
• Organise content with robust information architecture (IA) – clear taxonomy of programmes, courses, research output, news/events, alumni stories. This helps both human users and AI‑systems navigate.
• Ensure UX is aligned: streamlined paths for prospective students, structured metadata for researchers/press, accessible design (we’ve previously emphasised accessibility as a key pillar).
• Use structured data/schema markup for programmes, faculty, research outputs, FAQs – this improves the chance of being discovered in rich results, knowledge panels or AI answer feeds.

Content strategy in the age of AI

Content remains king – but content strategy must evolve:

• Focus on in‑depth, authoritative content (e.g., program pages, research summaries, faculty profiles, outcomes) that is structured for AI extraction.
• Use networks of content (blogs, research briefs, alumni stories) that can feed into multiple platforms: your site, partner sites, social, knowledge‑panels.
• Leverage user‑generated content (UGC) & advocacy (student stories, alumni testimonials) which help build brand trust and broaden the citation footprint.
• Always keep accessibility in mind – accessible content is more widely usable and more likely to be referenced by AI assistant chains (we’ve covered this in a previous article).
• Consider content designed for different intent and channels: e.g., voice‑friendly FAQ pages, YouTube transcripts, short‑form social video captions (since many students begin with video).
• Measure beyond clicks: focus on mentions, citations, knowledge panel inclusion, AI answer appearance.

Brand and visibility as strategic assets

In the era of generative‐search, brand becomes a “signal of trust” for AI and human users alike. So universities should:

• Monitor their brand footprint across platforms: Are you in knowledge graphs? Are you referenced by trusted third‑party sources?
• Ensure citations and partnerships: publish research in reputable outlets, ensure your institution is referenced in summaries and databases that AI may draw upon.
• Ensure that press & media coverage, research output, alumni success stories are structured and promoted in ways that boost visibility for AI extraction.

Understand that visibility may decouple from traffic: being “seen” in AI/assistant responses counts as success even if you don’t get the click. But you still want to convert once they visit.

Actionable Points for University Marketing & Tech Leaders

Here are practical steps you can take right now to respond to the future of search:

1. Audit your citation footprint and AI‑visibility
   • Map where your institution is referenced across knowledge graphs, academic databases, third‑party portals, and social search.
   • Identify major programmes, research centres and faculty outputs that are not being cited or surfaced by AI/assistant responses.
   • Use this as a baseline for improvement.
2. Upgrade your technical SEO and IA
   • Ensure site performance, mobile‑first, HTTPS, structured data/schema markup (Programme, Person, Organization, Event, ResearchProject).
   • Review your information architecture: ensure programmes, courses, research outputs are clearly and consistently structured.
   • Make sure your UX supports both prospective students (clear pathways) and researchers/press (easy access to research summaries, data sets, credentials).
3. Design a hybrid optimisation strategy: SEO + AEO/GEO
   • Continue keyword research, backlinks and content optimisation for classic SEO.
   • Add specific content elements for AI: FAQ pages, structured Q&A, concise summaries of research, schema markup that aids AEO.
   • Monitor whether your content is being picked up in answer boxes, chat‐assistant responses, knowledge panels.
   • Re‑optimise content accordingly.
4. Develop content with structure, depth & multi‑channel reach
   • Create high‑quality long‑form content for flagship programmes and research centres (which organisations/LLMs may cite).
   • Use micro‑content (video, transcripts, infographics) optimised for social search and YouTube/Instagram search.
   • Encourage student/alumni testimonials and UGC, and ensure it is structured (tagged, meta‑data, schema) so it can feed discovery systems.
   • Maintain accessibility standards (per our earlier article) to ensure wider usability and discoverability by AI systems.
5. Strengthen your institutional brand across sources
   • Work with academic partners, media outlets and third‑party aggregators to increase third‑party references (which help visibility).
   • Monitor brand mentions, knowledge panel presence, ranking in domain authority databases.
   • Use PR and research dissemination strategically: when your institution appears in peer‑reviewed publications, accessible research hubs, media summaries, it increases the supply of citations that LLMs may draw from.
6. Adapt measurement and KPIs
   • In addition to standard metrics (traffic, rankings, bounce rate), monitor:
   – Appearances in “answer box” / featured snippet / knowledge panel.
   – Mentions/citations of institution in AI/assistant responses (where trackable).
   – Brand searches (people typing your institution’s name) and cross‑platform searches.
   – Conversion quality over volume: e.g., high‑intent engagements from search/discovery sources.
   • Revise reporting cadence: as one higher‑ed study found, 62% of university leaders want consistent SEO reporting but only 31% receive it. (Search Influence)
7. Form cross‑functional teams bridging marketing, IT, content and research
   • As the search/discovery world evolves, organised silos won’t suffice. Marketing, digital experience, IT, research dissemination and brand teams must coordinate.
   • Ensure that research outputs, faculty publications, alumni stories, programme pages all align with the technical and discovery optimisation agenda.
   • Develop an “AI‑visibility” mindset across functions – it’s not just the marketing team’s job, it’s institutional.

Now’s the time to plan for the future of search for educational institutions

The future of search is no longer simply “we focus on organic ranking and website traffic”. For universities, it means rethinking how you get discovered, how you are cited, how you are known, and how you convert once someone finds you - whether via a search engine, an AI assistant, a voice device or a social search.

Your audiences – prospective students, researchers/press, alumni and partners – are evolving in behaviour and expectation. They will use multiple discovery sources, expect instant answers, and rely on brand familiarity and trust. If your institution is not optimised for this new ecosystem (technically, structurally and content‑wise), you risk being invisible in crucial moments when decisions are made.

But there is great opportunity: universities that proactively adapt will lead in recruitment, research visibility and brand authority.

If you’re a university marketing or technology leader looking to take the next step, the Remarkable team would like to help. We’re offering a free initial AI‑visibility and citation audit tailored for educational institutions – designed to map your current discoverability across AI/search/discovery systems and identify the quick wins and strategic gaps. If you’d like us to take a look at your institution’s visibility in the future of search, let’s talk.