By now, higher education marketers understand that institutional visibility in AI-driven search is no longer optional—it’s a critical touchpoint in the student’s evaluation process.
However, focusing narrowly on AI visibility risks forgetting the primary audience: the frustrated 17-year-old just trying to find a tuition calculator on a complex and disorganized site. Here’s the reality: If a human can’t navigate your site, an AI won’t advocate for it. The best way to rank in an AI overview isn’t to hack the algorithm—it’s to finally make your website usable.
Key Takeaways
- Strong information architecture improves both student experience and AI search visibility.
- Clear content ownership helps AI systems identify authoritative institutional information.
- Logical website hierarchy provides important context for both users and AI crawlers.
- Student-centered labels outperform internal jargon and creative naming conventions.
- Card sorting and tree testing help institutions validate navigation decisions using real user behavior..
What Is Information Architecture (IA)?
Information architecture (IA) is the way website content is organized, labeled, and connected so users and search systems can find information efficiently. Effective IA helps prospective students complete important tasks while also supporting AI search optimization and Answer Engine Optimization (AEO).
Two Audiences, One Information Architecture Strategy
Strong information architecture improves both AI visibility and student usability: clearly structured websites and pages; concise, fact-based answers to questions that aren’t buried by lots of fluff; statistics, experts, and authoritative sources that make it easy to know the content you’re viewing is up to date and trustworthy.
In fact, the things that make a higher education website valuable to AI crawlers are the same kinds of things that make them easy for humans to navigate and signal credibility to human readers:
Conventional Labels and Predictable Information Architecture
Human visitors arrive with a core set of top tasks (e.g., “How much does this cost?”). Conventional labels allow them to find information quickly, leveraging their experiences on other sites.
Similarly, AI models are trained on the existing corpus of websites. When you use human testing to ensure a student can find a page quickly, you are confirming your site matches the standard patterns AI uses to verify facts.
Trust Signals Through Established Patterns
Adhering to standards signals to students that an institution is established and credible. For a bot, clarity is just as vital.
When your IA is confusing to a human, it is ambiguous to a bot. Ambiguity can lead to inaccurate representations of your institutions or the AI simply skipping your site in favor of a competitor with a clearer, more authoritative structure.
Easy Paths Toward Task Completion
Confusing navigation leads to a poor user experience, which may cause site visitors to bounce. Search engines and AI-driven tools monitor these signals; they reward sites with high engagement rates.
If your site is optimized so students can find what they need, those engagement signals tell search engines that your site is a high-quality resource for searchers.
Two Structural Bottlenecks (And How to Fix Them)
While the principles of clear design are straightforward, implementing them within the complex ecosystem of a college or university often requires a deliberate structural approach.
We recommend focusing on two key areas that can be challenging for the large, decentralized sites that are common in higher education: clear content ownership and structured hierarchy.
1. Clear Content Ownership for AEO and AI Search
Ensuring that large language models (LLMs) can easily retrieve and understand key facts about your institution requires establishing and enforcing authoritative content sources for key facts.
Tuition rates should be clearly stated and posted in one place on the site rather than duplicated by individual program owners. The course catalog is the authoritative home for degree requirements and course listings; academic departments should link there rather than duplicating information.
AI search has increased the risk of exposure when you have duplicate and conflicting content. If an LLM sees two different tuition rates on your website, it won’t know which one is authoritative. It may cite the wrong data, or worse, skip your site altogether.
2. Structured Hierarchy
Short, simple URLs can be enticing, but they often strip away vital context for both human visitors and bots crawling your website.
Adding hierarchy to your website structure through a nested folder structure (e.g., /academics/undergraduate/degrees/nursing instead of just /nursing) provides a logical roadmap to understand how different components of your institution relate to one another.
Beware falling into the trap of thinking this means your website structure should mirror your org chart, however. Parent categories should be meaningful to site visitors, not simply a reflection of your internal organization.
How Internal Website Labels Hurt Student Navigation and AI Search
In our testing, one thing is clear: internal jargon and non-standard organization lose students. We consistently see two types of labeling failures that hurt site performance:
1. Internal Jargon
One institution we tested featured “J-Term” as a link in its navigation. When we asked prospective students what they thought it meant, most stumbled. They recommended using more common terminology, like “Winter Term.”
“I honestly have no idea what this could mean. Maybe like a junior term?”
“Since I am a first-year, I know what it is now, but that label would not have made sense to me a year ago.”
The lesson: labels that make sense internally may not make sense to prospective students—or to AI systems attempting to understand institutional content.
2. Being Clever
Confusing labels aren’t always born from academic jargon; sometimes they come from trying to be too approachable. When one website introduced its cost and financial aid content as “Paying for College,” students struggled to find it for the opposite reason: the label sounded approachable, but it didn’t match what they were looking for.
“The labeling was confusing. When I clicked on ‘Paying for College,’ I wasn’t necessarily thinking about the cost. I was expecting to just see FAFSA, scholarships, or grants.”
“When you asked me to find the cost of attendance, I was looking for ‘Tuition.’ Typically, Tuition is on a separate page from Financial Aid.”
The lesson: familiar, task-oriented labels generally outperform creative navigation labels because they align with user expectations and search intent.
Is Your Navigation Working for Students and AI Search? Consult the Experts
Good news! You don’t have to guess which labels are failing. You can use data to build a navigation strategy that balances institutional authority with clarity and familiarity.
Step one is an IA assessment. This process looks at your current site structure to identify the categories and labels that create friction. The goal isn’t to completely reinvent a website’s organization, but to ensure that your primary navigation items are actually serving their purpose.
To bridge the gap between higher education terminology and student understanding, consult your target audiences: prospective and current students. We recommend two specific types of user testing:
1. Card Sorting
In this exercise, users organize their content into categories that make sense to them. For example, administrators may categorize “Tuition” under “Administrative Services” or “Student Accounts,” while prospective students typically associate it with “Admissions & Aid.”
Card sorting helps identify the categories that students expect to see.
2. Tree Testing
Once you have a proposed content structure, tree testing assesses your labels. By eliminating visual design elements and asking users to find specific items within a text-only hierarchy, you can pinpoint where they encounter difficulty. If participants are unable to find resources in a tree test, a well-designed menu won’t resolve the issue.
Both card sorting and tree testing provide valuable evidence for navigation decisions, helping institutions improve user experience and strengthen AI search performance.
Build Higher Ed Websites for Students First, and AI Visibility Will Follow
By ensuring that your website is useful and usable for human visitors, you’ll strengthen both the user experience and the signals that support AEO for higher education and long-term AI search visibility. You don’t need two separate strategies for human UX and AI optimization.
The institutions that succeed in AI search won’t be the ones chasing algorithms. They’ll be the ones building websites that are clear, authoritative, and easy for students to navigate.
Invest in understanding how your students navigate your content, and AI performance follows.
Wondering whether your website structure supports student success and AI visibility? Start a conversation about opportunities to improve your website’s structure, content clarity, and performance in today’s AI-driven search environment.
This blog was written by Kirstin Swagman, Vice President, Web Strategy & User Experience, and Emerald Obie, Web Strategist.
What Higher Ed Teams Should Know About IA and AI Search
What is information architecture (IA) and why does it matter for AI search?
Information architecture is the way website content is organized, labeled, and connected so users and search systems can find information efficiently. Strong IA improves both student usability and AI search visibility at the same time.
Does improving IA for students also improve AI search performance?
Yes, the qualities that make a higher education website easy for students to navigate are the same qualities that help AI crawlers understand and cite your content. You do not need two separate strategies for human UX and AI optimization.
How does duplicate or conflicting content affect AI search visibility?
If an AI model encounters two different tuition rates on your website, it cannot determine which is authoritative and may cite the wrong data or skip your site entirely. Establishing a single, authoritative source for key facts reduces this risk.
Why does website hierarchy matter for AI crawlers?
A nested folder structure, such as /academics/undergraduate/degrees/nursing, gives both human visitors and AI crawlers a logical roadmap for understanding how different parts of your institution relate to one another. Short, simple URLs often strip away that context.
What labeling mistakes most commonly hurt higher education websites?
Internal jargon and overly creative navigation labels both cause students to struggle when searching for content. Familiar, task-oriented labels aligned with student expectations consistently outperform labels that reflect internal terminology or institutional branding.
How can institutions validate their navigation decisions before making changes?
Card sorting and tree testing are two user research methods that provide evidence-based guidance for navigation decisions. Card sorting reveals how students naturally categorize content, while tree testing identifies where users get lost within a proposed structure.
