Could search engine optimization (SEO) be going the way of the dinosaurs?
Not yet, but it is being forced to share the spotlight with its newer, flashier counterpart, generative engine optimization (GEO).
While much of the conversation about generative AI in higher education is centered around whether universities should embrace this technology (the jury’s still out), it’s already having a major impact in the world of digital marketing.
Looking at our own clients’ data, we found that traffic from ChatGPT and AI Overviews converts at a 157% higher rate than traditional search traffic. Even if higher ed institutions aren’t ready to go all-in on genAI, this is compelling, first-hand evidence to support the idea that they should at least make GEO a part of their digital marketing strategy.
Let’s talk a little more about what GEO is, how it differs from SEO, and how your institution stands to benefit from it.
What Is Search Engine Optimization?
If you have any digital marketing experience, you likely possess at least a baseline understanding of SEO. At a high level, it’s the process of optimizing your website to improve its visibility on search engines with the goal of driving qualified traffic to your site.
Optimizing how?
Well, there’s really no one way to do SEO. Instead, it consists of multiple elements, including:
- On-page SEO: The optimization of content and HTML source within a website; this includes keyword optimization, content quality improvements, internal linking, and easy-to-understand URL structure
- Off-page SEO: Any actions taken outside your site to improve its authority and credibility in the eyes of search engines, such as securing backlinks from reputable sites
- Technical SEO: This focuses on the backend of your site to ensure it’s properly structured for search engines to crawl and index; this includes improving site speeds and mobile-friendliness, creating an XML sitemap, and using schema markup
How Does SEO Work?
SEO works by aligning your university’s site with the ranking factors that search engines such as Google use. These search engines send out bots (known as crawlers) to scour the web and discover pages, which they then index. That means these crawlers store and organize information to retrieve later when search engine users make relevant search queries.
Once indexed, search engines rank pages based on factors such as how well the content matches the query, how authoritative the site is, the quality of the content, and the user’s overall experience. Google’s Quality Rater Guidelines — commonly known as E-E-A-T — are considered the gold standard.
The higher a search engine ranks your content, the more likely it will appear near the top of the search engine results page (SERP) when prospective students enter queries that align with the search terms you’re targeting.
What is generative engine optimization?
GEO is the new kid on the block. It’s conceptually similar to SEO, except that it focuses on optimizing your website for generative AI search engines, such as ChatGPT, Perplexity, Gemini, Copilot, and Google’s AI Overview.
In this case, when we say “optimizing,” we mean making sure that any content on your university’s website is easily understood, relevant, and contextually appropriate for large language models (LLMs). LLMs are AI systems that process and analyze astronomical quantities of data to understand and generate text. Generative AI search engines, in particular, are LLMs designed to answer common search queries.
Like SEO, GEO consists of quite a few core elements, including:
- Content relevance and context: More than just keyword optimized, content needs to be relevant to what the user is asking or searching for and provide contextual answers that genAI search engines can understand.
- NLP optimization: Content should be well-structured, flow naturally, and answer user queries clearly and comprehensively so that the natural language processing (NLP) models genAI search engines are built on can interpret it correctly.
- Topic authority and expertise: Content must demonstrate deep knowledge of the topic at hand to prove that it’s a reliable source of information.
- User intent alignment: Content should be tailored to meet the user’s specific need; for example, if a user is searching “best online MBA program for working professionals,” a genAI engine will serve them program recommendations, not just general information about online MBAs.
- Interactive and conversational content: Responses to queries should sound conversational, thereby increasing their chances of getting picked up and featured by voice search and AI-powered chatbots.
- Optimization for AI feedback loops: GenAI systems learn from every user interaction, so content should be updated based on how AI engines interpret and generate responses over time.
How Does GEO Work?
GEO optimizes content so that it can be easily interpreted and understood by genAI search engines. That way, when prospective students interact with these engines, they get relevant, high-quality, and contextually appropriate responses based on existing content.
When a user asks a question, genAI search engines rely on machine learning models to analyze content from websites, identify the most relevant answers, and present responses crafted from those answers. The models “understand” the content through NLP, meaning they interpret how words and phrases connect, their meaning, and their context.
GenAI engines assess content based on several factors, including relevance, clarity, user intent, and authoritativeness. The more aligned your content is with these factors, the more likely that an AI engine will use it to generate responses or rank it highly in relevant searches.
GEO vs. SEO: a side-by-side comparison
| Aspect | Search Engine Optimization | Generative Engine Optimization |
|---|---|---|
| Goal | To improve a website’s visibility and ranking on traditional SERPs | To ensure content is easily interpreted and recommended by generative AI engines |
| Target Platforms | Google, Bing, Yahoo | ChatGPT, Perplexity, Gemini, Copilot |
| Ranking Method | Based on algorithms that evaluate keywords, backlinks, domain authority, and UX signals | Based on AI models that interpret content relevance, context, authority, and relevance to generate accurate responses |
| Authority Signals | Backlinks from reputable sites, domain authority, social signals, and engagement metrics | Demonstrated topic expertise, trustworthy sources, and consistent factual accuracy |
| Content Structure | Keyword-driven and optimized for web crawlers; focuses on headings, meta tags, and HTML structure | Context-driven and optimized for NLP; favors bullet points, numbered lists, and data structured in clear hierarchies that allow for easy extraction |
| Output Format | Static search listings (titles, meta descriptions, and URLs on SERPs) | Dynamic, AI-generated text responses, summaries, or conversational outputs integrated into chat or voice interfaces |
| Query Length | Typically short (3–4 words) and keyword-based (e.g. “online MBA programs”) | Conversational, longer (20–25 words), and question-based (e.g. “What’s the best online MBA program for working professionals?”) |
| Success Metrics | Organic traffic, click-through rates, keyword rankings, backlink growth | AI-generated visibility, inclusion in AI summaries or responses, accuracy of presentation in AI outputs, engagement from conversational interfaces |
| Desired Behavior | Users click through to a website from search results | Users engage with or receive accurate information about an institution, program, or topic directly from a genAI response |
| Traffic Source | SERPs | AI-generated responses, summaries, chatbots, and voice assistants |
| Update Frequency | Search engines are updated periodically (often quarterly) through algorithm changes | Continuously evolving as AI models learn from user interactions and retraining cycles |
6 things GEO & SEO have in common
While the list of differences between SEO vs. GEO is fairly long, they do share several key qualities:
- They both place user intent front-and-center. Both SEO and GEO start with understanding what prospective students are really asking — they just do it a little differently. SEO uses keyword research and search analytics to identify the intent behind phrases such as “best nursing programs in Colorado,” while GEO extends this by interpreting more conversational questions such as “Which nursing programs in Colorado have the highest NCLEX pass rates?”
In both cases, success depends on anticipating why someone is searching and structuring content to meet that need.
- They both require quality content. Neither search nor generative engines reward thin, generic content. Instead, they prioritize well-written, accurate, and contextually relevant material that satisfies prospective students’ needs.
In practice, that means:
- Creating comprehensive, original content
- Demonstrating subject matter expertise
- Using clear formatting and logical organization
The difference here lies in how quality is interpreted: SEO measures relevance through keywords and engagement metrics, and GEO interprets it through content and clarity within natural language.
- They both prioritize authority and trust. Just as SEO emphasizes E-E-A-T (experience, expertise, authoritativeness, and trust), GEO relies on similar trust signals to determine whether a source is credible enough to use in AI-generated responses. That means citing reputable sources, featuring expert contributors, and maintaining transparent, factual accuracy are all essential to being recognized as a trustworthy source by both search algorithms and AI models.
- They both reward structured, machine readable content. Both search crawlers and AI language models rely on schema markup, clear headings, meta information, and accessible page structure to parse and categorize information correctly. In this case, SEO uses this structured data to help with indexing, while GEO leverages it to improve how models interpret relationships and context within content.
- They both believe there’s room for improvement. Neither SEO nor GEO is static. Both require monitoring, analysis, and adaptation as algorithms and AI models evolve. University marketers track rankings, engagement, and user behavior to refine SEO strategies; now, they must also monitor how AI engines surface and summarize their content to refine GEO efforts. While the process may be different, the principles are the same: visibility and relevance requires continuous improvement.
- They share an end goal. Ultimately, both disciplines aim to ensure the right audience discovers the right information at the right time. Whether it’s by improving placement in search results or improving inclusion and accuracy in AI-generated outputs, the goal of SEO and GEO is to build trust, deliver value, and motivate more prospective students to take action.
5 ways GEO is impacting SEO
In a 2000 interview, Google co-founder Larry Page said:
Artificial intelligence would be the ultimate version of Google. The ultimate search engine would understand everything on the web. It would understand exactly what you wanted, and it would give you the right thing.”
More than just an eerily accurate prediction of the future, Page’s observation offers a clear explanation as to why 71.5% of people report using AI tools for search. Rather than force prospective students to comb through results on SERPs in the hopes of finding a source that addresses their query to their satisfaction, AI engines deliver exact responses in just seconds, no guesswork needed.
While generative AI engines haven’t completely overtaken traditional search engines just yet — 79.8% of people still prefer Google and Bing for general information — they’ve had an indelible impact.
Here are just a few examples of how AI engines are redefining search:
- They’re changing user behavior. AI engines are conversational by nature, so prospective students are able to ask longer, more nuanced questions than they can with traditional search engines. For example, think “What’s the most flexible online MBA program for working professionals who don’t have a business background?” as opposed to “best online MBA programs.”
This shift toward long-tail, natural keyword queries means university marketers’ content strategies must evolve. Where SEO rewards broad, high-volume keywords, GEO rewards specificity. That specificity not only changes the kinds of queries institutions compete for, but also the type of content that performs well. Content that delivers direct, context-rich answers to highly targeted user questions is most likely to succeed.
- They’re increasing demand for bottom-of-funnel content. Because generative AI can handle complex, conversational searches, prospective students are increasingly looking for precise information that helps them make decisions. That means details about curriculum flexibility, faculty experience, time commitment, and return on investment, all of which can help them self-qualify before ever submitting an inquiry form.
- They’re improving intent and lead quality. Speaking of self-qualification, longer, conversational queries often come from prospective students who are further along in their decision journey. Someone who’s searching “part-time computer science degree” might be exploring options. However, someone asking “What is the best part-time computer science degree program for working professionals looking to change careers?” is evaluating programs — and is closer to applying.
This is one of GEO’s biggest opportunities. It surfaces more qualified leads because generative engines understand and serve content that matches a prospective student’s stage of intent. That means visitors arriving to your site through generative search are often better informed or more likely to convert than traditional SEO traffic, provided your content meets their deeper institutional needs. - They’re redefining visibility and authority. With traditional SEO, visibility is all about ranking on page on. In GEO, it means being cited or referenced by generative engines within AI-generated summaries or responses. This requires universities to place greater emphasis on credibility and authority so that AI systems can confidently use their content.
For example, an engineering program might publish a faculty-led analysis or renewable energy trends, or a law school might cite employment data from the American Bar Association. These kinds of trusted references increase the likelihood that AI engines will include (or attribute) your institution’s content when answering prospective students’ questions.
- They’re continuously adapting and improving. Unlike traditional search engines, which make periodic algorithmic updates, generative engines continuously learn. Their understanding of content shifts with each new piece of data they ingest and each new user interaction they have.
That means higher ed institutions can’t afford to publish and optimize once. Instead, they need to:
- Monitor how their programs are being represented in AI search responses
- Adjust their copy and structure so AI systems don’t misinterpret information (such as tuition, duration, or modality)
- Refresh examples, data, and language frequently to stay aligned with what users expect
Why both GEO & SEO matter
To (mis)quote Mark Twain, “Reports of my death have been greatly exaggerated.”
The same could be said for SEO. Despite the hand-wringing over the “death of SEO,” with even Google itself going all-in on generative search, SEO remains just as important a tool in higher ed digital marketing as GEO.
There are a few reasons for this:
- Students discover universities in multiple ways. Together, SEO and GEO account for all of them, driving both clicks and awareness. Without SEO, your institution might not appear in a SERP’s top results; without GEO, it might be left out of AI-driven conversations entirely.
- SEO creates a solid foundation that GEO can build on. Strong SEO gives your content structure, authority, and discoverability, the very elements that feed generative AI systems. As we’ve said, search engines and AI models both rely on the same pool of high-quality, authoritative web content.
If your institution’s site isn’t optimized for SEO, with fast load times, clear metadata, organized content, and accurate schema, it’s unlikely to be properly indexed and understood by generative engines. In that sense, SEO is the foundation GEO builds upon by ensuring that same content is also interpretable by AI and usable in conversational responses.
- SEO and GEO serve different stages of the student journey. Traditional SEO tends to capture students toward the top of the funnel, when they’re browsing broadly for programs, requirements, or rankings. GEO, on the other hand, often supports students in the decision or comparison phase, when they’re asking AI engines for more personalized recommendations or next steps. Both matter because they complement one another, with SEO fueling awareness and GEO nurturing confidence and conversion.
- Search isn’t dying, it’s diversifying. What’s changing isn’t search itself, but the format in which people access information. Where SEO once meant optimizing for SERPs, it now means optimizing for a multi-channel ecosystem. That doesn’t make SEO irrelevant; if anything, it makes it more essential than ever. SEO gives your content the structure and clarity it needs to thrive across every discovery platform, both old and new.
SEO isn’t dead; it isn’t even dying. And SEO and GEO are complements, not competitors. For university marketers, success requires building a strategy that connects the two in a new, AI-driven search landscape.
Creating a strong SEO foundation while navigating the brave new world of GEO can be daunting, but it doesn’t have to be. At Vital Design, we combine the best of both to rewrite the higher ed digital marketing playbook, so your institution shows up where it counts. Let’s talk.
