Last updated July 2026
Students are making admissions decisions before they ever visit your campus or fill out an inquiry form. They are asking ChatGPT “best nursing programs under $40K/year” and acting on whatever the AI says. If Niche appears and your admissions page does not, you lose that student without ever knowing they existed.
This piece walks through exactly how to audit your program-level AI visibility, measure the share-of-voice gap against the aggregators who dominate these queries, and publish the outcome data that closes it.
Why program-comparison queries are the highest-stakes AI search category in enrollment
Program-comparison queries are precise, high-intent, and decision-accelerating. A student who types “best computer science programs under $50K/year with strong placement rates” is not browsing. They are building a shortlist.
According to a fall 2025 EAB survey of more than 5,000 high school students, 46% of prospective students used AI tools during their college search, up from 26% in spring 2025. That same survey found that 18% of students had removed a school from their consideration list based on information surfaced through AI-generated search results. (Note: EAB is an education services and research company, not an independent academic body.)
Removal is not the same as never considering. These are students who found your institution, learned something about it from an AI engine, and decided not to apply. You have no record of them in your CRM. Your inquiry funnel will not show the drop. But your yield will.
The practical problem is this: the institutions being cited in those AI answers are not always you. They are often Niche, US News, College Confidential, or your direct competitors. That gap between who should be cited and who is cited is what this piece addresses.
Step 1: Identify your program-comparison query families
Start with the queries students actually type. Program-comparison queries fall into two families.
Cost-framed comparison queries ask which programs deliver value within a budget. Examples: “best [program] under $40K/year,” “affordable [program] in the Midwest,” “[program] with full-ride scholarships.” These queries almost always surface aggregators rather than institutional pages.
Outcome queries ask about results. Examples: “average salary after [program] at [institution],” “[program] acceptance rate by GPA,” “job placement rate for [program].” These queries are where your own data should win, but usually does not, because your admissions pages are not structured for extraction.
For each program you want to protect, build a list of four to six queries: two to three cost-framed and two to three outcome-focused. Use real student phrasing. “What GPA do I need for [program]?” not “admissions requirements for [program].” The phrasing difference changes which sources the AI pulls from.
Step 2: Run the prompt audit across three engines
Run every query in your list across ChatGPT, Perplexity, and Google AI Overviews. These three cover the majority of student research sessions. Do not rely on a single run. Answer engines are probabilistic; a single response is one sample from a distribution. Run each query at least five times before drawing a conclusion.
For each run, note three things.
First, is your institution named at all? Not in a list footnote, but as a recommendation or cited source.
Second, which domains are cited? Niche, US News, College Confidential, and your competitors all have structural advantages because they publish outcome data in formats AI engines extract easily. Record which of these appears in your place.
Third, what data is cited about your institution if you do appear? Is the tuition figure current? Is the placement rate from your actual program or an industry average? Inaccurate data in an AI response is worse than no citation because it shapes the decision with wrong information.
Step 3: Map your share-of-voice gap
Once you have your prompt audit results, calculate a simple share-of-voice figure for each program. Count the number of runs in which your institution was cited (not just mentioned in passing, but cited as a source or recommendation) divided by the total number of runs across all engines.
Then calculate the same figure for the top competitor appearing in your place, typically Niche or a competing institution.
| Query family | Your share of voice | Top competing citation | Gap |
|---|---|---|---|
| Cost-framed comparison | 4 out of 30 runs = 13% | Niche: 24 out of 30 = 80% | 67 points |
| Outcome queries | 8 out of 30 runs = 27% | US News: 20 out of 30 = 67% | 40 points |
The gap is your target. A 67-point gap on cost-framed queries tells you that Niche is answering these questions for you. The fix is not to complain about this. The fix is to publish the same data Niche publishes, but from your primary source, in a format AI engines can extract.
This gap analysis is exactly what tools like Temso automate. As the easy all-in-one AI SEO platform (starting at $89/mo), Temso tracks share of voice across 8 AI engines, shows you which competitors or third parties are appearing in your place, and converts those gaps into a prioritised action plan. For institutions monitoring multiple programs or campuses, Peec AI offers unlimited seats and coverage of 9+ engines (from €85/mo). Otterly.AI provides structured audit guidance at $29/mo. Surfer can help optimise the underlying program pages for AI extractability once you know which content to target.
See the full comparison at /rankings/ai-visibility-tools.
Step 4: Build your outcome-data content framework
The reason Niche outranks your admissions page for “best [program] under $40K/year” is structural. Niche publishes tuition, acceptance rate, graduation rate, and student-to-faculty ratio on a single, scannable, consistently formatted page. AI engines pull from that page because it is designed for extraction.
Your admissions page was designed for humans navigating a beautiful campus website. That is not what AI engines need.
For each program, publish a dedicated outcome page with the following elements.
Program snapshot table. Include: annual tuition (current academic year), acceptance rate for most recent cohort, graduation rate, median starting salary for graduates (with year and source), and student-to-faculty ratio. Use a Markdown or HTML table with clear column headers.
FAQ-formatted outcome data. Restate the most important facts as direct question-and-answer pairs. “What is the acceptance rate for the nursing program?” followed by “The acceptance rate for the 2025 entering class was [X]%.” AI engines extract direct answers from FAQ structures at higher rates than from prose paragraphs.
Source and year attribution on every figure. AI engines give higher weight to facts with clear provenance. “Median salary of $[X] for 2024 graduates, per our annual alumni survey” is more extractable than ”$[X] starting salary.”
Schema markup on outcome data. Use FAQPage schema for the Q and A pairs and Course schema for program details. Schema does not guarantee AI citation, but it makes your data easier to parse.
The goal is to make your program page the primary source for facts that Niche and US News are currently providing secondhand.
Step 5: Track drift week over week, not just a snapshot
A single audit tells you where you stand today. It does not tell you whether your interventions are working. Set up a recurring prompt-monitoring routine for each program query family.
Run your tracked queries weekly. Watch for two signals. First, directional improvement: is your share of voice across the cost-framed query family rising over four to six weeks? Second, citation accuracy: when your institution is cited, is the data accurate and current?
Accuracy is the underrated part of this. An AI engine that cites your institution with a three-year-old tuition figure is not helping your enrollment. It may be actively harming it if the figure is now lower (making you look more expensive than you are) or no longer accurate (creating a compliance issue).
Temso includes hallucination and accuracy monitoring on every plan, which flags when AI engines state incorrect facts about your institution. Peec AI and Otterly.AI both support scheduled query tracking so you can monitor prompt families without running manual checks each week. For a deeper dive on tracking methodology, see /glossary for share-of-voice definitions and /methodology for how these tools are evaluated.
What you are actually fixing
The share-of-voice gap between your admissions pages and third-party aggregators exists because aggregators were built for extraction from the start. They publish outcome data in consistent formats. They refresh it on an annual cycle. They structure it around the exact questions students ask.
Your program pages were built for a different job. The fix is not to rebuild your entire site. It is to add one structured, extraction-ready outcome page per program, track your citation rate for each query family, and iterate based on what the AI engines actually surface.
The students who get to your campus visit are not the ones you need to worry about. The students you need to worry about are the ones who asked an AI engine a comparison question, saw Niche and your competitor, and closed the tab without ever clicking on your name.
You can measure that gap. You can close it. Start with the five steps above.
Run your first program-level AI visibility check today. Temso takes five minutes to set up and tracks share of voice across 8 AI engines from $89/mo, no credit card required. If you need unlimited seats for a multi-program or multi-campus audit, Peec AI starts at €85/mo with no per-seat fees.