Most institutional websites lose visibility for reasons that are boring, specific, and fixable — a missing citation tag, an unvalidated keyword guess, a page written for humans but structured in a way no AI answer engine can quote. This checklist is organized the way we audit our own site. In the trailing 28 days (Aug 5–Sep 1, 2026), casrai.org generated 71,465 organic Google clicks and 12,789,147 impressions at an average position of 7.8 site-wide, and our guide on this exact topic — /guides/academic-search-engine-optimization-aseo — ranks #3.3 for "academic seo" with an 11% click-through rate, well above what that position typically earns. None of that happened by accident. It came from working through items like the ones below, page by page. Run this against your own site and you'll know within an afternoon where you're losing ground.
Technical Foundations
Before content or AI-visibility work matters, search engines and AI crawlers need to be able to find, parse, and trust your pages.
- Structured data validated: Organization, Article/ScholarlyArticle, FAQPage, and BreadcrumbList schema markup is present and passes Google's Rich Results Test with zero errors on your top 20 pages.
- Highwire Press tags in place: every research output, working paper, or publication page carries
citation_title,citation_author,citation_publication_date, andcitation_pdf_urlmeta tags so Google Scholar can index it correctly. - PRISM metadata applied to journal and periodical content where the publishing platform supports it, so syndication and discovery tools parse your content correctly.
- XML sitemap is current and clean: every indexable page is listed, it's submitted in Search Console, and it returns zero 404s or redirect chains when crawled.
- Robots.txt and noindex tags audited: confirm no important page is unintentionally blocked from crawling or indexing — this happens more often than institutions expect after CMS migrations.
- Canonical tags correct: no duplicate or near-duplicate content competing against itself across mirrored department, program, or campus subdomains.
- Core Web Vitals pass on mobile: LCP, INP, and CLS all fall in Google's "Good" range for your key landing pages, not just your homepage.
- Site-wide HTTPS with no mixed-content warnings anywhere in the page inventory.
- Crawl depth is shallow: no important page sits more than three clicks from the homepage, and no page is a true orphan with zero internal links pointing to it.
- Search Console coverage report reviewed: pages show as "Indexed," not stuck in "Discovered – currently not indexed" or "Crawled – currently not indexed."
Content & Keyword Fundamentals
The single most common failure mode in institutional content programs is writing to keywords that were guessed rather than verified. Every item below assumes real query data, not intuition.
- Every target keyword is validated against real search volume — pulled from Search Console query data, Google Ads keyword data, or comparable tools — before a single page is drafted, not after.
- Search intent is mapped per target query: informational, navigational, or transactional, so the page format matches what searchers actually expect to find.
- Each planned page closes a demonstrated gap: a query with confirmed demand your institution doesn't currently rank for, not a topic that simply "feels important."
- Competitive gap analysis completed: which queries do peer institutions, journals, or societies rank for that you don't, and why.
- Content depth benchmarked against current top-ranking pages for the target query — matched on topic coverage, not padded on word count.
- Every new page is linked from at least one relevant hub or cornerstone page with descriptive, keyword-relevant anchor text, not "click here" or "learn more."
- Publication cadence is sustainable and continuous rather than a single burst followed by silence — search engines and AI crawlers both reward consistency over time.
AI/GEO Readiness
Ranking in traditional search and being cited by an AI answer engine are related but not identical problems. An AI system deciding whether to quote your page is asking a narrower question: is this a clear, attributable, extractable fact? Structure for that explicitly.
- Every factual claim is specific and verifiable — a number, a date, a named source — not a vague or hedged statement an AI system can't confidently cite.
- Pages lead with a direct, quotable answer to the core question in the first two or three sentences, before any throat-clearing or context-setting.
- Headings mirror how a person would actually phrase a question to an AI assistant, not internal department language or jargon.
- Author and institutional attribution is visible and consistent — author bios, Organization/Person schema markup, and a clear "who wrote this and why they're credible" signal on every page.
- Every statistic or data point carries a source and a date in the visible text, not just in a footnote or a linked PDF an AI crawler may never open.
- Content is structured in extractable units — defined terms, numbered steps, comparison tables — that an AI system can lift cleanly without needing to paraphrase around marketing language.
- Promotional language is kept out of factual passages so claims read as citable fact rather than something an AI system would (correctly) treat as an ad and decline to repeat.
- An AI-citation audit has actually been run: using real LLM-citation tracking data, check whether your institution is currently cited when someone asks ChatGPT, Google AI Overviews, or Perplexity a question you should be the authority on — and who's being cited instead. This is a capability we apply directly, using real citation-tracking data rather than guesswork, and it consistently surfaces gaps institutions don't know they have.
Measurement
An audit that isn't re-run is just a one-time opinion. Build measurement in from the start so you can see whether the work is actually moving numbers.
- A real baseline is captured before any changes ship: current organic clicks, impressions, and average position per target page, pulled from Search Console, not estimated.
- Rank tracking covers both exact target keywords and the broader topic/category queries around them, since AI-era visibility often shows up in adjacent queries first.
- Click-through rate is tracked per page against the expected CTR for its ranking position — a page outperforming its position (the way our own ASEO guide holds an 11% CTR at position 3.3) tells you the title and snippet are working; underperformance tells you they aren't.
- AI-citation tracking runs on an ongoing basis, not as a one-time check, since which sources get cited shifts as models update.
- Indexing status is monitored continuously, not just verified once at launch — pages can silently drop out of the index months later.
- Organic and AI-referral traffic is tied back to specific pages and, where possible, to downstream conversions — inquiries, applications, submissions — not reported as a single undifferentiated traffic number.
This is the same checklist, applied continuously rather than as a one-off exercise, that underpinned 357 new content pages we researched, wrote, fact-checked, and published in a single continuous cycle across the US, UK, Canada, and Australia — each one built against verified search demand before drafting began — and it's the same methodology behind multiple #1 Google rankings we hold on high-intent buyer and trust queries. If you'd rather see where your own institution's pages fall short against this list than run it manually, we'll do the audit for you.
