similar sites

Browse

Tools

🚀Submit Your Site to AIPush your URL to AI search engines 🤖AI Crawler CheckerSee which AI bots your site blocks 🎯AI Readiness CheckerScore your site 0–100 for AI 🔗Backlink CheckerWho links to any site — free All tools →

Sites similar to briankov.com

Briankov alternatives & similar sites

Brian Kovalesky – Professor of History — 18 websites ranked by shared content topics, category and on-page relevance.

Each result shows its full tech stack, contacts and AI-policy — not just a name · Browse all sites in Education →

Domain	Match	Title	Country/Lang	Category	AI files	Contact	AI-protection
kristinlafollette.com ↗	71 match 1 shared topics	Kristin LaFollette, Ph.D. – Professor – Scholar – Writer	en	education WordPress	robotsllmsaihumans	emailphone	none
aleksandra-bartoszko.com ↗	71 match 1 shared topics	Aleksandra Bartoszko – Social anthropologist & professor of social work	en	education WordPress	robotsllmsaihumans	emailphone	none
alisonwitte.net ↗	70 match 1 shared topics	Alison Witte – Professional Portfolio	en	education WordPress	robotsllmsaihumans	emailphone	none
alleswriting.net ↗	68 match 1 shared topics	Dictionary with History	en	education	robotsllmsaihumans	emailphone	none
outlawsandjustice.org ↗	67 match 1 shared topics	(Out)Laws & Justice \| HISTORY ON STAGE.	en	education Weebly	robotsllmsaihumans	emailphone	none
porcoapush.com ↗	67 match 1 shared topics	AP U.S. History - Mr. Porco - Home	en	education Weebly	robotsllmsaihumans	emailphone	none
viviennewestbrook.com ↗	67 match 1 shared topics	Vivienne Ruth Westbrook, Ph.D. – Professor of English and Cultural Studies	en	education WordPress	robotsllmsaihumans	emailphone	none
mdylanspencer.com ↗	66 match 1 shared topics	Dylan Spencer	United States~ en	education	robotsllmsaihumans	emailphone	none
abcp.org.uk ↗	66 match 1 shared topics	ABCP – Association of British Chinese Professors	United Kingdom en	education WordPress	robotsllmsaihumans	emailphone	none
hhshistory.com ↗	66 match 1 shared topics	History of the Hughes, Arkansas Public School	en	education	robotsllmsaihumans	emailphone	none
korolova.com ↗	65 match 1 shared topics	Aleksandra Korolova, Assistant Professor of Computer Science and Public Affairs at Princeton	United States~ en	education	robotsllmsaihumans	emailphone	none
csusmhistory.org ↗	65 match 1 shared topics	Csusm History -	en	education WordPress WooCommerce	robots llmsaihumans	emailphone	none
shumanmss.com ↗	65 match 1 shared topics	Mary Shuman – Digital History and Blog, Master of Arts	en	education WordPress	robotsllmsaihumans	emailphone	none
bighistoryproject.com ↗	65 match 1 shared topics	Big History Project: For Lifelong Learners	en	education	robotsllmsaihumans	emailphone	none
mcgillscience.org ↗	65 match 1 shared topics	Association of McGill Professors of Science – Association of McGill Professors of Science	en	education WordPress WooCommerce	robotsllmsaihumans	emailphone	none
megan-a-chan.com ↗	64 match 1 shared topics	Megan Chan – Academic Profile of Megan Chan	en	education WordPress	robotsllmsaihumans	emailphone	none
dhrice.org ↗	64 match 1 shared topics	Dan Hiaeshutter-Rice — Associate Professor, Michigan State University	United States~ en	education	robotsllmsaihumans	emailphone	none
moohyungcho.com ↗	64 match 1 shared topics	Moohyung Cho \| Assistant Professor, Ewha Womans University	en	education WordPress	robotsllmsaihumans	emailphone	none

How the match score works

Each match is a 0–100 similarity score — the higher it is, the more two sites resemble one another. It’s computed automatically from our own crawl data (never from what a site says about itself) by combining several independent signals, so a high score means several of them point the same way:

shared topics how many of the same fine‑grained content topics (IAB sub‑categories) both sites cover — the main signal.
same category whether they sit in the same business / industry category.
content & title how closely their titles and page descriptions line up in wording and meaning.

No single signal decides the result — they’re blended together. Treat the score as a way to rank candidates rather than an absolute percentage; the chips on each result show which signals contributed.