AI SEO & GEO · original data

We asked AI for alternatives to Coursera and Udemy. It named Coursera and Udemy, 7 times out of 7

Original measurement by Vincent Wesley Couey · 1 grounded AI engine, 21 independent runs, August 2026

What we did. We put the buying question for online courses to a grounded AI assistant 21 separate times, across three phrasings, and logged every product it named. 192 product mentions in total.

What came back. Coursera and edX were named in 21 of 21 runs. Not most. All of them. And in the cell where we explicitly asked for platforms other than Coursera and Udemy, both were returned anyway in 7 of 7 runs.

Why it matters. If you are a course platform outside the top few names, the assistant is not weighing you against them. It is returning a settled field and then, if pushed, returning the same field again.

The three questions we asked

All three were asked as independent calls with no shared conversation, so nothing carried over between runs. Seven runs each.

The leaderboard, blended across all 21 runs

Ranked by how many of the 192 product slots each name occupied.

Platform or credentialSlotsRuns naming it
Coursera2121 of 21
edX2121 of 21
LinkedIn Learning1414 of 21
CompTIA1212 of 21
Udemy1212 of 21
Google Career Certificates1010 of 21
HubSpot Academy99 of 21
Skillshare77 of 21
Udacity77 of 21
DataCamp77 of 21
Pluralsight77 of 21
Thinkific77 of 21
Teachable77 of 21
Khan Academy66 of 21

The finding that surprised us: a certification body ties a course platform

CompTIA is not a course platform. It is a certifying body that sells exams. It was named as often as Udemy, in a question about online courses, in 12 of 21 runs.

That is worth sitting with if you sell courses. When somebody asks an assistant about certificates, part of the answer is not a course at all. It is an industry certification you study for anywhere and then pay separately to sit. Google Career Certificates at 10 and HubSpot Academy at 9 push the same way: two of the top seven results are credentials, not platforms.

The challenger test, and why it is the most useful number here

The third phrasing asked, in plain words, for platforms other than Coursera and Udemy. A recommendation engine that weighs options afresh each time should have honoured that. It did not. Coursera and Udemy each appeared in all seven runs of that cell.

The field is stickier than the prompt. Skillshare, Udacity, DataCamp, Thinkific and LinkedIn Learning did also appear seven times each in that cell, so the challenger framing genuinely widened the field. It just did not displace the incumbents at the top of it.

What to do with this

If you are buying: the near-unanimity is a signal about consensus, not about fit. Coursera and edX being named every single time tells you they are the safe answer, not that they are your answer. The challenger cell is the more useful list if you already know what you want to learn.

If you sell a course platform: being absent from a settled field is not a ranking problem you can fix by being slightly better. The assistant returned the same names even when instructed to avoid them. Presence in the sources those answers are built from matters more than incremental product improvement.

Questions people ask

Is this the same as your 8-engine AI Recommendation Audit?

No, and the difference matters. The AI Recommendation Audit puts one question to eight engines and compares them. This study puts three phrasings to one grounded engine, 21 times, to measure stability and phrasing sensitivity within a single engine. Different question, different design, and the results should not be quoted as multi-engine consensus.

Does 21 runs prove anything?

It proves what it measured and nothing beyond it. A name appearing in 21 of 21 runs is a strong stability signal at this sample size. A name appearing in 6 of 21 is not a ranking, it is a presence. We publish the counts rather than a tidy top-five precisely so the sample size stays visible.

Why does the order change between runs?

Because these systems are not deterministic. That is the entire reason to ask 21 times instead of once. A single screenshot of a single answer tells you almost nothing about what an assistant usually says.

Method and honesty note

Original measurement, August 2026. The buying question for this category was put to a single grounded AI assistant with live web search, 21 independent times, balanced 7 runs across each of three phrasings. Every call was independent, so there was no shared conversation state and no memory carried between runs. Every answer was logged verbatim, including runs that named nothing, and product names were extracted deterministically against a fixed 70-name lexicon that deliberately includes platforms absent from our own coverage.

What this is not. It is one engine, not eight. It is a dated snapshot, not a permanent ranking, and AI answers move. It measures what an assistant said, not what any of these companies are worth. We have no commercial relationship with any platform named above at the time of writing.

Reproducing it. The phrasings are printed above in full and the capture protocol is the same one published with our DOI-registered audit. Ask the same three questions 21 times and you should land in the same neighbourhood; if you do not, we would like to know.

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