# Coursera and Udemy Merge: AI Is Reshaping Online Education

On May 11, 2026, online education platform Coursera announced that it had completed its merger with Udemy. The combined company will continue to use the Coursera name and trade on the New York Stock Exchange under the ticker COUR. Udemy shares will be delisted from Nasdaq.

According to the official announcement, the combined platform reaches more than 290 million learners, 18,000 enterprise customers, 95,000 content creators, over 315,000 courses, and hundreds of university and industry partners.

Andrew Ng wrote on X that Coursera and Udemy came together to keep serving learners around the world. He said both companies believe access to high-quality education can change lives, and that helping people build job-relevant skills will become even more important as AI changes how work is done.

![](https://leafw-blog-pic.oss-cn-hangzhou.aliyuncs.com/posts/coursera-udemy-ai-online-education/andrew-ng-tweet.png)

Andrew Ng will continue as board chair of the combined company, and Greg Hart will continue as CEO.

If you only look at the headline, this seems like a normal merger between two online education companies. But given the rapid development of AI in recent years, and the fact that Andrew Ng himself is a major figure in AI, it raises a deeper question.

When many people run into something they do not understand, their first reaction has already shifted from searching for a course to asking ChatGPT or Claude directly, or throwing code, papers, and error messages into an LLM. Once that habit forms, the original selling points of online education platforms become less stable.

Coursera and Udemy happen to represent two paths online education has taken over the past decade. One is closer to putting universities and institutional education online. The other is closer to a free market for skills courses. Their merger points to a larger question: when AI can explain knowledge at any time, what is left for online education?

## Coursera: Moving Elite Classrooms Online

Coursera was founded in 2012 by Stanford professors Andrew Ng and Daphne Koller.

It caught the peak of the MOOC wave. MOOC stands for Massive Open Online Course. At that time, the imagination around online education was straightforward: if courses from top universities could be placed online, could people around the world learn content that had once been hard to access?

That was Coursera's original idea.

It worked with universities, educational institutions, and later technology companies to put courses, specializations, professional certificates, and even degrees online. You did not need to actually enter Stanford, Michigan, or Penn to access courses produced by those institutions.

This is also the biggest difference between Coursera and ordinary video-course platforms.

Coursera sells a full course structure: chapters, assignments, quizzes, certificates, and endorsement from universities or institutions. This path is closer to school education, except that school education has been broken into products that can be bought and studied online.

My first systematic study of machine learning was through Coursera. Later, I also completed the Deep Learning Specialization and received the certificate. That experience had a big impact on me because it connected the important knowledge in deep learning in a reasonable order. It avoided the fragmented state of watching one video here and reading one blog there, and it also helped avoid the places where I would easily get stuck when reading books by myself.

![](https://leafw-blog-pic.oss-cn-hangzhou.aliyuncs.com/posts/coursera-udemy-ai-online-education/coursera-deeplearning-specialization.png)

The value of these courses is that they help learners organize knowledge.

There are machine learning and deep learning resources everywhere online. The hard part is knowing what to learn first, what to learn later, which concepts are central, which details can be skipped for now, and which exercises prove that you understand enough to start building.

So Coursera's path can be understood as the platformization of educational institutions.

It turns the course-production ability of universities and authoritative institutions into a globally distributed online product.

## Udemy: Turning Skill Supply Into a Market

Udemy is older than Coursera. It was founded in 2010 by a team that included Eren Bali, Oktay Caglar, and Gagan Biyani.

Udemy has a different character from Coursera.

Coursera feels like universities moving toward the internet. Udemy feels like a skills marketplace that grew out of the internet itself. Its focus is allowing many individual instructors to create and sell courses on the platform.

That leads to a very different content shape.

On Udemy, you can find courses on very specific topics: Python basics, Excel tricks, machine learning fundamentals, project management, photography, design, marketing, and language learning. Many courses serve concrete needs: learn a tool, fill a skill gap, or quickly enter a new area.

I have also used Udemy to learn some machine learning basics. It felt different from Coursera.

![](https://leafw-blog-pic.oss-cn-hangzhou.aliyuncs.com/posts/coursera-udemy-ai-online-education/udemy-machine-learning.png)

Coursera feels like joining a designed course program with stages, assignments, and certificates. Udemy feels like entering a huge course market, picking an instructor based on need, and quickly filling a knowledge gap.

The advantages are obvious: lots of content, fast updates, and strong practicality. When a skill becomes hot, instructors can quickly create courses for it. New tools, frameworks, software, and job requirements often appear on Udemy quickly.

The problem is also obvious: quality is unstable.

It depends more on individual instructors and market filtering. You may buy a very practical course, or you may buy one that merely reads the documentation aloud. Anyone who has used Udemy should recognize that experience.

So Udemy represents another path: the marketization of individual skill supply.

It puts people around the world who want to teach skills and people who want to learn skills into the same marketplace.

## Why the Two Paths Are Coming Together

Put Coursera and Udemy together, and they are two typical samples of online education over the past decade.

Coursera emphasizes structure, credibility, institutional endorsement, and certification.

Udemy emphasizes breadth, flexibility, practical skills, and an instructor ecosystem.

One is more like a school. The other is more like a market.

In the early stage of online education, both paths solved the same problem: quality education resources were not equally available.

In the past, hearing a world-class university professor teach machine learning had a high barrier. Coursera made it easier. In the past, it was also hard for an ordinary developer, designer, or data analyst to turn their experience into a course and sell it to global learners. Udemy made that easier.

So the first half of online education was roughly about solving the problem of putting content online.

University courses went online. Career courses went online. Individual instructors went online. Platforms handled matching, distribution, payment, and certificates.

But now the ways to access courses have become extremely diverse.

Bilibili, YouTube, blogs, open-source documentation, paid communities, podcasts, ebooks, public courses, bootcamps, plus ChatGPT and Claude. Learners face too much content. The harder questions are: whom should I trust, what should I learn, and how far do I need to go before I can say I know it?

This is where AI becomes troublesome for online education.

## After ChatGPT, Explaining Knowledge Became Cheap

Before ChatGPT, when people encountered something they did not understand, the first reaction was often to search, read a blog, or find a video course.

Now it is different.

If you do not understand backpropagation, you can ask ChatGPT directly. If Transformer is hard to understand, you can ask Claude to explain it another way. If your code errors, you can paste the error into a model. If a paper is hard to read, you can let an LLM first break down the structure, explain formulas, and summarize contributions.

This does affect online education platforms.

In the past, much of a course's value came from someone explaining knowledge clearly. Now knowledge explanation is quickly becoming cheap. If the question is reasonably phrased, an LLM can often provide a smooth and understandable explanation.

This does not mean LLMs have replaced education, but they have already weakened one of online education's most basic values: one-way explanation.

If a course only explains concepts, reads documentation, or reorganizes information already available online, it will become less attractive. Learners can simply ask AI to explain the same material again according to their own background, pace, and questions.

AI is also more flexible than recorded video.

A video course will not stop and ask where you got lost. It will not change its explanation according to your code error. An LLM can. It can change examples, analogies, and language style, and it can answer follow-up questions at any time.

So online education platforms can no longer assume that having course content is enough to bring users in.

That logic has loosened.

## Education Still Needs Paths, Practice, and Feedback

We should not go too far in the other direction either.

AI is like a super encyclopedia, but it is definitely not a carefully arranged and reviewed textbook.

This is very obvious when I use LLMs to learn. They can explain a concept so smoothly that I think I understand it, but once I actually practice, I realize the knowledge is still too scattered.

LLMs are good at answering questions, but education is much more complex. Especially for beginners, the reason they get stuck is often not that there is no answer, but that they do not know what to ask.

If you want to learn machine learning, should you learn linear algebra first or Python first? How much probability theory is enough? How deeply should you understand gradient descent? Should you read derivations first or build projects first? When should you learn PyTorch? When should you start reading papers?

These questions are hard to solve naturally with a single prompt.

Learning needs a path. AI can certainly help arrange a path, but turning that path and the required knowledge into standardized teaching is almost impossible through conversation alone.

Learning also needs practice. Understanding an explanation of backpropagation is different from writing a simple neural network yourself. Understanding a RAG architecture diagram is different from building a local knowledge base that can answer questions reliably.

Learning also needs feedback. Where did you go wrong? Was it a concept problem, a coding problem, a data processing issue, or an evaluation issue? LLMs can give feedback, but that feedback is best embedded in concrete tasks, quizzes, projects, and evaluation standards. It cannot remain only in a chat window.

Finally, learning needs verification.

There is a big gap between \"I feel like I understood\" and \"I can actually do it.\" This problem becomes worse in the AI era because LLMs are so good at explaining. Sometimes you feel as if you understand, but when you start doing the work, you have nowhere to begin.

So online education platforms cannot just pile up more videos in the future.

They need to do harder things: help set goals, plan paths, provide content, arrange practice, give feedback, verify ability, and ideally connect learning to real work scenarios.

## The Merger Question: How to Integrate Two Platforms

Back to the Coursera and Udemy merger.

One detail in the official statement is worth noting: the merger will not immediately change how learners and instructors use the platforms.

For learners, existing courses, subscriptions, pricing, and certificates will not change in the short term. For content partners and instructors, existing agreements, revenue sharing, and support structures do not immediately change either, and courses will remain on Coursera.org or Udemy.com. For enterprise customers, course supply, platform access, pricing, and existing contracts will not immediately change.

This shows that the companies themselves know the user habits and supply mechanisms behind the two platforms are different. Integration after the merger will be a long process.

If this merger only puts two course libraries together, it does not mean much.

In an age of content surplus, more courses do not necessarily solve the problem. Whether a platform has 10,000 courses or 100,000 courses may not make things easier for ordinary learners. It may make choosing harder.

The real question is whether Coursera's structure and Udemy's flexible supply can be combined.

Coursera has institutional partnerships, a certificate system, and relatively strong credibility. Udemy has a rich instructor ecosystem and can follow market changes more quickly. If they only merge catalogs, it is not interesting. If they can add AI and connect courses, learning paths, project practice, and ability assessment, then there is something to imagine.

Future platforms need to answer more concrete questions:

- What level are you at now?
- What skills does your target job require?
- Which courses and projects fit you?
- What have you not mastered yet?
- What practice should you do to fill the gap?
- Can your ability be recognized by companies or the market?

This already goes beyond the scope of traditional online course platforms.

Traditional platforms are centered on course catalogs. If future platforms want to remain valuable, the center may need to become skill graphs, learning paths, AI tutors, project training, ability assessment, and certificate proof.

Coursera's official announcement repeatedly mentions skills. That is the point behind it.

Of course, corporate announcements always sound polished. The direction itself is not surprising. In the AI era, knowledge points are not scarce. Verifiable ability is.

## What Will Online Education Sell Next?

Online education over the past decade can roughly be divided into two stages.

The first stage was moving educational content online.

Coursera moved university courses and professional certificates online. Udemy moved individual instructors and practical skills courses online. This stage solved content supply and distribution.

The second stage is turning online content into continuous ability training.

After AI, the barrier to explaining knowledge has been lowered. Learners no longer only care whether a course exists. They care whether it can help them solve real problems, change roles, improve efficiency, complete projects, and prove that they truly possess a skill.

This is not friendly to online education platforms.

Building a course platform is relatively simple: produce content, distribute content, sell subscriptions, and issue certificates. Building a training system is much harder. It needs to understand learners, understand the relationship between jobs and skills, and track learning outcomes.

AI will accelerate this change.

It can provide personalized Q&A, recommend content based on weak points, generate exercises, simulate interviews, help grade assignments, and connect internal company job requirements with learning paths.

So education platforms naturally need to introduce different teaching methods.

If a platform is only a video player plus a payment system, it can easily be weakened by more flexible AI learning methods. Only platforms that provide structure, practice, feedback, certification, and connection to real scenarios have a chance to remain useful.

## Closing Thoughts

The Coursera and Udemy merger, to me, may show one thing: having courses to learn is no longer enough.

When MOOCs first rose, the exciting part was that ordinary people could finally access world-class courses. Back then, the problem was that educational resources were not open enough.

Now the problem has changed. There is no shortage of content, no shortage of explanation, and arguably no shortage of teachers, because an LLM can sit beside you and answer questions at any time. What is truly scarce is a clear path, enough practice, effective feedback, and a mechanism to prove that you have really mastered a skill.

So I do not think AI will simply destroy online education.

It will first eliminate platforms that only sell recorded courses. The remaining platforms either need to build paths, practice, feedback, and certification, or slowly become content warehouses.

Coursera and Udemy coming together happens right at this turning point.

The old keyword for online education was \"put courses online.\"

The next keyword will probably be \"how to prove you really learned it.\"

## References

- [Coursera Completes Combination with Udemy to Build the World’s Most Comprehensive Skills Platform](https://investor.coursera.com/news/news-details/2026/Coursera-Completes-Combination-with-Udemy-to-Build-the-Worlds-Most-Comprehensive-Skills-Platform/default.aspx)
- [Coursera and Udemy are now one company, creating the world’s most comprehensive skills platform](https://blog.coursera.org/coursera-and-udemy-are-now-one-company-creating-the-worlds-most-comprehensive-skills-platform/)
- [About Udemy](https://about.udemy.com/company/)
- [Udemy Founder Eren Bali Returns as Chief Technology Officer](https://investors.udemy.com/news-releases/news-release-details/udemy-founder-eren-bali-returns-chief-technology-officer)
- https://x.com/AndrewYNg/status/2053857910451827061