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Show HN: Academa – Long-form STEM lecture videos generated by LLMs

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Why This Matters

Academa introduces a novel approach to online STEM education by generating lecture videos through AI-driven code that describes teaching actions. This method allows for easy editing and updating of educational content, addressing common issues like mistakes and revisions inherent in traditional video production. Leveraging AI and large language models, this approach promises more maintainable, adaptable, and scalable STEM learning resources for both educators and students.

Key Takeaways

The Idea

Online STEM education is, in practice, a library of lecture videos. Think Khan Academy, Udemy, Coursera, MIT OpenCourseWare, YouTube, etc.

But why videos?

Picture a professor teaching a class at a blackboard. The professor is performing a form of presentation: they write an equation while explaining it, circle a term, draw a plot, then point to what matters and continue. Record that presentation, and you have a lecture video. Put it on the internet, and you have the paradigm behind today’s online STEM education.

Producing a good lecture video is really hard. But let's say you managed to do it. Two weeks later, you notice a mistake. Now you have a problem: the video is already finished, and you cannot go back and fix it. In practice, you either leave the video as it is or go back into production and make it again.

While thinking about this problem, a thought occurred to us.

We write code and ship it, but later, we find mistakes, fix them, and ship a new version.

What if we could do the same with lecture videos? What if we wrote lecture videos as source code so that they are maintainable?

Lecture videos as code

Start with what the teacher actually does. The teacher says something, writes an equation, circles a term, then keeps talking. Each of those actions can be described as code:

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