If you can remember the web of 30 years ago(!), you can remember a time when all it took to make a website was a little knowledge of HTML and a tilde account on the university VAXcluster (e.g., /~wiley6/). While it’s still possible to make a simple website today with just HTML, making modern websites requires a dizzying array of technical skills, including HTML, CSS, JavaScript frameworks, databases and SQL, cloud devops, and others. While these websites require far more technical expertise to build, they are also far more feature-rich and functional then their ancestors of 30 years ago. (Imagine trying to code each of the millions of pages on Wikipedia.org or Amazon.com completely by hand with notepad!)
This is what large language models (LLMs) like ChatGPT are doing to OER. Next generation OER will not be open textbooks that were created faster or more efficiently because LLMs wrote first drafts in minutes. That’s current generation OER simply made more efficiently. The next generation of OER will be the embeddings (from a 5R perspective, these are revised versions of an OER) that are part of the process of feeding domain knowledge into LLMs so that they can answer questions correctly and give you accurate explanations and examples. Creating embeddings and injecting this additional context into an LLM just-in-time as part of a prompt engineering strategy requires significantly more technical skill than typing words into Pressbooks does. But it will also give us OER that are far more feature-rich and functional than their open ancestors of 25 years ago.
Here’s a video tutorial of how to integrate a specific set of domain knowledge into GPT3 so that it can dialog with a user based on that specific domain knowledge. This domain knowledge could come from chapters in an open textbook, but in the example in the video it’s coming from software documentation. Granted, this video is almost two months old, which feels more than two years old at the rate AI is changing right now. So this isn’t the exact way we’ll end up doing it, but the video will give you the idea.
Rather than fine tuning an LLM, where the entire model training process has to be repeated, embeddings allow us to find just the right little pieces of OER to provide to the LLM as additional context when we submit a prompt. This is orders of magnitude faster and less expensive than retraining the entire model, and still gives the model access to the domain specific information we want it to have during our conversation / tutoring session / etc. And by “orders of magnitude faster and less expensive” I mean this is a legitimate option for a normal person with some technical skill, unlike retraining a model which can easily cost over $1M in compute alone.
Every day feels like a year for those of us trying to keep up with what’s happening with AI right now. It would be the understatement of the century to say lots more will happen in this space – we’re literally just scratching the surface. Our collective lack of imagination is the only thing holding us back. What an incredible time to be a learner! What an incredible time to be a teacher! What an incredible time to be working and researching in edtech!