I’m really curious about what’s coming next in the world of AI and spaced repetition systems. Does anyone know who’s working on cutting-edge stuff and what they’re working on? I feel like AI is going to play a huge role in this, and I’m wondering if people are already developing systems that go beyond what we have now.
I’m imagining something like AI that observes your screen or activity, maybe using Retrieval-Augmented Generation (RAG), and saves everything as vectors in a database. The AI would not only know your knowledge base but also track your learning patterns and adapt based on that to give you the ideal material to review.
Could recommender systems also play a role here? Similar to how Netflix or Spotify predict content, could an AI system recommend the next card or concept based on what you’ve learned and where you’re struggling? I don’t know much about how recommender systems work, but it seems like they could be useful for making personalized suggestions in spaced repetition systems.
Does anyone know how a solution like this would be structured technically? What would the architecture look like? Is it just about vectorization and retrieval, or are there other advanced techniques at play here, like deep learning models for knowledge tracking, or complex recommender systems that optimize the learning flow?
I don’t know much about the technical side of this, but I’m really interested in learning more if anyone has insights or ideas!
The only person I know is Jarrett. Other than that, probably not much is going on.
I think the first goal would be to understand more of neuroscience. It’s a field barely 100 years old and we still don’t know a lot of stuff. Then maybe, yeah.
If there’s anything I want happening right now, it’s a math academy like “encompassing graphs” that connect flashcards and update a card’s scheduling when another partially related card is reviewed, and maybe we can use ML techniques to train a model to do that for us.
@Expertium Do you have any “vision” for what’s going to happen years later in SRS space?
Tracking changes in memory state outside of SRS. And we don’t need Neuralink for that. For immersion based langauge learning, we need an add-on+reader application that tracks what you are reading and update memory state based on that. So if I come across the word “deliberate”, the vocab card for the same word will get updated. Given that we have JPDB/LingQ and others who combine content with SRS, this is totally possible. It’s just that no one will do it.
For language learning, maybe the AI taking the role of a teacher. Rather than answering questions through a program like Anki, it will be an interactive conversation with the AI. It will build a picture of your vocabulary, use of grammar, pronunciation, strengths and weaknesses and lead the conversation based on that. It would give you novel, purpose-built questions and sentences tailored specifically to your needs at the current point in your learning. Giving it your Anki history could allow it to build a picture of where you are now.
Detecting Interference. It is a fact that learning things in a certain order would have different results than if they were learned in a different order.
Also newly learned information absorbed could adversely affect old learned information, which is why things memorized by heart are suddenly forgotten sometimes after taking up new information.
I would like to see this interference effect and the effect of time of the day on card retention be modelled one day, perhaps in FSRS 6. One day…
Well, the mathacademy example you gave is not actually AI. It’s more like SRS with some extra work. It’s possible in Anki but requires a lot of work, and I doubt it would be as effective for math learning as it is for language learning.
Andy presents a promising vision but I don’t think it’s possible for the next few years. The cutting edge AI model is still struggling in many things and you can’t trust it being a tutor, especially when you know little about the topic.