Content-Aware Spaced Repetition

Summary

Content-aware Spaced Repetition Spaced repetition systems are powerful, but they have a fundamental blind spot: they don’t understand what your flashcards are about. To your SRS, a card asking “what’s the capital of Italy?” and another asking “what country is Rome the capital of?” are treated independently, each with its own isolated review history. It has no concept that reviewing related material should reinforce your memory of the whole topic. At the heart of every SRS is a memory model which predicts how long you’ll remember each card based on your past performance. Most of today’s models ignore the content of the cards entirely. This is where content-aware memory models come in: they account for the semantic meaning of your cards, not just your review ratings. This is more than a minor tweak for scheduling accuracy. It’s a foundational change that makes it practical to build the fluid, intelligent learning tools many have envisioned: from idea-centric memory systems that test understanding from multiple angles to truly conversational spaced repetition with a voice-enabled AI agent as tutor. This post explores what content-aware memory models are, and the new kinds of learning experiences they make possible. Contents Schedulers and memory models I find it useful to distinguish between schedulers and memory models. This distinction wasn’t immediately obvious to me when I first approached the topic, but I’ve found it essential for thinking clearly about spaced repetition systems (SRS). Here, I’ll introduce both concept and I’ll make the case that separating schedulers from memory models enables independent innovation and simplifies the development of each component by isolating user experience (UX) concerns within the scheduler. In the literature, the scheduler is sometimes called the “teacher model” since it decides what to teach, while the memory model is the “student model” since it represents what the student knows. In Anki and other spaced repetition systems,...