AI Flashcards and Spaced Repetition

Spaced repetition rests on a well-established finding: memory fades over time, a pattern Hermann Ebbinghaus described in his forgetting curve in 1885, and each successful recall slows that fading. Reviewing at increasing intervals, such as one day, then several days, then weeks, is far more efficient than cramming. Early systems were manual. The Leitner system, from the 1970s, moved paper cards between boxes reviewed at different frequencies. Piotr Wozniak's SuperMemo software introduced the SM-2 algorithm in the late 1980s, and the popular open-source app Anki long used a variant of it. In 2023 Anki added support for FSRS, the Free Spaced Repetition Scheduler, which fits a model of your personal memory from your review history. AI changes the card-creation step. Writing good cards is slow, so many learners skip spaced repetition entirely. A language model can read notes, a textbook chapter or slides and produce draft cards in seconds, including cloze deletions where a key word is hidden. The catch is quality. AI-generated cards can contain factual errors, especially on numbers, names and specialized details. They can also be badly designed: cards that bundle several facts, questions with more than one valid answer, or cards that test recognition of wording rather than understanding. A widely cited guideline, Wozniak's minimum information principle, says each card should test one small, clear idea. A practical workflow: generate cards from your own trusted material, not from the model's general knowledge; review every card against the source before adding it; split complex cards; delete trivial ones. A common misconception is that more cards means more learning. A smaller deck of accurate, focused cards reviewed consistently beats a large deck you abandon.