How to create spaced repetition flashcards in Logseq

Spaced repetition in Logseq can be set up with surprisingly low friction: flashcards are just structured text blocks, and the newest Logseq features make it easy to attach audio (and even pictures) so review sessions can include real prompts like bird calls or spoken phrases. Instead of spending hours building decks in tools like Anki, the workflow here leans on Logseq’s outliner-style card format plus its ability to render and play files stored in the Logseq assets folder.

Cards in Logseq follow a simple structure. The first bullet/block becomes the question (the card front), and an indented block beneath it becomes the answer (the card back). Adding the tag “card” to the question block triggers the built-in flashcard interface. During review, Logseq presents the prompt and then uses keyboard shortcuts to reveal the answer and grade recall—typically “forgot” versus “remembered”—which updates scheduling parameters so cards reappear at the right point on the forgetting curve.

A key practical point is that the default card list can feel intimidating when it shows a large number of cards (over a hundred in the example). To make review manageable, the workflow supports building a custom “dashboard” page that groups cards by topic—such as bird calls, Portuguese verbs, and bird pictures—so practice is focused and contextual.

Audio-rich cards are handled through a Logseq assets hack. Audio and images live in the Logseq assets folder, and Logseq can render a block that points to an asset file (for example, an mp3). For bird calls, the card prompt can be a bird name while the answer includes an embedded audio element that plays the corresponding call. The transcript also notes a small limitation: the reviewer may need to press the play control/shortcut for audio to work reliably depending on how audio input is configured.

Beyond manual typing, the transcript demonstrates two “import” pipelines that turn existing materials into Logseq cards.

First, translated text from a Google Doc can be cleaned and converted into card-ready blocks using Notepad++ and Excel. Empty lines are removed, then Excel formulas add the “card” tag to the question lines and ensure the answer lines are indented correctly so Logseq interprets each pair as a front/back card. The resulting markdown can be pasted into a new Logseq page, then pulled into the spaced repetition dashboard using Logseq’s “/cards” embed.

Second, for a language learned from audio (Gossa, with clicks), the workflow avoids typing hundreds of phrases by chopping audio into snippets and generating a list of filenames. PowerShell can output the directory’s mp3 filenames, Excel can split and transform those names into two columns (English prompt and Gossa phrase), and formulas can construct the exact Logseq asset links for each mp3. Those links are assembled into a markdown file and imported into Logseq, where the cards become reviewable immediately.

Overall, the core takeaway is mechanical: Logseq’s text-based card system plus assets-based audio/picture embedding makes spaced repetition feasible for real-world study materials—without the deck-building grind that often blocks people from using spaced repetition tools.