Can You Be More Efficient Than Anki for Language Learning?

The short answer

Yes. You can be more efficient than Anki for language learning. Anki is excellent at scheduling cards. It was not built for language learning, so a typical deck reviews one word per card. The main language-specific win is packing several due words into one sentence: one exercise instead of five cards. Then grade each word separately, and use a new sentence every review so you do not memorize the prompt. That is how you get more usable language per minute than a bigger Anki queue.¹

What Anki is built for

Anki is a general-purpose spaced repetition app. It schedules whatever cards you make, which is a great job for facts and terms. It was not built for language learning, so it cannot take advantage of language-specific optimizations.

Pack several due words into one sentence

If quiero, café, and con are due, Anki gives you three cards. A language-optimized review gives you one sentence: “I want coffee with milk.” You produce all three words in one go. Same due list, fewer exercises.

That packing is where the efficiency comes from. Writing a whole sentence to review a single word would be slower than an Anki card. Writing a sentence that carries several due words at once is faster than those cards stacked back to back, and you practice the words in context instead of in isolation.² Keep the sentence natural: pack what fits. See multi-concept sentence reviews.

Grade each word, not the whole sentence

Packing only works if each word has its own schedule. If one sentence is one card, a single miss marks the whole review wrong, and four correct words get punished. A single pass marks everything right, and the weak word disappears for too long. Grade each word and grammar point on its own. The sentence is the exercise. The scheduler still tracks items, not the whole line. See word-by-word grading.

Use a new sentence every review

If you always review the same packed sentence, you start remembering that sentence, not the words. The word gets tied to that one line. When you need it in a different sentence, it often will not come. Keep the due words. Put them in a new sentence each review. Then you are practicing the words themselves, not one example you already know by heart.³ See why sentence mining fails.

Cloze cards have a related leak. The sentence is already built, so you only fill a blank. That is quick, and it does not train producing the line. See cloze card drawbacks.

Two supporting upgrades

Better timing: FSRS

FSRS predicts forgetting more accurately than older SM-2-style heuristics, so you waste fewer reviews on items you already know.⁴⁵ Anki can run FSRS with an add-on. A language-first app can run it on every word and grammar concept by default. See FSRS vs SM-2.

A short ramp for new words

A brand-new word in a packed sentence often fails from overload, not from a bad schedule.⁶ A few recognitions in context, then a packed sentence, saves retries. Anki leaves that design to you. See what is laddering in language learning.

Anki vs. a language-optimized SRS loop

Dimension	Typical Anki language deck	Language-optimized loop
Due items per exercise	Usually one	Several, when they fit naturally
Grading	Whole card, pass or fail	Each word scheduled on its own
Prompt	The same card repeats	A new sentence each time
Setup	You build and maintain the deck	Curriculum and grading are built in

What Anki still does well

Anki is still a strong choice for general memorization, or if you like building decks. For usable vocabulary in less review time, packing due words into fresh sentences beats a bigger card queue.

Try a language-optimized loop

If you want to try these optimizations without building them yourself, try LinGoat. See how LinGoat works or start free at app.lingoat.app.

References

1. Nielsen, E., et al. (2024). Sentence-based spaced repetition for vocabulary learning. Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA). https://aclanthology.org/2024.bea-1.29/
2. Nation, I. S. P. (2001). Learning Vocabulary in Another Language. https://doi.org/10.1017/CBO9781139524759
3. Tulving, E., & Thomson, D. M. (1973). Encoding specificity and retrieval processes in episodic memory. Psychological Review. https://doi.org/10.1037/h0020071
4. Ye, J., Su, J., & Cao, Y. (2022). A stochastic shortest path algorithm for optimizing spaced repetition scheduling. https://doi.org/10.1145/3534678.3539081
5. Expertium. (2025). Benchmark of spaced repetition algorithms. https://expertium.github.io/Benchmark.html
6. Sweller, J. (2010). Element interactivity and cognitive load. Educational Psychology Review. https://doi.org/10.1007/s10648-010-9128-5