Anki AI: how to turn your lecture notes into good flashcards (without trusting the AI blindly)

By Roupen Odabashian, MD, FRCPC · Published October 20, 2026 · Last updated November 7, 2026 · 12 min read

How we write and check articles: Editorial Policy.

A student at a laptop turns lecture notes into flashcards, with a stack of cards and a stethoscope beside the screen.

AI-assisted research and drafting. How we write.

If you want to use AI with Anki, the safest approach is to treat AI as a drafting assistant, not an autopilot. AI tools can generate first‑pass flashcards from your own material, but you still need to review, edit and delete cards before they ever hit your long‑term spaced‑repetition schedule.^[1] If you refuse to edit, an all‑in‑one AI platform may be less dangerous than piping everything straight into Anki.

How does AI actually help you make Anki flashcards from lecture notes?

AI is best used to draft flashcards from your lecture PDFs or notes, then you review and decide what is worth sending into Anki. Some AI tools can already generate cards directly from PDFs and notes and then schedule them on FSRS themselves, instead of in Anki.^[1]^[2] No matter the stack, you still own the editing step.

Right now, AI tools fall into two patterns. Some are classic spaced‑repetition engines that added AI on top; others are AI‑first platforms that started from ingesting documents and only later added spaced repetition.^[2] At least one AI platform lets you upload a PDF, lecture notes or a past paper and turns that into a flashcard deck using FSRS scheduling.^[1] Another platform records lectures, transcribes them, identifies key points and creates cards automatically.^[2]

This gives you a practical workflow:

Capture your raw content once (PDFs, lecture handouts, typed notes).
Use AI to propose question–answer pairs from that content.
Delete, rewrite or split cards until they actually test the right thing.
Either keep them inside the AI platform’s own FSRS scheduler, or export to Anki if the AI tool supports .apkg export.^[1]

If you are curious about how FSRS‑based tools compare in general, we cover that in more depth in our pieces on Anki and AI study tools, such as our Anki guide and our overview of best AI study tools.

What does spaced repetition (and FSRS) actually do for these AI‑generated cards?

Spaced repetition works by revisiting information at increasing intervals, roughly doubling the gap when you recall correctly and shortening it when you forget.^[3] Modern schedulers like FSRS aim to predict when you are about to forget and show you the card just before that, using models of memory to target a chosen retention level.^[3]^[4]^[5]

Spaced repetition has decades of evidence behind it. Early work proposed spacing “acts of revision” over intervals that grow from one day to two days, four days, eight days and so on.^[3] Later, large‑scale tests on thousands of students showed spaced repetition is effective at improving recall.^[3] Software schedulers now automate this by adapting the spacing based on how easily you recall each card.^[3]

For you, the key point is this: a powerful scheduler amplifies whatever you put into it. FSRS can help you remember more with fewer reviews, but if the underlying AI‑generated cards are vague, wrong or recognition‑based, the algorithm will efficiently reinforce bad learning. That is why every AI card from any platform should be treated as a draft that you explicitly approve before it joins your daily queue.^[1]^[2]

Alma schedules its flashcards with FSRS, an open spaced-repetition algorithm that Anki also offers as an option. In Alma, deck options include a desired retention setting (70 to 97%, default 90) and an Exam Mode with short intervals so cards return before your exam.

What do AI flashcard generators usually get wrong (and how do you fix it)?

AI flashcard tools can turn PDFs or lectures into cards quickly, but they often produce decks that are only a first draft: there may be duplicates, cards that test recognition instead of recall, or ones that fixate on definitions instead of clinical reasoning.^[1]^[2] You should plan to prune and edit AI cards before using them seriously.

Reviews of AI flashcard tools consistently point out this trade‑off. One comparison notes that an AI tool can generate a deck from your PDF or notes and schedule it on FSRS, but that the auto‑generated cards are still just a starting point; you are expected to delete duplicates and rewrite weak questions.^[1] Another review highlights that an AI note taker can record live lectures, transcribe them, identify key points and create cards, yet the spaced repetition and configurability remain more basic than in dedicated engines.^[2]

Common AI errors include:

Overly long cards: Paragraph‑length answers where you should have two or three focused cards.
Pure definition questions: “What is X?” instead of “Patient with Y, why is X happening?” For medicine, this under‑tests application.
Shallow distractors: For multiple‑choice cards, options that are obviously wrong and teach you nothing.
Incorrect or hallucinated facts: Any AI model can misinterpret a PDF or mix in outside information.

Some tools even warn you not to “blindly trust the finished deck without double‑checking,” especially when the AI is drawing from uploaded sources.^[6] That is a good norm to adopt for every AI flashcard, whether it lives in Anki or in an AI platform.

Practical editing steps you can apply:

Delete any card that you would never want to see on test day.
Split compound questions into separate prompts.
Turn recognition prompts (e.g. “Which of these is true…”) into active recall prompts (e.g. “List three features of…”).
Where the AI seems to be guessing, cross‑check with your lecture slides or a trusted primary resource before keeping the card.

Alma leans into this workflow: you upload lectures and notes to the Learning Hub for vignette-style quizzes, write in the Notebook and turn selected text into a flashcard with the Card button, and every AI-generated card gets a check-and-edit step before it is saved. Alma is not a source of real exam questions; it works from your own material.

How do Anki FSRS, image occlusion, and .apkg imports fit into an AI workflow?

Anki remains the most configurable spaced‑repetition engine, with FSRS scheduling and extensive add‑ons for things like image occlusion and extra scheduling control.^[4]^[2]^[7] Some AI tools export decks in Anki’s .apkg format, so you can combine AI generation with Anki’s review engine instead of choosing one or the other.^[8]^[1]

Anki supports powerful scheduling and customization. Its FSRS option replaces the older SM‑2 algorithm and has been benchmarked as more accurate in predicting recall.^[4]^[2] Anki’s manual notes that FSRS helps you remember more material in the same amount of time by more accurately modeling forgetting.^[5] You can tune desired retention (often around 90%) and let the software adapt intervals accordingly.^[5]

On the content side, Anki stores information in notes that generate one or more cards, and you can extend its capabilities with add‑ons.^[4]^[7] Official documentation mentions that add‑ons can provide features such as “extra control over scheduling.”^[7] Community add‑ons also implement image occlusion, where you cover parts of an image (for example, an anatomy diagram) and then reveal them during review.^[9]

For AI workflows, several tools can export generated decks as .apkg files so that Anki can import them and you can review using FSRS.^[8]^[1] Some systems that talk to Anki use the Anki‑Connect add‑on, which starts an HTTP server inside Anki to allow external applications to execute actions like exporting and importing .apkg packages.^[8] The same API supports including or excluding scheduling data when exporting a deck.^[8]

Once you understand how spaced repetition and .apkg deck exchange work, you can decide where each card type belongs: some cards might live entirely inside an AI platform; complex multi‑step reasoning cards may be worth hand‑crafting in Anki; and diagram‑based or occlusion cards could sit wherever your workflow for images is smoother.

Should you pair Anki with AI add‑ons, or use an AI platform with built‑in FSRS?

If you like total control and are prepared to spend time editing, Anki plus AI add‑ons can give you the deepest spaced‑repetition engine with AI as a helper.^[4]^[2] If you want less configuration and built‑in document ingestion, an AI‑first study platform with FSRS may suit you better, even if its scheduling is less tweakable.^[1]^[2]

Existing reviews describe a real split in the market. Some tools, like Anki and other classic spaced‑repetition engines, emphasise the scheduling core and then layer AI generation on top.^[2] Others, like AI‑first platforms, start by ingesting PDFs or lectures, summarising them and generating cards, then add spaced repetition later.^[1]^[2] One such AI‑first platform supports FSRS and a knowledge graph, offers PDF annotation with flashcard generation and image occlusion, and sells multiple pricing tiers with AI credit limits as of the time that review was written.^[2]

Anki itself is free on desktop and on some mobile platforms, with a paid iOS app funding development and sync services, but it has no built‑in AI card generation.^[2] You either build cards manually or install add‑ons, some of which connect to external AI APIs.^[2]^[7] The upside is that Anki’s FSRS implementation is highly configurable; the downside is that integrating AI takes more setup.^[4]^[2]

AI‑first platforms often come with simpler spaced repetition but easier ingestion. One comparison notes that a particular platform’s spaced repetition is “functional but less configurable than Anki’s FSRS,” while another supports FSRS alongside the older SM‑2 algorithm.^[2] Another article points out that AI flashcard tools can sit between Anki and simpler options by auto‑building cards from your PDFs and then scheduling them on FSRS, cutting down manual work.^[1]

A rough decision guide:

If you already live in Anki and enjoy tweaking deck options, keep using it as your main engine. Add AI only where it saves you time on drafting cards, and be disciplined about editing before importing.
If you rarely open Anki because building cards feels like a chore, consider trying Alma's flashcards. Alma has image occlusion too, and you can import an .apkg deck, which keeps the deck tree and can bring review history across so your scheduling does not restart from zero. You can also export a deck back to .apkg.
If your exam is very close, use whatever puts high‑quality cards in front of you tonight: ready‑made decks in Anki or a fast AI generator that works off your current lecture notes.^[1]

For more detailed comparisons across AI tools and when Alma is and is not a good fit, you can read our broader overviews, such as best AI tools to study for medical school or our discussion of general‑purpose AI versus med‑specific platforms.

How should you review AI‑generated cards before you actually start studying?

Before you study with AI‑generated cards, do a focused quality pass: check facts against your lecture or primary source, rewrite prompts to test recall, and delete aggressively. Some reviewers of AI flashcard tools explicitly recommend not blindly trusting AI and double‑checking finished decks instead.^[1]^[6]

One hands‑on report with conversational AI flashcards noted that cards drawn from uploaded documents pulled out important names, concepts and details, yet the writer still cautioned against trusting the deck without double‑checking.^[6] Cards you miss can later be practised again, but if the question is flawed you are just repeating a misconception.^[6]

Borrow this simple review process for any AI‑generated deck, whether in Anki or in an AI platform like Alma:

Source check: For each new batch, skim the underlying PDF or slides to ensure the AI did not misread a table or confuse similar entities.
Delete freely: If a card is low yield, confusing, or redundant with a better card, delete rather than “fixing” it. The AI can always generate more.
Rewrite prompts: Move from definition‑only questions toward clinical or application‑focused prompts where appropriate.
Tag or group: Cluster related cards to make later editing easier if you realise an entire lecture’s framing has changed.

When is Alma a better fit than Anki with AI, and when not?

Alma can be a good fit if your bottleneck is turning messy lecture material into something learnable; Anki with AI add-ons may suit you better if you already have a solid Anki habit and want maximal control. Both depend on you verifying AI output against reliable sources rather than trusting the model.

Frequently asked questions

Can AI replace making Anki cards by hand?

AI can cut down the time it takes to draft cards from PDFs or lecture notes, but it cannot replace your judgement. Reviews of AI flashcard tools consistently state that automatically generated cards should be treated as a first draft that you still need to edit and prune.^[1]^[2] If you skip that step, you risk reinforcing inaccurate or low‑yield material.

Is FSRS really better than older algorithms like SM‑2?

FSRS is designed to model memory more accurately than SM‑2 by tracking factors like difficulty and retrievability.^[4] Benchmarks cited in reviews indicate that FSRS can achieve the same recall with fewer reviews than SM‑2.^[4]^[2] In Anki, FSRS is built in as an optional scheduler and is recommended for users whose clients all support it.^[4]^[5]

Do I need image occlusion, or are text cards enough?

For image‑heavy subjects like anatomy, image occlusion can be very helpful: you cover parts of an image and test yourself on what is hidden.^[9] Anki supports this via add‑ons, and some AI‑first platforms support similar image‑based card types.^[2] For more conceptual material, well‑designed text cards plus occasional diagrams are usually enough.