AI flashcards from PDFs, slides and lecture notes in Alma: a step by step workflow for medical students

By Roupen Odabashian, MD, FRCPC · Published October 21, 2026 · 13 min read

How we write and check articles: Editorial Policy.

A medical student watches scattered lecture files transform into organized flashcards and mind maps.

Disclosure: Alma publishes this article and makes one of the study tools it discusses.

If you are a medical student sitting on piles of PDFs, slides and lecture notes, the goal is simple: turn that chaos into tested flashcards without wasting hours formatting. In Alma, you can upload your material, generate AI flashcards from it, and have them scheduled with a modern FSRS spaced repetition algorithm that also powers Anki as an option.^[1]^[2] This article walks step by step through that workflow and how to check AI cards for mistakes. Alma is our own product.

We will cover how to move from a lecture PDF in Alma's Learning Hub to flashcards, how Alma schedules them with FSRS, how to import external .apkg decks, and how to review AI output safely. If you want a broader comparison of AI tools first, see this guide to AI tools for medical students.

What is the fastest safe way to get from PDFs and slides to AI flashcards in Alma?

The fastest safe path has two routes. Route one: upload a lecture file to the Learning Hub, then use Import from Learning Hub in Flashcards to create 1 to 100 cards from it. Route two: write or paste your own notes in the Notebook, then use Generate from Notes to create 5 to 20 cards, or select text and press Card. Either way, review and edit the first pass before you trust the deck, then start short daily reviews, fixing or deleting weak cards as you go.

Student turning lecture printouts into neat flashcards

This workflow has two guardrails. First, everything is driven by your own course material instead of generic AI content, so the wording matches what you are actually tested on. Second, you do not accept the AI’s first draft blindly: you skim for errors, compare to your primary resources and refine the prompts when needed.

Below, we will go concrete: where to click in Alma, which settings matter, how FSRS differs from a simple “again / good / easy” schedule, and how to avoid a deck full of subtle AI hallucinations. For detailed advice on writing card content itself, you can also read our flashcard method guide.

How do I upload PDFs, slides and lecture notes into Alma’s Learning Hub?

To start, gather the material that actually maps to your exams: lecture PDFs, slide decks, faculty handouts and your own typed notes. Alma's Learning Hub accepts PDF, Word, PowerPoint and image files up to 100 MB, or pasted text, and quizzes, mind maps, lecture slides and flashcard import can all draw from that upload.

Student gathering different study files into one binder

From a Learning Hub upload you can generate 8, 12 or 15 clinical vignette questions in the style of your exam, and you can focus on specific sections. You can also import flashcards from the same upload, which is the basis for the next sections.

Before uploading, decide how to chunk your material. Very large files, like entire textbooks, are harder for any AI to turn into focused flashcards in one go. You can mirror the common advice from other tools by working in smaller chunks, such as lecture-sized uploads, so it is easier to check and adjust the cards that come back.

Once your lecture PDFs, slide decks and handouts are in the Learning Hub, give them clear titles like “Renal physiology week 3” rather than vague names. That makes it much easier later when you are generating flashcards or quizzes from specific lectures instead of entire courses.

How do I turn my own notes into flashcards with Generate from Notes in Alma?

After your material lives in Alma, you can build notes that are easier to turn into cards. Alma's Notebook supports rich notes, mind maps, data tables and a knowledge graph, and you can turn selected text into a flashcard with the Card button.

Messy pages turning into tidy digital notes

Quizzes come from Learning Hub uploads, not from Notebook notes, so keep the two steps separate: use a quiz to find what you missed, then write that gap up in the Notebook in your own words. As you clarify confusing points or rephrase faculty slides, those notes accumulate as a structured summary of the lecture rather than a raw transcript.

Alma also lets you turn selected text into an AI image and build mind maps and data tables from your notes. If you sketch out, for example, the renin angiotensin system or cardiac cycle as a mind map, that visual can help you decide which steps deserve their own flashcards. For steps you consistently forget, flashcard pictures can add a textbook illustration or a cartoon mnemonic, or you can use image occlusion on a diagram.

This is the idea behind Generate from Notes: instead of asking AI to invent flashcards from scratch, you first build or clean up human readable notes in the Notebook, then ask Alma for 5 to 20 cards from that note. Once you trust the notes, trim and correct the cards. That way, when a card disagrees with your primary resources, you update the note and regenerate, instead of tweaking isolated cards with no clear source.

How do I turn my notes and uploads into AI flashcards in Alma?

When your notes and files are ready, you can have Alma generate flashcards from them. Alma supports flashcards from your own notes, from Learning Hub uploads and from Books, with decks you can edit afterwards and schedule using FSRS spaced repetition. The key is to start with a specific scope, then aggressively prune and correct.

Alma can create flashcards from either side of your workflow:

From a Learning Hub upload with Import from Learning Hub, which creates 1 to 100 cards and does not count against your AI allowance
From the Notebook with Generate from Notes (5 to 20 cards), or by selecting text and pressing Card

When you generate cards, keep the scope narrow. Other AI flashcard tools describe starting from one course or lecture at a time in their own marketing pages,^[3]^[4] and that is good advice here too. It makes error checking realistic: you can read through 30 cards from one pathology lecture, but not thousands of cards spanning your full curriculum.

Alma is not a question bank of real exam questions and is not affiliated with the NBME, USMLE program, NBOME or any exam body. That means AI cards should mirror patterns of high quality exam questions and flashcards, but not literal questions you remember from test day. For best practice on what belongs on a card and how to avoid pseudo clinical vignettes that are too vague, see our detailed flashcard guide.

Once Alma has proposed a deck, treat it as a draft. Delete redundant or overly long cards, split multi step cards into one fact per card, and add or reword cards so they sound like your own explanations. A competing tool's own page also tells you to read through and change any card you would word differently.^[4] The same applies here: AI is a starting point, not the final product.

How does importing Anki .apkg decks into Alma fit this workflow?

If you already live in Anki, you do not have to choose between years of existing work and Alma's tooling. You can import an Anki deck as a .apkg file into Alma, keeping review history when the file includes it, and continue refining and scheduling those cards alongside decks generated from your PDFs and notes. This lets you centralize, not replace, your existing spaced repetition.

Once imported, you can edit cards, trimming bloated cards or updating outdated guidelines. You can also export a deck as .apkg, and decks are shared only inside an organization.

If a deck is very large or covers multiple unrelated subjects, consider splitting it by topic so that your daily review sessions stay focused.

How does FSRS scheduling in Alma work, and why should I care?

Spaced repetition as a technique is commonly applied when a learner must acquire many items and retain them indefinitely in memory, such as vocabulary in second-language learning.^[5] FSRS is one implementation of spaced repetition that represents the memory state with Stability (S) and Difficulty (D).^[1]

In FSRS, each review updates the card’s Stability and Difficulty based on the grade you give it. Grades typically map to responses like again, hard, good and easy, and FSRS uses those plus the time since last review to compute the next interval.^[1] The memory state is represented by Stability and Difficulty, and the algorithm uses a forgetting curve where retrievability R after t days is modeled mathematically.^[1]

Practically, this means:

Early cards you keep missing will tend to come back frequently in any spaced repetition system, because material that is hard appears more often and material that is easy less often.^[5]
Overdue reviews do not simply explode your schedule; FSRS caps the stability gain from overly late reviews, instead of scaling linearly with delay^[1]

You should still keep review streaks reasonably consistent. FSRS cannot help if you never open the app, but it will protect you from small lapses in scheduling more intelligently than rigid or purely linear systems.

In Alma you can set a deck's target retention between 70 and 97 percent in the deck options, and turn on Exam Mode when a test is close.

How do I check AI generated flashcards for errors and hallucinations?

AI is fast but fallible. You must treat every AI generated card as a suggestion until you have checked it against a trusted source like your course syllabus, faculty slides or primary references. A practical way to do this is to batch review by lecture: check a small set thoroughly, then keep checking any card that feels off when it comes back in later reviews.

Several competing AI tools that turn PDFs, slides or notes into flashcards and quizzes pitch themselves in their own marketing as ways to start studying instead of organizing, but they still assume you read through what the AI wrote and correct anything you would not say yourself.^[3]^[4] AI can misinterpret abbreviations, overgeneralize rare exceptions, or subtly mix up similar sounding drugs. This is especially dangerous in pharmacology and micro.

Here is a concrete process you can use in Alma:

Generate cards from one upload or one chunk of notes, not an entire course
Keep each lecture in its own deck so you can check cards by topic
Keep your original PDF or slides open next to the cards while you check them
Scan each card: is the fact correct, precise and in line with your lecture?
Fix wording so it matches how you think and how your faculty talk
Delete speculative or low yield cards; add missing high yield facts by hand

As you start reviewing, stay alert for cards that feel “off” when they resurface. If a card repeatedly confuses you, treat that as a signal, not a failure: either the card is poorly written, incorrect or addressing a concept you never properly understood. In each case, go back to your primary resource, update your notes in Alma's Notebook, then regenerate or rewrite the card from that corrected note. Ask Alma and Change this card can help you reword a card, but check the result against your source.

For broader context on when AI is and is not safe to trust for medical studying, we recommend reading our guide to AI for USMLE Step 1. Alma is a study tool, not a clinical decision system, and AI content is not a substitute for official exam materials or guidelines.

How do I combine AI flashcards, Alma’s Notebook and mind maps in a full workflow?

Alma keeps the Learning Hub, the Notebook and Flashcards in one workspace, so you can move from a lecture to cards without exporting anything. A typical block might look like this:

Before lecture: skim the PDF in Alma and start a short outline in the Notebook
During lecture: type key clarifications into the Notebook, or upload a fresh version of the slides afterward
After lecture: use Alma to generate a quiz from that upload to test broad understanding
Refine notes: fix gaps based on missed quiz questions and build a mind map from tricky pathways
Make cards: use Generate from Notes, the Card button or Import from Learning Hub, then review them on the FSRS schedule

By keeping everything anchored to your own materials and tightening the loop from lecture to notes to quiz to cards to spaced repetition, you reduce the chance that AI quietly fills in a concept wrong and leaves it unchallenged. Every time a card comes back on a spaced repetition schedule, you have another chance to catch and correct it.

Frequently asked questions

How is Alma’s FSRS schedule different from basic spaced repetition?

Simple descriptions of spaced repetition sometimes involve increasing intervals as you successfully recall information.^[5] FSRS instead represents each card’s memory state with Difficulty (D) and Stability (S) and defines a retrievability function R(t,S) over time.^[1] Anki has also added FSRS as an optional scheduler in recent versions.^[2]

Can I rely on AI flashcards alone for med school exams?

No. AI flashcards are a speed boost for drafting and organizing, not a replacement for primary resources, faculty materials or reputable question banks. You should always check AI generated content against your syllabus, lecture slides and trusted references, especially before using it to guide what you do and do not study.

What if my PDFs are huge textbooks or review books?

Any AI system will struggle to create precise flashcards from an entire textbook at once. Other tools that turn PDFs into flashcards recommend working in chapters or page ranges and focusing on one section at a time.^[6]^[7] In Alma, you can mirror that by splitting uploads into lecture sized chunks and generating cards from one chunk at a time. Alma Books also lets you read a book in the app, ask questions with page cited answers and make flashcards from it.