Flashcard generator · biology notes
Biology notes are half diagrams. Most generators only read the text.
Every generator handles the slide bullets. Almost none handle the labeled figure on the next slide. That figure is the part your biology professor writes the practical from. Here is what changes when the generator extracts both, in a real 58-second run on a 90-slide membrane-transport deck.
Direct answer · verified 2026-05-16
Upload your biology notes (PDF lecture deck, PPTX, scanned textbook chapter, handwritten photo from your phone, or a YouTube lecture) to studyly.io. About 60 seconds later you have roughly 200 multiple-choice cards from a 90-slide deck, plus an image-occlusion card for every labeled figure in the source. Export to Anki as .apkg with the diagram masks intact, or drill inside Studyly where the stems reword on revisit. Held-out three-document quality eval: 81.3 vs Unattle 78.0, Gauntlet 68.0, Turbolearn 57.8. Methodology on studyly.io/quality.
What “biology notes” actually looks like to a generator
Walk through a typical biology lecture deck and the content splits into two piles. The first pile is bullet-text slides: definitions, taxonomies, mechanisms written as numbered steps, comparison tables. A reasonable text-only generator handles this pile fine. The second pile is figures with labels pointing at structures: a eukaryotic cell with seven organelles tagged, a Na/K pump cross-section with ion movements annotated, a phylogenetic tree with clade names attached, a microscopy image with cell types pointed at. This is the pile that disappears when the generator only reads text. And for most biology classes, this is also the pile your professor writes the practical from.
The shape of a biology-aware flashcard generator follows from that split. The pipeline has to extract both the text content and the labeled regions on each figure, then create a different card type for each kind of fact. The diagram below is what that looks like as a single source going in and four card types coming out.
One biology source, four card formats
Anchor fact · one real 58-second run
A 90-slide membrane-transport deck, end to end.
The terminal below is the actual pipeline log on a real bio lecture: 90 slides, 14 labeled diagrams (the Na/K pump cross-section, the secondary-active-transport schematic, a phospholipid bilayer with channel proteins tagged, and so on). 76 slides carried testable content; 14 were title or reference slides and got skipped. The output is 204 multiple-choice cards from the text plus 14 image-occlusion cards on the labeled figures. Total: 218 cards in 58 seconds, .apkg export including IO Enhanced masks. Hand-placing one image-occlusion mask in Anki runs 10 to 15 minutes; the 14 figures alone would have been a 2 to 3 hour afternoon.
What the held-out eval actually measured
Three lecture documents that none of the tested generators had seen before (a slide deck, a textbook chapter, a paper) were held out from any training set. Each tool generated questions from the same three sources. Graders scored every output on four dimensions: factual correctness against the source, stem clarity, distractor plausibility, and coverage of question types. Higher is better.
The 23.5-point gap between Studyly and Turbolearn is the difference between a deck where most cards are usable and one where you spend half your study time editing the generator's mistakes. Full methodology, the rubric, and the leaderboard are public on studyly.io/quality.
What text-only generators handle vs what biology actually needs
Side by side on a typical biology lecture, type of content vs how each generator treats it. The figure-handling rows are where the deck either reflects the practical or doesn't.
| Feature | Typical text-only generator | Studyly |
|---|---|---|
| Text on a slide (definitions, mechanisms, classification) | Read and turned into term/definition pairs or short-answer cards. This is the part every generator does well. | Same, plus an MCQ stem with three plausible distractors drawn from neighboring concepts in the same lecture, not from a generic web bank. |
| Labeled diagram (cell organelles, dissection plate, microscopy) | Most commonly skipped entirely. Some tools attach the figure as a static image card with no masking. You see the diagram with the labels still on it, which tests nothing. | Figure extracted, every label parsed, mask placed over each labeled region. You see the diagram with one structure hidden and recall what it is. Identical to the IO Enhanced workflow Anki users know. |
| Pathway figure (Krebs, glycolysis, signal transduction) | Either lost in the figure-drop or summarized in a text card that names the inputs and outputs but skips the intermediate steps. | Each step on the pathway diagram becomes an image-occlusion card. The case-style stem variant asks you to predict what happens if the intermediate is blocked. |
| Microscopy image with cell-type tags | Stripped on extraction. Even when the image survives, the tag positions are not resolved, so no card asks you to identify the cell type. | Tags are resolved to coordinates, each cell-type label becomes an image-occlusion card on that region of the image. |
| Distractor quality on bio MCQs (the part that tests concept discrimination) | Distractors drawn from open-web sources, often biologically nonsensical or obviously wrong, which makes the card a recognition test on the format rather than the fact. Field-average distractor score: 67.9. | Distractors drawn from the same lecture context, so wrong answers are plausible biology, not random text. Held-out distractor score 81.3 vs Turbolearn 57.8. |
| Revisit behavior (same fact on Friday vs Monday) | Same stem, same options, same order. By review #3 you have memorized the wording, not the underlying mechanism. | Stem reworded by a rephrase pass on every revisit, distractor pool rotated, format can shift from MCQ to case-style stem. The fact stays the same; the path to it changes. |
When a generator is the wrong tool for the bio class
Two honest cases where a different approach fits better.
- Quantitative biology with worked problems. Population genetics calculations, enzyme kinetics, Punnett squares with multi-allele crosses. These need a step-by-step problem solver, not a flashcard generator. Concept questions on those topics work; the calculation walkthroughs do not.
- Lab-protocol memorization for a specific bench technique. If you need to memorize a 14-step Western blot protocol exactly as your TA wrote it, type the steps into a numbered card by hand and put it on cloze deletion. Generators are good at facts distributed across pages; they are middling on a single ordered list where every step has to come back in the right slot.
Try it on your next bio lecture
Drop the PDF in. Watch the labeled figures come back as cards.
Free tier on app.jungleai.com, no credit card. The email gate sends a one-click access link.
Common questions about generating flashcards from biology notes
How do I actually make flashcards from my biology notes?
Drop the file in (PDF lecture deck, PPTX, handwritten photo, scanned textbook chapter, or a YouTube lecture link) on studyly.io. The system runs an extraction pass that pulls out both the testable text facts and any labeled diagrams on the source. About 60 seconds later you have roughly 200 multiple-choice cards from a 90-slide deck, plus an image-occlusion card for every labeled figure. Export to Anki as .apkg, or drill them inside Studyly where the wording rephrases on revisit.
What makes biology notes different from any other lecture material?
Diagram density. A typical biology lecture is 30 to 50 percent labeled figures: cell organelles, the Krebs cycle laid out in a circle, a signal transduction cascade, a dissection plate with structures pointed at, a microscopy image with cell-type tags. Most flashcard generators read the slide as text and skip the figures. That means the cards reflect maybe half of what your professor will test on. The other half (recall the masked label, identify the cell type, name this structure) only shows up if the generator can mask the labels on the figure itself.
Does this work for handwritten biology notes, or only typed PDFs?
Handwritten works. Snap a photo from your phone, the OCR pass runs first, then the same flashcard generator handles the result. Scanned textbook chapters with no OCR layer also work; the OCR happens automatically inside the pipeline. The one limit is figure-label legibility: if your photo of a diagram is blurry, the image-occlusion pass cannot tell where the labels are pointing, and you get text cards only.
Is image-occlusion really worth the extra step versus just MCQs?
For biology, yes. The image-occlusion card type is what trains the visual recall biology exams measure. A text MCQ that asks 'which organelle is responsible for ATP synthesis' is testing one fact. An image-occlusion card with the mitochondrion masked on a cell diagram is testing visual identification plus the fact plus the spatial context. Anatomy practicals, micro labs, and most undergrad bio practicals lean heavily on the second kind. Building image-occlusion cards by hand in Anki with the IO Enhanced add-on runs 10 to 15 minutes per diagram. The automated pass runs in under a second per figure.
What if I use ChatGPT or Quizlet's AI maker instead?
ChatGPT will write you a list of text questions from a prompt and then forget the deck the next time you open a chat. There is no spaced repetition, no quality rubric, no figure handling, and the same wording appears on every revisit so the cards turn into a recognition test by review #3. Quizlet's AI maker reads the text from your notes and produces term/definition pairs, which is fine for vocabulary; it does not handle labeled diagrams as image-occlusion. On a held-out three-document quality eval that grades factual correctness, clarity, distractor quality, and type coverage, Studyly scored 81.3 against the field average of 67.9. Methodology is public on studyly.io/quality.
How many flashcards do I usually get from one biology lecture?
Roughly 200 multiple-choice cards from a 90-slide deck, plus one image-occlusion card per labeled figure in the deck. A 90-slide anatomy lecture with 14 labeled diagrams produced 204 MCQ plus 14 image-occlusion cards in a 58-second run. Cell-bio decks with denser figure content tend to produce more image-occlusion cards (sometimes 20 or 30 per lecture). Pure-text molecular bio decks lean MCQ-heavy.
Can I export the cards out of Studyly into my existing Anki collection?
Yes. The .apkg export carries MCQ, free-response, case-style stems, and image-occlusion cards intact. Image-occlusion cards arrive as the IO Enhanced format Anki users already know, with masks placed on each labeled region. Drop them in next to your AnKing or your own deck and your scheduler picks them up. The one tradeoff: the auto-rephrasing on revisit is a Studyly feature, so if you study the deck in Anki you get the canonical card set, not the reworded versions.
I'm cramming the night before a bio final. Is this actually faster than my own flashcards?
A 90-slide lecture by hand is somewhere between 20 and 30 hours of work if you do MCQ with distractors plus image-occlusion. The same lecture through Studyly is about 60 seconds of conversion plus however long you want to drill the cards. The five-minutes-a-day version is better for retention; the night-before version is the gap between 'no cards' and 'real cards' and that gap is bigger than most students assume.
Does it cover the biology subfields specifically — cell bio, micro, immuno, anatomy?
Yes. The image-occlusion pass is well suited to anatomy (dissection plates, neurovascular plates), cell biology (organelle diagrams, mitosis stages), microbiology (gram stains, virus structure), and immunology (T-cell receptor diagrams, complement cascade). Pathway figures (glycolysis, Krebs, electron transport chain, signal transduction) generate as both image-occlusion cards on the diagram itself and as case-style stems on the underlying mechanism. The product's published power-user verticals include biology, anatomy, immunology, and microbiology specifically.
How is the 81.3 quality score actually measured?
Three lecture documents that no tool had seen before (a slide deck, a textbook chapter, a paper) were held out from any training set. Each tool generated questions from the same three sources. Graders scored every output on four dimensions: factual correctness against the source, stem clarity, distractor plausibility, and coverage of question types. The numbers: Studyly 81.3, Unattle 78.0, Gauntlet 68.0, Turbolearn 57.8. The full methodology, the rubric, and the leaderboard are public on studyly.io/quality.
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