Anki and AI MCQ are not directly comparable on distractor quality.

Most Anki cards in the wild do not. The dominant community decks for medical school (AnKing, Zanki, Pepper) are predominantly cloze deletion cards, where a blanked-out span in a sentence is the answer and there are no wrong options to evaluate. The cloze format skips the distractor question entirely. The Anki cards that DO have distractors are the rarer subset: hand-written multiple-choice notes, MCQ-format imports from question banks like UWorld or Amboss, and .apkg files exported from an AI MCQ generator. The comparison only resolves on that subset.

Half-malformed. The framing assumes both sides emit MCQs at comparable rates, but in practice Anki users live mostly inside cloze cards. The honest answer is to first ask what you actually want to compare: hand-written Anki MCQ cards against AI MCQ generators, question-bank imports against AI-generated MCQs, or the cloze workflow as a whole against the MCQ workflow as a whole. Three different answers. The page below walks each split.

Hand-written distractors vary enormously by author. A motivated student writing a card on a topic they understand well can put together better distractors than any generator because they know which adjacent concepts a peer would actually confuse. The cost is roughly one to two minutes per card if you are writing real distractors and not filler. On a 200-card lecture, that is three to seven hours of typing. AI MCQ generation is 60 seconds for the whole deck. On a held-out four-criterion eval, the best AI MCQ generator scored 81.3 on distractor quality; the worst scored 57.8. A motivated hand-writer probably beats 81.3 on their own deck and gets crushed by the time cost on volume.

Wide. The held-out three-document eval at studyly.io/quality scored Studyly 81.3, Unattle 78.0, Gauntlet 68.0, Turbolearn 57.8 on the distractor-quality axis. The mechanism that decides the score is whether the distractor pool is sourced from the same upload as the correct answer (source-grounded) or pulled from the model's pretrained distribution of plausible-sounding wrong answers. Tools that ground their distractors avoid four of the five common failure modes (filler templates, length tells, pretrained drift, grammatical mismatch) by construction.

Yes, on the per-card axis. UWorld and Amboss employ physician item-writers who follow the NBME Item-Writing Guide and revise distractors based on student performance data. Their cards are gold standard for Step 1 and Step 2 distractor quality. The catch: those banks do not cover your professor's specific slide deck or the class exams that decide your GPA. The 'Anki vs AI MCQ' question is usually being asked about class content, not boards content. For class content, neither UWorld nor a community Anki deck exists, and the realistic comparison is hand-writing your own MCQ Anki cards or letting a source-grounded AI MCQ generator make them.

The NBME Item-Writing Guide says distractors must be plausible (drawn from common student misconceptions or close-topic neighbors of the correct answer), parallel in grammar and length, and never one of the filler shapes ('all of the above', 'none of the above', 'both A and B', 'it depends'). The same principles apply whether the card is hand-written, imported from a bank, or AI-generated. The rubric does not care about the source, only about whether the option list discriminates between students who know the material and students who do not.

If your study workflow is boards-content cloze cards from AnKing, the distractor question is mostly irrelevant; you are not making MCQs and you should not switch tools to add a workflow you do not need. If your study workflow needs MCQs on class content (your professor's slide deck, where no community Anki deck exists), the honest comparison is between hand-typing distractors yourself (high quality, slow) and a source-grounded AI MCQ generator (81.3 on the held-out eval, 60 seconds per deck). Tools that are not source-grounded land at field-average and need post-hoc editing on most cards.

Yes. A source-grounded AI MCQ generator that exports to .apkg ships the cards as a Studyly-namespaced note type ('Studyly MCQ'), which does not collide with AnKing or Zanki note types on import. The cards land in a new top-level deck with the lecture title. AnKing stays untouched. The MCQ cards live alongside your cloze cards in the same Anki collection, which is the configuration most med students actually want.

Mostly not. Four of the five distractor failure modes (filler templates, length tells, grammar mismatch, same-as-correct decoys) have no cloze analog because there are no distractors. The fifth, pretrained drift, can still apply: a model writing a cloze card can occlude a fact that contradicts your professor's slide. The cloze workflow is structurally simpler on the distractor question and structurally harder on the recognition question (the cloze cue is fixed so on revisit five you are matching the wording, not retrieving the concept).

Methodology, the four criteria, the held-out documents, and the per-tool scores are at studyly.io/quality. The longer write-up specifically on AI Anki distractor failure modes and a 90-second-per-card grading rubric you can run on any deck is at studyly.io/t/anki-card-distractor-quality.