Does explaining the wrong answers actually help?

Supported, with a precise boundary. Little et al. (2012) is real and has held up, but the advantage is specific to related, untested information. On directly tested material, short answer with feedback wins: Kang et al. (2007), and the synthesis in Üner, Tekin & Roediger (2021).

For an exam of novel passages testing adjacent knowledge, related-untested is arguably the right target. So the claim survives for the MCAT specifically, not in general.

This is the weak link. Three problems:

1. Butler, Karpicke & Roediger (2007) tested feedback type on MC directly and found no difference. Timing mattered; type did not.
2. Finn & Metcalfe (2010) found the winning elaboration was learner-generated: scaffolded hints. Provided elaboration tied with plain correct-answer feedback.
3. Alamri & Higham (2022) found corrective feedback actively impairs related-item performance, the exact outcome the Little benefit is measured on.

Revised: MC works for transfer when the learner is made to reason about the distractors, not when the explanations are handed to them.

No research supports this, because nobody has studied it. What exists is adjacent: LLM distractor generators still produce invalid distractors needing expert review, and automated explanatory-feedback pipelines report meaningful error rates.

The likelier explanation is structural. Anki ships no multiple-choice note type, no distractor field, and no way to record confidence. The format is absent from the tooling, not merely expensive, and a bad distractor is worse than none, since the entire effect is conditional on competitiveness.