← all posts
ai

Building a Better Anki: Enhancing Flashcards for Multi-Step Active Recall

08 Oct 2025 · 5 min read · vmargb

Flashcards persistently remain essential for all forms of learning and disciplines. Anki's spaced-repetition excels for simple tasks like remembering facts, definitions, vocabulary etc… However it clearly reveals its limitation with complex tasks like coding, problem-solving or studying grammar(over vocabulary) which demand more sophisticated approaches. The obvious answer here would be to transition to traditional note-taking for anything that goes beyond brute-memorisation, but what if there's a better way? This post outlines my vision for a next-generation flashcard system which prioritises multi-step active recall and adaptive learning.

The Problem with Linear Flashcards

Most flashcards assume knowledge is binary, you either know it or you don’t. But learning is often more of a progressive process.

You might grasp a concept in plain English but struggle to implement it in code.

You might remember a formula but fail to derive it step-by-step.

You might recognise a grammatical rule but forget how to arrive there

A better system would:

Break complex topics into sequential recall steps. Adjust review frequency per step (not per card). Allow branching paths, different routes to the same answer.

Instead of a single "answer," each flashcard has multiple checkpoints, each reinforcing understanding.

Multi-Step Active Recall

Traditional flashcards present linear front/back interactions, but more complicated concepts are not as simple as that, sometimes understanding a concept requires passing through multiple bridges or middle-men that help you arrive at the conclusion. For example, lets say we're solving a coding problem:

  1. Front: Question text
  2. First Recall: Solution in plain-english
  3. Second Recall: Code implementation

or:

  1. Front: Question text
  2. First Recall: Solution in plain-english
  3. Second Recall: Step-by-step solution (still in english)
  4. Third Recall: Code implementation

Example

Front How would you reverse a linked list?

First Recall (Plain English Explanation) Start from the head, and iteratively reverse the pointers of each node until reaching the end.

Second Recall (Step-by-Step Explanation)

  1. Initialise three pointers: previous, current, next.
  2. Iterate through the list:
    • Store the next node.
    • Reverse the current node's pointer to point to the previous node.
    • Move the previous and current pointers forward.
  3. Return the new head (previous pointer).

Third Recall (Code Implementation) 

def reverselinkedlist(head):
    prev, curr = None, head
    while curr:
        nextnode = curr.next
        curr.next = prev
        prev = curr
        curr = nextnode
    return prev

And each layer towards the solution has its own confidence prompt. Perhaps it is more difficult to arrive at the code straight away, but an explanation in plain english creates a eureka moment for you. This allows you to solidify each layer and systematically arrive at the solution slowly. Which allows you to focus on one cognitive task at a time without overloading your working memory.

The system should evaluate each recall layer independently. A user might:

  • Find the conceptual explanation straightforward (easy rating)
  • Struggle with edge cases in implementation (hard rating)
  • Require more frequent review for the coding step while spacing out the conceptual review

Branching Paths for Adaptive Learning

Some topics require different approaches. Instead of rigid steps, we could instead allow optional alternate pathways:

## Grammar: Past Perfect Tense

  • Front: When do you use past perfect?
  • Path A (Rule-Based):
    • Answer: "For actions completed before another past action."
  • Path B (Example-Based):
    • Answer: "E.g., 'She had left before I arrived.'"

This approach would make flashcards completely different from how Anki usually works because Anki treats each card as a fixed question–answer pair, with one “right” response. In contrast, a multi-path system introduces more flexibility. Where instead of memorising isolated facts, you could have multiple angles within a single card, this prevents review sessions from becoming repetitive-drills.

Validate