# How Learner Progress Is Tracked and What the Review Queue Is in AI Engineering From Scratch > Discover how AI Engineering From Scratch tracks learner progress via localStorage and what the review queue is for targeted re-teaching of underperforming lessons. - Repository: [Rohit Ghumare/ai-engineering-from-scratch](https://github.com/rohitg00/ai-engineering-from-scratch) - Tags: deep-dive - Published: 2026-09-02 --- **The AI Engineering From Scratch curriculum tracks learner progress entirely in the browser using a `localStorage`-based system exposed as `window.AIFSProgress`, while the "review queue" is a tutor-managed markdown log that captures lessons scoring below 70% for targeted re-teaching.** The rohitg00/ai-engineering-from-scratch repository implements a privacy-first, serverless learning architecture where every quiz interaction and checkpoint completion is stored locally. This self-contained approach eliminates network dependencies while providing granular visibility into the learning journey, complemented by a structured review mechanism that automatically surfaces concepts requiring reinforcement. ## Browser-Only Progress Architecture The curriculum eschews server-side state management in favor of a lightweight, client-side tracking system defined in **[`site/progress.js`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/site/progress.js)**. This design ensures that learning data remains private and accessible offline. ### The localStorage Data Model At the core of the tracking system is a structured JSON object stored under the key `aifs:progress:v2`. The schema version 2 structure captures every meaningful interaction a learner has with the curriculum: ```javascript { schemaVersion: 2, lessons: { "": { answers: { "": { picked: string, // Learner's choice correct: boolean, // Whether it was correct t: number // Timestamp } }, quizPassedAt: number | null, checkpoints: { readAt: number | null, // Read lesson text builtAt: number | null, // Built code from scratch ranAt: number | null, // Ran implementation evidenceAt: number | null // Submitted evidence }, completedAt: number | null, completionSource: string, // e.g., 'learner', 'migrated-v1' visitedAt: number } }, updatedAt: number } ``` The **lesson path** serves as the primary key, using relative paths like `phases/05-nn/01-backprop/` extracted from URLs via the internal `extractPath` function. Each lesson maintains its own state object tracking when the learner first visited, answered quiz questions, passed assessments, and completed four distinct hands-on checkpoints. ## Core API Methods for Tracking Progress The global `window.AIFSProgress` object exposes a minimal API for mutating the progress state. All methods handle the `localStorage` persistence automatically: - **`recordVisit(path)`** – Logs `visitedAt` timestamp when a learner opens a lesson page. - **`recordAnswer(path, qid, picked, correct)`** – Stores individual quiz responses with correctness flags. - **`markQuizPassed(path)`** – Sets `quizPassedAt` if not already recorded. - **`setCheckpoint(path, checkpoint, complete)`** – Updates one of four milestone timestamps (`readAt`, `builtAt`, `ranAt`, `evidenceAt`). - **`markLessonComplete(path, source)`** – Finalizes the lesson by setting `completedAt` and documenting who marked it complete. - **`reset()`** – Clears both v2 and legacy v1 storage keys, effectively wiping all progress. Because these operations write directly to `localStorage`, there is **no network traffic, no user accounts, and no external database**—the progress is completely private to the learner's browser. ## The Review Queue Mechanism While the progress tracker captures *what* the learner did, the **review queue** determines *what they need to redo*. This concept is defined in **[`skills/learn/SKILL.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/skills/learn/SKILL.md)** (Step 4) and addresses the pedagogical requirement for mastery-based progression. ### Automatic Queueing Logic After any lesson quiz concludes, the tutor calculates the score as a ratio (`N/M`). The review queue logic follows a strict threshold: **If the score is less than 70%, the tutor adds the lesson to the review queue along with the specific missed topic(s).** The queue itself persists in the learner's **[`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md)** file—a simple markdown log that the tutor reads and appends to. Entries follow a pipe-delimited format: ```markdown 2024-03-15 | phases/07-transformers/14-build-a-transformer-capstone | 4/5 | attention-mask confusion ``` Each row contains the date, lesson path, score ratio, and a brief description of the knowledge gap. ### Working the Review Queue When a learner completes all lessons in the curriculum, Step 0 of the tutor flow offers the option to *"work the Review queue."* The tutor reads the [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md) file, parses the queued entries, and re-introduces those specific lessons for focused re-teaching. This ensures learners revisit weak spots before claiming full curriculum completion. Because the queue lives in a human-readable markdown file rather than a database, learners can manually edit their [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md) to add topics they want to review or remove entries they feel confident about. ## Practical Implementation Examples The following patterns demonstrate how the progress API and review queue interact in practice: ```javascript // Log that the learner opened the backpropagation lesson AIFSProgress.recordVisit('phases/05-nn/01-backprop'); // Record a quiz answer where the learner chose 'B' incorrectly AIFSProgress.recordAnswer( 'phases/05-nn/01-backprop', 'q3', // Question ID from quiz.json 'B', // Learner's selection false // Incorrect answer ); // Mark hands-on checkpoints as completed AIFSProgress.setCheckpoint('phases/05-nn/01-backprop', 'readAt', Date.now()); AIFSProgress.setCheckpoint('phases/05-nn/01-backprop', 'builtAt', Date.now()); // Finalize the lesson after passing the quiz AIFSProgress.markLessonComplete('phases/05-nn/01-backprop', 'learner'); // Retrieve current state for UI rendering const progress = AIFSProgress.getLessonProgress('phases/05-nn/01-backprop'); console.log(progress.completedAt); // Timestamp or null ``` When a quiz scores below the 70% threshold, the tutor automatically appends an entry to [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md): ```markdown 2024-03-15 | phases/07-transformers/14-build-a-transformer-capstone | 4/5 | attention-mask confusion ``` ## Summary - **Progress tracking** in rohitg00/ai-engineering-from-scratch uses a browser-only `localStorage` implementation in [`site/progress.js`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/site/progress.js), exposing the `AIFSProgress` API for recording visits, quiz answers, and four distinct hands-on checkpoints. - The **review queue** is a pedagogical mechanism defined in [`skills/learn/SKILL.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/skills/learn/SKILL.md) that automatically logs lessons scoring below 70% into a [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md) file for later re-teaching. - All learner data remains local to the browser, requiring no server infrastructure or user accounts while maintaining a complete audit trail of learning activity. - The integration between these systems ensures that learners cannot complete the curriculum without addressing identified knowledge gaps. ## Frequently Asked Questions ### Where is my progress stored if there is no server? All progress data lives in your browser's `localStorage` under the key `aifs:progress:v2`. The [`site/progress.js`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/site/progress.js) module serializes the state object to `localStorage` on every mutation, meaning your data persists across browser sessions but never leaves your device. If you clear your browser data or use incognito mode, your progress will be reset. ### What triggers a lesson to enter the review queue? A lesson enters the review queue when the tutor detects a quiz score below 70%. Specifically, after calculating the ratio of correct answers (`N/M`), if the percentage is less than 70, the tutor appends a row to [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md) containing the lesson path, score, and topic description. This threshold is hard-coded in the tutor logic within [`skills/learn/SKILL.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/skills/learn/SKILL.md). ### Can I manually add lessons to the review queue? Yes. Because the review queue persists as plain text in your [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md) file, you can manually append entries following the pipe-delimited format: `YYYY-MM-DD | lesson-path | score | topic`. The tutor will read these entries during the "work the Review queue" phase regardless of whether it created them or you did. ### How do I reset my progress and start over? Invoke the `reset()` method on the global `AIFSProgress` object. This method clears both the current v2 storage key and any legacy v1 keys, returning the curriculum to a pristine state. Alternatively, you can delete the `aifs:progress:v2` key directly from your browser's developer tools localStorage panel.