Recall First

Recall First is an AI study partner that shows you what you only think you know

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Recall First

Live site · Open the app · Android APK · Scope · PRD · Spec · Demo video

Latest release npm package on GitHub Packages MIT licence Live site Built with the Devpost Learn skill pack

About

Recall First is an AI study partner that shows you what you only think you know.

You add your notes (PDF, Word, text, or pasted). The AI writes questions from those notes only. You answer from memory, by typing or speaking, and you say how sure you are before you see the result. At the end, Recall First puts what you felt you knew next to what you actually knew, and tells you exactly which confident answers were wrong, so you know what to study first.

Most AI study tools make studying feel easier. Recall First measures whether your confidence is earned.

Recall First: find out what you only think you know

Contents

Why this exists

Asking an AI to summarise your notes feels like studying. The summary reads smoothly, so it feels familiar, and that feeling of fluency gets mistaken for knowledge. Students stop studying too early, and the gaps show up in the exam.

Two findings shaped this project:

  • Answering from memory beats rereading. Practice testing outperforms restudying across 122 experiments.
  • Learners can't tell what they know without testing it. Confidence judgments only become accurate after the learner gets direct evidence of what they can and can't recall.

So the app never explains first. You answer, you commit to a confidence level, and only then do you see the result.

How it is different

Quizlet Gemini Notebook (NotebookLM) ChatGPT Study Mode Knowt Anki Recall First
Questions generated from your own files Paid Yes Yes Yes No (add-ons) Yes, PDF, Word, text, paste
Answer typed or spoken from memory, no multiple choice Partly Partly Yes Partly Self-graded Yes, Whisper speech to text
Confidence rated before each result No No No No No Yes, four levels
Score that punishes being sure and wrong No No No No No Yes, certainty-based XP
"Felt vs knew" calibration chart and Brier score No No No No No Yes
Catch a planted error in an AI summary No No No No No Yes
Second question, new angle, on each confident miss No No No No No Yes (hypercorrection)
Explanations only after you answer No No Optional No n/a Always
Account required Yes Yes Yes Yes Optional No

Competitor features are taken from their public pages and a 2026 comparison of AI study tools. The point is not that they are bad tools. They compete on generating content, shared libraries and scheduling. None of them measures whether your confidence matches what you can actually recall, and that is the whole product here.

See it working

On Android

Screenshots of the Recall First APK running on Android 15 (1080 × 2400, Android emulator), against the live site with real AI. These runs also caught two phone-only bugs that are now fixed: the page drawing under the status bar on Android 15, and the keyboard popping up over each question.

Recall First on Android: start, question, confidence, sure-but-wrong feedback, explanation

Start screen
Add notes as sources		 Source added
Each source is a switchable chip		 Question
The coach asks, and reads it aloud
Confidence
Commit to a confidence: see the stakes		 Feedback
Certain and wrong: −8 XP		 Why
"Why?" names the mix-up, adds a memory hook

How it works, in motion

Answering by voice
Quiz: sample notes → AI question → answer by voice (transcribed by Whisper) → Certain → "Wrong, −8 XP" → Why?		 Results
Results: spot the false sentence → felt vs knew → calibration chart → AI coach → Ask your notes

The results screen

Spot the error
One sentence in this AI summary is false		 Felt vs knew
The gap between felt and knew		 Calibration chart
Does each confidence level mean what it says?
AI coach
AI coach: the pattern and three next steps		 Ask your notes
Ask your notes, with the line quoted		 Voice answer
Spoken answer, ready to edit

The results captures and GIFs were recorded in Chromium at phone size (390 × 844) against the live site. The microphone input in the voice GIF is a synthesized voice clip played into a virtual microphone.

How it works

flowchart LR
  A[Add notes<br/>PDF · Word · text · paste] --> B[AI writes questions<br/>from your notes only]
  B --> C[Coach reads the question aloud]
  C --> D[You answer from memory<br/>type or speak]
  D --> E[You choose a confidence<br/>Guessing · Unsure · Fairly sure · Certain]
  E --> F[AI grades it<br/>right · partly · wrong]
  F --> G{Sure and wrong?}
  G -- yes --> H[−XP, Why? explanation,<br/>study first]
  G -- no --> I[+XP]
  H --> J[Results: felt vs knew,<br/>calibration, coach]
  I --> J
  J --> K[Fix these now · Ask your notes ·<br/>Anki and calendar export]
  1. Add your notes. Drop PDFs, Word files, text or Markdown, or paste. Text is extracted in the browser; files are never uploaded or stored.
  2. Pick a level. Easy, Exam or Hard. Hard questions make you connect facts from different parts of the notes.
  3. Answer from memory. The notes are hidden. Type, or tap the mic and speak.
  4. Say how sure you are, before the result. The screen shows what is at stake: Certain earns +4 if right and costs −8 if wrong. "I don't know" costs nothing, because honesty is never the mistake.
  5. Get graded. Paraphrases count. Half-answers get half credit. Wrong or hinted answers can ask "Why?".
  6. See the gap. Felt vs knew, a calibration score, which confidence levels you can trust, and a coach with three next steps.
  7. Close the gap. Fix the confident misses with a new question from a different angle, write the correction in your own words, ask your notes, and export what you missed for spaced review.

Features

Catch the illusion

  • Confidence before every result, four levels, with the stakes shown live.
  • Certainty-based XP. Sure and right earns most, sure and wrong costs most, "I don't know" costs nothing.
  • Calibration chart and Brier score. How often each confidence level was right, against what it claims.
  • Fluency traps. Answered fast, marked Certain, and wrong.
  • Spot the false sentence. An AI summary of your notes with one planted error.

Close the gap

  • Fix these now. A second question on each confident miss, from a new angle.
  • Why? Explain it. After a miss, the explanation from your notes plus a memory hook.
  • Teach the gap. Write the correction in your own words; it is graded against your notes.
  • Hint ladder. Three hints, weakest first; a right answer after a hint counts half.
  • Blind-spot map. Which concepts in your notes were right, missed, sure but wrong, or untested.
  • AI coach. Names the pattern in your run and gives three concrete next steps.

Make it stick

  • Guess first. Quiz before you read, read, then quiz again with new questions.
  • Mix sources. Questions spread across several files.
  • Spaced return. Export missed questions to Anki, or add 1, 3 and 7-day calendar reminders.
  • Streak and XP, kept only in your browser.
  • Share the shape, not the notes. A Wordle-style card with no content in it.

Voice and AI

  • Read aloud. Questions, feedback, explanations and coach messages are spoken.
  • Answer by voice. Recorded, transcribed by Groq Whisper, put in the answer box for you to check. Never submitted on its own.
  • Ask your notes. After the quiz only. Answers come only from your notes, with the supporting line quoted word for word; questions your notes can't answer are refused.
  • Question-quality check endpoint flags questions whose answer isn't in the notes.

The research behind it

Found with academic search (Consensus, SciSpace). Recall First applies these findings; it does not claim to prove learning gains on its own.

Finding Source Where it shows up
Practice testing beats rereading, and transfers to new questions (122 experiments, d ≈ 0.40) Pan & Rickard, 2018, Psychological Bulletin The whole loop
Recall tests help more than recognition tests Rowland, 2014, Psychological Bulletin Typed or spoken answers, no multiple choice
After retrieval practice, confidence judgments become more accurate Barenberg & Dutke, 2018, Memory Confidence before every result
Feedback on both performance and judgments reduces overconfidence Callender et al., 2016, Metacognition and Learning Calibration chart and coaching line
Errors made with high confidence are the easiest to correct after feedback Butterfield & Metcalfe, 2001; Carpenter et al., 2018 Study these first, Fix these now
…but they come back after a week unless re-tested Butler, Fazio & Marsh, 2011 Calendar reminders; week-later re-test in the roadmap
Pretesting before studying improves learning; learners underestimate it Pan et al., 2023; Pan et al., 2021 Guess first mode
Successive relearning (retrieve until correct, over spaced days) improves exams and retention Rawson et al., 2013; Higham et al., 2021 Spaced return; relearning decks in the roadmap
Interleaving helps for confusable topics, less for word lists Brunmair et al., 2019, meta-analysis Mixed sources
Self-explanation beats elaborative interrogation O'Reilly et al., 1998 Teach the gap
Students with free AI answers did worse once it was removed; hints reduced the harm Bastani et al., 2025, PNAS No explanations before answering, hint ladder

Honest limits. The testing effect is weaker and less certain in maths (Murray et al., 2025), so Recall First fits fact- and concept-heavy notes best. Training calibration with a scoring rule alone has had null results (Martin et al., 2024), which is why the app pairs the score with per-level feedback and a concrete next step rather than a number alone.

Run it

Try it now: recall-first.vercel.app/app. Press Try sample notes if you don't have notes handy. On Android, install the APK and allow the install when asked.

Run it locally (Node.js 22 or newer, and a free Groq key or an Anthropic key):

git clone https://github.com/LSUDOKO/recall-first.git
cd recall-first
npm install
cp .env.example .env        # paste your key after GROQ_API_KEY= (or ANTHROPIC_API_KEY=)
npm start                   # http://localhost:3000 (landing) and /app (the quiz)

Or install the package from GitHub Packages. GitHub's npm registry needs a GitHub token with read:packages, even for public packages:

echo "@lsudoko:registry=https://npm.pkg.github.com" >> ~/.npmrc
echo "//npm.pkg.github.com/:_authToken=$(gh auth token)" >> ~/.npmrc
GROQ_API_KEY=your-key npx @lsudoko/recall-first     # http://localhost:3000
Command What it does
npm start Local server with live AI
RECALL_MOCK=1 npm start Practice mode: canned questions and a word-overlap grader, no key needed
npm test 23 tests: score, XP, calibration, scheduling, interleaving, share codec
android/build.sh Rebuilds dist/recall-first.apk with the Android SDK build-tools (no Gradle)

Deploy: the repo deploys to Vercel as-is (public/ static, api/*.js functions). Set GROQ_API_KEY (or ANTHROPIC_API_KEY) in the project's environment variables. Optional: RECALL_MODEL, RECALL_TTS_VOICE.

Read aloud with Groq's voice: Groq's Orpheus voice model needs a one-time terms acceptance by the Groq org owner at console.groq.com/playground?model=canopylabs%2Forpheus-v1-english. Until then the browser's built-in voice is used automatically.

Architecture

flowchart TB
  subgraph Browser["Browser or Android app (WebView)"]
    UI["public/app.js<br/>screens, session, XP, voice"]
    SRC["public/sources.js<br/>PDF (pdf.js), Word (mammoth), text"]
    CAL["public/calibration.js<br/>Brier, reliability, coaching"]
    ST["localStorage<br/>XP, streak, trend only"]
  end
  subgraph Server["server.js locally, api/*.js on Vercel"]
    R["lib/routes.js"]
    AI["lib/ai.js<br/>strict JSON-schema calls"]
    V["lib/voice.js<br/>speak, transcribe"]
  end
  Groq["Groq<br/>gpt-oss-120b · Whisper · Orpheus"]
  Claude["Claude (optional)<br/>claude-opus-5-5"]
  UI -->|fetch /api/*| R
  SRC --> UI
  CAL --> UI
  UI --> ST
  R --> AI --> Groq
  AI -.-> Claude
  R --> V --> Groq
  • One API, two hosts. lib/routes.js holds every handler; server.js serves them locally and api/*.js wraps them as Vercel functions.
  • Structured output everywhere. Every model call sends a strict JSON schema and the reply is validated again on the server before the page sees it.
  • Keys stay on the server. The browser never sees an API key.
  • No database, no accounts. Notes travel in the request and nowhere else. XP, streak and the felt/knew trend live only in the learner's browser.
Route Purpose
POST /api/questions Five questions, concepts, hints and a summary with one planted error (difficulty: easy, exam, hard)
POST /api/grade right, partly or wrong, with feedback and the correct answer
POST /api/followups A new-angle question for each confident miss
POST /api/explain Why the answer is right, plus a memory hook (after answering only)
POST /api/coach The pattern in this run and three next steps
POST /api/ask Answers from the notes with a verbatim quote, or a refusal
POST /api/review Flags questions not answerable from the notes
POST /api/speak Text to speech (Groq Orpheus), browser fallback
POST /api/transcribe Speech to text (Groq Whisper)
GET /api/status Provider, model and voice availability

Built for Build With AI: Basics

This project was built for the Devpost learning hackathon Build With AI: Basics, using the Devpost Learn skill pack. The main requirement is to design and build with the skill pack, using flipped interaction and planning documents. Everything below can be checked in this repository.

The repository and the four planning documents: scope, PRD, technical spec, build checklist

How the skill pack was used

flowchart LR
  A[1-start<br/>flipped interview,<br/>learner profile] --> B[2-scope<br/>scope.md]
  B --> C[3-prd<br/>prd.md]
  C --> D[4-spec<br/>spec.md]
  D --> E[5-build<br/>checklist.md,<br/>one slice per commit]
  E --> F[6-ship<br/>deploy, app map,<br/>wrap-up]
Skill What it did Proof in this repo
1-start Introduced flipped interaction: the AI interviews the learner, the learner brings the idea and makes the decisions. Wrote the learner profile every later skill reads. Skill at .claude/skills/1-start. The profile is kept out of git on purpose, as the skill requires: see the /devpost/learner-profile.md rule in .gitignore.
2-scope Turned the idea into one kernel and one core loop, with what is in and out of the proof of concept. devpost/scope.md, status: approved
3-prd Product requirements: the user journey, every screen, edge cases, look and feel, each traced back to the scope. devpost/prd.md, status: approved
4-spec Technical spec in plain language: server, API routes, AI calls with JSON schemas, file structure. devpost/spec.md, status: approved
5-build Split the build into small slices. Each slice has a "why now", PRD and spec references, a mechanical verify step and a commit message, and was ticked only after it passed. devpost/checklist.md, status: approved, slices 1–8 ticked
6-ship Deployment, a code tour that maps the code back to the plan, and the wrap-up record. devpost/app-map.html, live at recall-first.vercel.app

The planning documents

Scope Build checklist
scope.md on GitHub, status approved checklist.md on GitHub, slices ticked
Product requirements (PRD) Technical spec
prd.md on GitHub, status approved spec.md on GitHub, status approved

The plan, slice by slice, in the commit history

The proof of concept was built exactly in the checklist's order, one commit per slice:

Checklist slice Commit
Empty repository with a licence 04451e5 Initial commit
1. Paste notes; the page says when there is enough to quiz on 960a7b1 Add Start screen with note intake and server
2. Quiz me turns your notes into five real questions 04b0c9b Generate five questions from notes with structured output
3–4. Answer from memory, rate confidence, graded fairly 3edc47c Add answer, confidence picker, I don't know flow and grading
5. Results show where confidence and knowledge disagree 2719b18 Add results with calibration scale and study-first list
6. Errors, edge cases, README and polish be45e0c Handle errors and edge cases, add README
Ship: code tour and wrap-up 5753229 Add app map and build wrap-up records
7. Expansion features e07a272 Add hints, calibration scoring, spot-the-error, fix round and exports
8. Deployed on Vercel 130b817 Deploy to Vercel

Changes of plan are written down, not hidden

Every change after approval is recorded under Revisions in devpost/checklist.md, with the reason and the documents it touched. Examples: the look changed twice, slices 3 and 4 were merged, Groq was added as a provider, and voice and the AI tutor came after the proof of concept. Work beyond the original scope is kept as a separate Expansion After the Proof of Concept section in the PRD, so the approved plan stays readable.

Submission checklist

Requirement Where
New project, started from an empty folder during the submission period First commit 04451e5 is an empty repo with a licence
Built with the skills (1-start → 6-ship) .claude/skills/ and their outputs in devpost/
Planning docs in the repo Scope, PRD, spec and checklist, all status: approved
Open source licence MIT
Public repository You are here
Demo video (1–3 minutes) recall-first-film.mp4 (2:54, in the latest release)
Working app recall-first.vercel.app and the Android APK

Project layout

server.js              local server: static files + /api/*
api/                   Vercel functions, one per route
lib/ai.js              question, grade, follow-up, explain, coach, ask, review (Groq or Claude)
lib/voice.js           Groq Orpheus text to speech, Groq Whisper speech to text
lib/routes.js          the API handlers, shared by both hosts
lib/vercel.js          wraps a handler as a Vercel function
public/index.html      landing page
public/app.html        the quiz
public/app.js          session, screens, XP, voice, results
public/sources.js      PDF, Word and text extraction in the browser
public/calibration.js  Brier score, per-level reliability, coaching line
public/store.js        browser-only decks, relearning schedule, share codec (roadmap)
public/styles.css      design tokens and components
public/media/          logo, banner, screenshots and GIFs used in this README
android/               WebView wrapper and a Gradle-free build script
dist/recall-first.apk  signed debug build
test/                  node --test suites
devpost/               scope, PRD, spec, checklist, app map
samples/               example notes

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