NEET PG Prep App Development using Bubble.io
Budget: ₹12,500 – ₹37,500 INR
My app is called ReviseLikeTeacher. It is a NEET PG (and similar exams) prep platform that builds a personalized, data-driven revision plan. It combines a question bank, AI-style feedback on answers, a memory-decay–based revision scheduler, and advanced analytics for both students and admins.
Core user roles
Student: signs up, sets target exam/date/score, study capacity, and practices questions with voice or text answers while following a personalized schedule.
Admin / Faculty: manages the question bank, imports content from PDFs, reviews AI extractions, and monitors cohort performance.
Main modules / pages
Dashboard (index)
Shows target exam, exam date, target score, and study capacity.
Displays readiness status, reasoning/summary messages, recent updates.
Offers quick-start practice modes: balanced mix, more clinical, rapid-fire.
Shows today’s revision plan, 7‑day schedule, adherence/progress, topic mastery, recall/memory-decay views, and subject proficiency.
Practice (practice)
Runs question sessions (esp. ENT example content).
Supports voice-based answering (record, stop, re‑record).
After submission, shows feedback like “what you did well”, “what was missing”, model explanation, and impact on topic mastery.
Tracks current session stats (questions, accuracy, time, mode) and shows session history.
Has a session setup popup to choose number of questions, mode, subjects/topics, difficulty mix, and question-type distribution.
Question Studio (question_studio)
Full question bank manager: filter by subject, topic, type, difficulty, importance, cognitive focus, status.
Create/edit questions with fields for stem, type, difficulty, importance, cognitive focus, ideal answer/key points, previous year tags, optional image.
PDF upload and extraction pipeline:
Upload a PDF, app parses and extracts questions, detects type/subject/topic/importance/cognitive focus, and key points.
Admin reviews extractions (accept/reject/save draft) before adding to the question bank.
Question preview with usage stats (attempts, correct rate, etc.).
Revision Schedule (schedule)
Personalized, memory-decay–driven schedule based on exam date, capacity, and mastery.
Shows configuration (exam, date, target score band, daily minutes, weekly questions).
Weekly readiness forecast and plan‑vs‑actual adherence.
Daily sessions list with subject, topic, planned vs completed questions and status (complete/partial/skipped).
Tools to regenerate schedule, “tune balance” (clinical vs factual, difficulty and load distribution), and update session status.
Memory decay inspector and heatmaps for topics, recall levels, and remaining revisions.
Metrics Lab (metrics_lab)
Advanced analytics / model tuning console.
Global filters (date range, exam, subject, topic, importance, difficulty).
Tabs for:
Per question: attempts, correct rate, avg score, difficulty, avg revisions.
Per topic: success rate, time to strong, typical revisions, cognitive focus.
Per subject: mastery distribution, high‑importance weak areas.
Per exam: blueprint coverage, students on track/borderline/off‑track.
Per question type: success rate and attempts by format.
Additional blocks: memory-decay trends, revisions distribution, cohort topic difficulty, missed key points analytics, and tuning parameters (mastery thresholds, recall thresholds, scheduler weights, forecast weights, etc.).
Admin Dashboard (admin_dashboard)
Cohort‑level monitoring: active students, on‑track/borderline/off‑track percentages, avg questions/week, rolling accuracy.
Global mastery distribution and top weak topics across the cohort.
Error rates by question type and cognitive focus (factual/conceptual/clinical).
Summaries of content coverage and gaps.
Recent PDF imports and question extraction summary.
Sample student trajectories (improving/plateau/declining).
Auth & onboarding
Common header across pages with Log In / Sign Up / Log Out and menu navigation.
Popups for login and sign‑up.
Onboarding flow that collects: target exam, exam date, target score band, chosen subjects, daily study minutes, weekly question target, then creates a userprofile record.
Separate Edit Study Capacity popup to adjust daily minutes and weekly questions.
Other pages
reset_pw: standard password reset flow.
404: simple “page does not exist” placeholder.
State of implementation (from the freelancer’s perspective)
Frontend UI for all core modules (Dashboard, Practice, Question Studio, Schedule, Metrics Lab, Admin) is already built in Bubble.
Core workflows exist for:
Auth (login, signup, logout).
Onboarding and user profile creation.
Creating/editing questions and PDF upload records.
Basic schedule generation display, session status updates.
Toggling tabs, filters, and popups across the app.
The data model already includes key types like userprofile, question, attempt, topicmastery, questionmastery, revisionschedule, examreadiness, pdfupload, extractedquestion, and system tuning parameters.
How you can paste this into a job post
You can copy everything above into your job description, then add a short section like:
“I need a Bubble freelancer to:
Review and refine the existing data workflows (question attempts, mastery, scheduling).
Implement or connect AI services for PDF parsing, question extraction, and answer evaluation where needed.
Optimize performance and UX on the main flows (onboarding → schedule → practice → analytics).
Help me get this to a production-ready state (privacy rules, edge cases, responsive tweaks, etc.).”
Core user roles
Student: signs up, sets target exam/date/score, study capacity, and practices questions with voice or text answers while following a personalized schedule.
Admin / Faculty: manages the question bank, imports content from PDFs, reviews AI extractions, and monitors cohort performance.
Main modules / pages
Dashboard (index)
Shows target exam, exam date, target score, and study capacity.
Displays readiness status, reasoning/summary messages, recent updates.
Offers quick-start practice modes: balanced mix, more clinical, rapid-fire.
Shows today’s revision plan, 7‑day schedule, adherence/progress, topic mastery, recall/memory-decay views, and subject proficiency.
Practice (practice)
Runs question sessions (esp. ENT example content).
Supports voice-based answering (record, stop, re‑record).
After submission, shows feedback like “what you did well”, “what was missing”, model explanation, and impact on topic mastery.
Tracks current session stats (questions, accuracy, time, mode) and shows session history.
Has a session setup popup to choose number of questions, mode, subjects/topics, difficulty mix, and question-type distribution.
Question Studio (question_studio)
Full question bank manager: filter by subject, topic, type, difficulty, importance, cognitive focus, status.
Create/edit questions with fields for stem, type, difficulty, importance, cognitive focus, ideal answer/key points, previous year tags, optional image.
PDF upload and extraction pipeline:
Upload a PDF, app parses and extracts questions, detects type/subject/topic/importance/cognitive focus, and key points.
Admin reviews extractions (accept/reject/save draft) before adding to the question bank.
Question preview with usage stats (attempts, correct rate, etc.).
Revision Schedule (schedule)
Personalized, memory-decay–driven schedule based on exam date, capacity, and mastery.
Shows configuration (exam, date, target score band, daily minutes, weekly questions).
Weekly readiness forecast and plan‑vs‑actual adherence.
Daily sessions list with subject, topic, planned vs completed questions and status (complete/partial/skipped).
Tools to regenerate schedule, “tune balance” (clinical vs factual, difficulty and load distribution), and update session status.
Memory decay inspector and heatmaps for topics, recall levels, and remaining revisions.
Metrics Lab (metrics_lab)
Advanced analytics / model tuning console.
Global filters (date range, exam, subject, topic, importance, difficulty).
Tabs for:
Per question: attempts, correct rate, avg score, difficulty, avg revisions.
Per topic: success rate, time to strong, typical revisions, cognitive focus.
Per subject: mastery distribution, high‑importance weak areas.
Per exam: blueprint coverage, students on track/borderline/off‑track.
Per question type: success rate and attempts by format.
Additional blocks: memory-decay trends, revisions distribution, cohort topic difficulty, missed key points analytics, and tuning parameters (mastery thresholds, recall thresholds, scheduler weights, forecast weights, etc.).
Admin Dashboard (admin_dashboard)
Cohort‑level monitoring: active students, on‑track/borderline/off‑track percentages, avg questions/week, rolling accuracy.
Global mastery distribution and top weak topics across the cohort.
Error rates by question type and cognitive focus (factual/conceptual/clinical).
Summaries of content coverage and gaps.
Recent PDF imports and question extraction summary.
Sample student trajectories (improving/plateau/declining).
Auth & onboarding
Common header across pages with Log In / Sign Up / Log Out and menu navigation.
Popups for login and sign‑up.
Onboarding flow that collects: target exam, exam date, target score band, chosen subjects, daily study minutes, weekly question target, then creates a userprofile record.
Separate Edit Study Capacity popup to adjust daily minutes and weekly questions.
Other pages
reset_pw: standard password reset flow.
404: simple “page does not exist” placeholder.
State of implementation (from the freelancer’s perspective)
Frontend UI for all core modules (Dashboard, Practice, Question Studio, Schedule, Metrics Lab, Admin) is already built in Bubble.
Core workflows exist for:
Auth (login, signup, logout).
Onboarding and user profile creation.
Creating/editing questions and PDF upload records.
Basic schedule generation display, session status updates.
Toggling tabs, filters, and popups across the app.
The data model already includes key types like userprofile, question, attempt, topicmastery, questionmastery, revisionschedule, examreadiness, pdfupload, extractedquestion, and system tuning parameters.
How you can paste this into a job post
You can copy everything above into your job description, then add a short section like:
“I need a Bubble freelancer to:
Review and refine the existing data workflows (question attempts, mastery, scheduling).
Implement or connect AI services for PDF parsing, question extraction, and answer evaluation where needed.
Optimize performance and UX on the main flows (onboarding → schedule → practice → analytics).
Help me get this to a production-ready state (privacy rules, edge cases, responsive tweaks, etc.).”