SparkMind - The AI-native learning OS that grows with you

Static Learning Systems

Traditional learning platforms deliver fixed curricula that fail to adapt to individual interests and learning styles.

One-Size-Fits-All Content

Students receive the same materials regardless of:

• Cognitive pace
• Learning preferences
• Background knowledge
• Motivation patterns

Outdated Materials

Textbooks and courses become obsolete quickly in fast-changing fields.

Teacher Overload

Educators spend excessive time on:

• Lesson planning
• Grading
• Content adaptation
• Administrative tasks

Late Intervention

Most systems detect learning gaps only after poor exam results, when it is already too late.

Primary Users

• K–12 students
• High school learners
• University students
• Homeschoolers
• Lifelong learners

Secondary Users

• Teachers
• Schools
• Tutors
• Parents
• Corporate training teams

User Profile

• Curious and digitally native
• Uses tablets/laptops daily
• Interested in self-directed learning
• Open to AI-powered guidance

Learner Features

• AI-powered knowledge graph
• Personalized learning paths
• Interactive learning expeditions
• Multimodal lessons (text, audio, visuals, simulations)
• Adaptive difficulty scaling
• Progress dashboard
• Curiosity recommendations

Teacher Features

• AI lesson generator
• Quiz and assessment builder
• Content adaptation by level
• Student risk alerts
• Performance analytics

MVP Limitations

• Limited simulation library
• Core subjects only
• English-first launch
• Manual curriculum mapping

High-Level Architecture

Client Apps (Web/Mobile)
        ↓
API Gateway
        ↓
Auth & User Service
        ↓
Learning Data Engine
        ↓
Knowledge Graph Service
        ↓
AI Orchestration Layer
        ↓
Content & Simulation Engine
        ↓
Analytics & Prediction Service

Architecture Style

• Microservices
• Event-driven pipelines
• Cloud-native deployment
• Modular AI services

Users

users
- id (UUID)
- email
- role (student/teacher/parent)
- created_at

Profiles

profiles
- user_id
- age
- grade_level
- preferences_json
- learning_style

Knowledge Graph

knowledge_nodes
- id
- user_id
- topic
- mastery_level
- last_updated

Activities

activities
- id
- user_id
- topic
- type
- score
- duration
- timestamp

Lessons

lessons
- id
- topic
- difficulty
- content_json
- created_at

Assessments

assessments
- id
- lesson_id
- user_id
- result_json
- created_at

Risk Flags

risk_flags
- id
- user_id
- type
- confidence
- created_at

Authentication

POST /v1/auth/login
POST /v1/auth/refresh

Learning Data

POST /v1/activity/log
GET  /v1/activity/history

Knowledge Graph

GET  /v1/graph
POST /v1/graph/update

Lessons

GET  /v1/lessons/recommend
POST /v1/lessons/generate

AI Assistant

POST /v1/assistant/chat

Teacher Tools

POST /v1/teacher/lesson
GET  /v1/teacher/students

Analytics

GET /v1/analytics/progress
GET /v1/analytics/risk

Frontend

• React / Next.js
• React Native (mobile)
• TypeScript
• TailwindCSS
• Recharts
• Zustand

Backend

• NestJS (Node.js)
• PostgreSQL (Prisma)
• Redis
• gRPC (internal services)

AI / ML

• Python FastAPI
• PyTorch
• Graph ML models
• LLM APIs
• Reinforcement learning

Infrastructure

• AWS / GCP
• Kubernetes
• Terraform
• GitHub Actions

Integrations

• Google Classroom
• Khan Academy
• Coursera (content sync)

• FERPA & GDPR compliance
• Child data protection
• AES-256 encryption
• TLS 1.3
• Role-based access control
• Parental consent management
• Audit logs
• Data minimization policies

B2C

• Freemium student tier
• Premium: €10–20/month
• Family plans

B2B

• School licenses
• District-wide subscriptions
• Enterprise training packages

Add-Ons

• Advanced simulations
• Certification tracks
• AI tutor sessions

Phase 1: Early Adopters

• Homeschool communities
• EdTech forums
• AI enthusiasts
• Indie teachers

Phase 2: Content Marketing

• Learning blogs
• YouTube tutorials
• Study productivity content

Phase 3: Partnerships

• Schools
• Tutoring centers
• Publishers

Phase 4: Enterprise

• Governments
• Universities
• Corporations

Pre-Build

• Landing page + waitlist
• Demo expeditions
• Teacher interviews

Beta

• 200–500 students
• Classroom pilots
• Teacher feedback loops

KPIs

• Weekly active learners
• Session duration
• Topic mastery growth
• Retention rate
• Teacher adoption rate

Phase 1 (0–3 months)

• Core MVP
• Knowledge graph engine
• Basic AI lessons

Phase 2 (4–6 months)

• Simulations
• Teacher dashboard
• Risk detection

Phase 3 (7–12 months)

• Multilingual support
• Advanced analytics
• Parent portals

Phase 4 (12+ months)

• Global rollout
• Accreditation
• Open ecosystem

• Data privacy regulations
• AI bias in learning paths
• Over-reliance on automation
• Teacher resistance
• Content accuracy
• Scalability costs
• Regulatory approval
• Trust building

• VR/AR classrooms
• AI debate partners
• University credit programs
• Career path mapping
• Skill-to-job matching
• Creator marketplace
• Personalized textbooks
• Neurodiversity-focused modules

You are a senior full-stack engineer building the MVP for “SparkMind” (AI-native Learning Operating System). Implement a production-ready but minimal system with a Next.js web app (student + teacher dashboards), a NestJS (Node/TypeScript) backend API, and a Python FastAPI AI orchestration service for knowledge graph updates, personalization, and generative learning expeditions. Use PostgreSQL for core data and S3-compatible object storage for generated artifacts (lesson packs, images, simulation bundles, audio).
The system must create a persistent Interest + Knowledge Graph for each learner, generate adaptive learning expeditions from any prompt, provide teacher automation tools, and run predictive analytics to flag students at risk early.
GOALS (MVP)
User can sign up and authenticate (Google/email magic link acceptable as stub).
User can choose role: student or teacher (optional parent/guardian role can be stubbed).
Student can ask a question (“How do I build a city on Mars?”) and receive an interactive learning expedition:
structured learning path (steps)
multimodal lesson cards (text + images + optional audio)
1–2 interactive activities (quiz, flashcards, mini-sim)
adaptive difficulty based on grade/age
System builds and updates the student’s Knowledge Graph:
topics (nodes)
prerequisites (edges)
mastery level per node
interest intensity per node
misconceptions/struggle tags
System tracks learning signals:
time on task
quiz attempts
correctness
confusion events (“I don’t get it”)
drop-off points
Student can view:
graph summary (“what you know”)
progress dashboard
recommended next expeditions (“curiosity feed”)
Teacher can:
generate lesson plan from a topic + grade level + standards tag (optional)
generate quizzes (MCQ + short answer)
adapt material to reading levels (e.g., grade 4 vs grade 8)
Teacher can view class analytics:
mastery overview
struggling topics
“at-risk” flags
System issues proactive nudges:
recommended review
suggested next activity
teacher intervention prompt (for at-risk students)
All generated content is stored as versioned artifacts and reproducible (prompt + params + model_version).
TECH STACK
Web App
Next.js (React), TypeScript
TailwindCSS
TanStack Query (react-query)
Zustand
Recharts (charts)
TipTap/MDX (rich lesson rendering)
Backend API
NestJS + TypeScript
PostgreSQL (Prisma ORM)
Redis (queues, caching)
JWT auth (or NextAuth for MVP + API tokens)
AI / Orchestration Service
Python FastAPI
LLM APIs (for expedition generation, teacher assistant)
Embeddings + vector search (pgvector or separate vector DB)
Graph processing (NetworkX for MVP; upgrade path later)
Basic ML for risk prediction (rule-based + logistic regression baseline)
Storage / Infra
S3-compatible object storage (private ACL)
Docker Compose (local dev)
Deploy via AWS ECS Fargate / Render / Fly.io
GitHub Actions CI/CD
DELIVERABLES
A) Monorepo structure:
/apps/web
/apps/api
/apps/ai
/packages/shared
B) Docker Compose that runs:
postgres
redis
api
ai
C) Prisma migrations matching schema below.
D) REST API endpoints with validation + auth.
E) Minimal web UI flows:
Auth + role selection
Student: Ask → Expedition → Activities → Results
Student: Progress + Graph view
Teacher: Create lesson + quiz + adapt reading level
Teacher: Class dashboard + at-risk flags
F) Seed script:
20 demo topics
10 demo expeditions
3 demo classes (teacher + students)
100 activity events
DATABASE SCHEMA (Prisma Models)
User(id, email, role, createdAt)
StudentProfile(userId, age, gradeLevel, learningPrefsJson, createdAt, updatedAt)
TeacherProfile(userId, schoolName?, createdAt)
Classroom(id, teacherId, name, createdAt)
ClassroomMember(id, classroomId, studentId, joinedAt)
Knowledge Graph
KnowledgeNode(id, studentId, topic, canonicalKey, mastery, interest, difficultyPref, updatedAt)
KnowledgeEdge(id, studentId, fromNodeId, toNodeId, relationType, weight)
Expeditions & Content
Expedition(id, studentId, prompt, topicKey, gradeLevel, status, modelVersion, createdAt)
ExpeditionStep(id, expeditionId, order, title, contentJson)
Artifact(id, ownerId, type, objectKey, metadataJson, createdAt)
Activities & Signals
Activity(id, expeditionId, type, payloadJson, createdAt)
Attempt(id, activityId, studentId, score, answersJson, durationSec, createdAt)
EventLog(id, studentId, eventType, payloadJson, timestamp)
Teacher Tools
LessonPlan(id, teacherId, topicKey, gradeLevel, contentJson, createdAt)
Quiz(id, teacherId, topicKey, gradeLevel, questionsJson, createdAt)
Risk & Analytics
RiskFlag(id, studentId, classroomId?, type, confidence, reasonsJson, createdAt)
API ENDPOINTS (NestJS)
Auth
POST /v1/auth/login
POST /v1/auth/refresh
GET /v1/me
Student
POST /v1/expeditions { prompt, gradeLevel?, mode? }
GET /v1/expeditions
GET /v1/expeditions/:id
POST /v1/activities/:id/attempt
POST /v1/events/log
Knowledge Graph
GET /v1/graph
GET /v1/graph/summary
POST /v1/graph/recompute
Curiosity Feed
GET /v1/recommendations/next
POST /v1/recommendations/refresh
Teacher
POST /v1/teacher/classes
POST /v1/teacher/classes/:id/invite (stubbed token invite ok)
GET /v1/teacher/classes/:id/students
POST /v1/teacher/lesson-plans { topic, gradeLevel, readingLevel }
POST /v1/teacher/quizzes { topic, gradeLevel, format }
POST /v1/teacher/adapt { contentId, targetReadingLevel }
Analytics
GET /v1/analytics/student/:id
GET /v1/analytics/class/:id
GET /v1/analytics/risk?classId=
AI SERVICE (FastAPI)
Endpoints
POST /expeditions/generate
Input:
prompt
student profile (age, grade, prefs)
current knowledge graph summary
Output:
expedition JSON (steps, activities, recommended artifacts)
topicKey + prerequisites
estimated difficulty
POST /graph/update
Input:
attempt results
events
prior graph snapshot
Output:
node mastery updates
interest updates
struggle tags
new edges/prereqs suggestions
POST /recommend/next
Input:
student graph + recent events
Output:
list of next recommended expeditions with reasons
POST /teacher/lesson
Input:
topic + gradeLevel + readingLevel
Output:
lesson plan JSON + handouts
POST /teacher/quiz
Input:
topic + gradeLevel + difficulty
Output:
quiz JSON + rubric
POST /risk/predict
Input:
classroom aggregates + student recent performance
Output:
risk flags with reasons + confidence
IMPLEMENTATION NOTES
Graph-first design: store canonical topic keys (e.g., space.mars.habitats) to avoid duplicates.
Explainability: every recommendation and risk flag must include “why” (reasonsJson).
Reproducibility: store modelVersion, prompt params, and tool outputs for each expedition.
Safety: add content filters and teacher controls (block topics, set age constraints).
Adaptive difficulty: start with rule-based leveling (grade bands) before ML personalization.
Interactive activities MVP: implement quizzes + flashcards + “design canvas” (simple constraint-based sim).
Caching: cache graph summaries and expedition renders for 15–30 minutes per user.
Cost controls: enforce token budgets; compress context; store embeddings once per topic.
PROJECT STRUCTURE
/apps
  /web         (Next.js)
  /api         (NestJS)
  /ai          (FastAPI)
/packages
  /shared      (types, zod schemas, constants, topic taxonomy)
LOCAL DEV
Provide docker-compose.yml running:
postgres
redis
api
ai
Provide .env.example for each app.
Provide seed scripts:
demo classes, students, expeditions, activity events.
DEPLOYMENT
Provide Dockerfiles for api + ai
Deploy plan (minimal):
Postgres (RDS / Neon / Supabase)
Redis (Upstash / ElastiCache)
S3 bucket (private) for artifacts
API + AI services on ECS/Fly/Render
Secrets in Secrets Manager / platform secrets
HTTPS via Cloudflare / ALB
SECURITY CONSIDERATIONS
FERPA/GDPR readiness
Role-based access control (student/teacher/parent)
Tenant isolation per classroom/school
Encryption at rest + TLS 1.3
Signed URLs for artifact access
Rate limiting + abuse detection for AI endpoints
Audit logs for teacher actions (content generation, edits, exports)
Parental consent flags for minors (MVP: required checkbox + stored timestamp)
QUALITY BAR
Input validation using Zod/class-validator everywhere
Centralized error handling + typed error codes
Correlation IDs + structured logging
Unit tests:
graph update rules
expedition generation contract validation
risk flag logic
Integration tests:
student expedition flow end-to-end
teacher lesson/quiz generation flow
Data integrity constraints (unique canonical topic keys per student)
SUCCESS METRICS
Activation: first expedition completed within 10 minutes
Weekly active learners (WAL)
Average session duration
Mastery growth rate per week
Teacher time saved (self-report + usage proxy)
At-risk detection precision/recall (pilot)
Retention: 4-week cohort return rate
FINAL INSTRUCTION
Now implement the SparkMind MVP end-to-end following this specification.
Prioritize personalization quality, safety for minors, teacher usefulness, explainability, and cost control.