Study Scheduler System

An intelligent study planning platform that turns daily school progress into a personalized revision schedule.

You log what was taught in school, attach topics to upcoming exams, and the system automatically generates daily study tasks using spaced repetition and exam-aware prioritization.

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🚀 Vision

Most schools complete syllabus delivery close to exam dates, often leaving students with little structured revision time.

This app solves that by continuously planning revision in the background:

• If no exam is set: keeps students always exam-ready through rolling revision.
• If an exam is set: connects taught topics to that exam syllabus and reprioritizes study tasks.
• If topics are revised: they get lower priority than unrevised or weak topics.
• If new topics are added late to an exam: urgency rises automatically.

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🧩 Core Features (MVP)

• Daily topic logging (“What was done in school today?”)
• Exam creation with date and syllabus-topic linking
• Auto-generated daily study plan
• Priority logic based on:
  • spaced repetition due state
  • exam proximity
  • mastery/revision history
  • newly added exam topics
• Study session feedback (easy, medium, hard, missed) to update future plans

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💸 Freemium Model

Free Tier

• Manual topic logging
• Exam + syllabus linking
• Daily auto scheduler
• Basic progress dashboard
• Topic graph view (Obsidian-style visualization)

Premium Tier (AI)

• AI-generated study breakdowns per topic
• AI “what to focus on today” coaching
• AI weak-area diagnostics across subjects
• Smart revision summaries / quiz suggestions
• AI-generated exam-cram plans for short deadlines

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🧠 Scheduling Logic (High Level)

Each topic receives a daily priority score from weighted factors:

• Due factor: overdue/due topics get boosted
• Exam urgency: topics in near exams get boosted
• Mastery factor: well-revised topics get reduced weight
• Recency factor: recently studied topics get temporary reduction
• Late-addition factor: newly added exam topics get short-term urgency boost

This ensures unrevised and weak topics surface first while still protecting exam readiness.

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🗂 Data Model (Conceptual)

• users
• subjects
• topics
• exams
• exam_topics (many-to-many syllabus mapping)
• study_sessions (revision outcomes)
• review_state (spaced repetition state per topic)
• daily_plan
• subscriptions
• ai_usage_logs

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🕸 Obsidian-like Graph View

A visual graph helps students see relationships:

• nodes: topics, subjects, exams
• edges:
  • topic → subject
  • topic → exam (syllabus inclusion)
  • topic ↔ topic (optional conceptual links)
• filters:
  • by exam
  • by due state
  • by unrevised/weak topics
• visual cues:
  • color by mastery
  • size by urgency
  • border/highlight for exam-linked topics

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🔌 Obsidian Integration (Future Plan)

Possible integration tracks:

1. Markdown Export

   • export topics/exams as markdown notes
   • include metadata tags and links

2. Vault Sync (Plugin/Folder-based)

   • sync topic nodes to Obsidian vault structure
   • maintain bidirectional metadata updates (future)

3. Graph Interop

   • map internal topic links to Obsidian wikilinks
   • preserve identifiers for round-trip sync

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🏗 Suggested Architecture

• Frontend: React / Next.js
• Backend: FastAPI (or Node/Nest)
• Database: PostgreSQL
• Auth: Email/OAuth + JWT/session
• Jobs: Celery / BullMQ / cron workers for daily planning
• Billing: Stripe
• AI Layer: provider abstraction (OpenAI/Anthropic/etc.)
• Graph Engine: topic-relation service + cached graph payloads

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🛣 Roadmap

Phase 1 — MVP

• User accounts
• Topic + exam flows
• Scheduler engine
• Daily dashboard

Phase 2 — Freemium Foundation

• Subscription gating
• Usage limits
• Feature flags for premium AI

Phase 3 — AI Premium

• AI daily coaching
• AI weak-area analysis
• AI adaptive revision plans

Phase 4 — Graph Experience

• Obsidian-style graph
• filters + urgency overlays
• topic relationship editing

Phase 5 — Obsidian Integration

• markdown export
• vault-compatible link mapping
• optional plugin path

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🔐 Privacy & Safety

• User-owned learning data
• Transparent AI usage logs
• Configurable retention for AI prompts/results
• Secure storage for subscription and account data

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✅ Success Metrics

• Daily active study completion rate
• Revision consistency (streak + due-topic completion)
• Exam readiness score trend
• Premium conversion for AI features
• Retention after first exam cycle

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📌 Status

Planning and architecture phase.
Implementation will prioritize scheduler quality, then monetization, then AI and graph expansion.