AI-Powered Lesson Planning

AI Lesson Plan Generator

Generate structured, standards-aligned lesson plans with citations from your uploaded materials using RAG and OpenAI

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Citation-backed
RAG-Powered
GPT-4 Generation
OpenAI LLM
JSON Schema
Structured Output

Feature Overview

Core features for AI-powered lesson plan generation with RAG

Must-Have

  • Grade level selection (K-12 + higher-ed)
  • Topic/Standard/Objective text inputs
  • Optional file upload (PDF/DOCX) for context
  • Standards framework selection (CCSS, TEKS, etc.)
  • Exemplar template selection (PBL, 5E, UDL, Direct)
  • Structured lesson plan output with citations
  • Generation history and reuse capabilities

Nice-to-Have (Later)

  • Multi-language support
  • Collaborative editing
  • Calendar integration
  • Assessment rubric generation
  • Student handout creation
  • Parent communication templates
  • Batch generation API

🎯Primary Users

Teachers & Instructional CoachesCurriculum DesignersDistrict AdministratorsEdTech Platforms

System Architecture

React + FastAPI + RAG + OpenAI for intelligent lesson plan generation

React Frontend

Lesson plan form, file upload, results viewer, history page, export controls

React 18TypeScriptTailwind CSSReact Query

FastAPI Backend

Auth, generation orchestrator, RAG retrieval, prompt engineering, output validation

FastAPIPython 3.11+PydanticOpenAI SDK

Data Layer

User/file/document metadata, chunks, generation history, citations tracking

PostgreSQLQdrant/pgvectorS3/Azure Blob

LLM Integration

OpenAI GPT-4 for generation, structured JSON output with function calling

OpenAI APIEmbeddingsFunction Calling

RAG Pipeline Design

Four-stage retrieval-augmented generation for citation-backed lesson plans

Stage 1

Document Ingestion

Upload PDFs/DOCX (curriculum guides, standards, prior lessons) → extract text → chunk (400-800 tokens, 50-100 overlap)

PDF/DOCX parsingConfigurable chunkingMetadata extractionStorage in PostgreSQL
Stage 2

Embedding Generation

Generate vector embeddings for each chunk using OpenAI text-embedding-3-small → store in Qdrant or pgvector

OpenAI embeddingsVector storageMetadata filteringFast retrieval
Stage 3

Semantic Retrieval

Teacher query (grade + topic + criteria) → vector search → top-k chunks → optional reranking → context for LLM

Vector similarity searchMetadata filteringReranking (optional)Score thresholding
Stage 4

Citation Tracking

Track which chunks were used → include source labels (filename, page) → display citations in UI alongside lesson plan

Source trackingPage referencesChunk attributionGrounding verification

RAG Best Practices

  • •Chunk size: 400-800 tokens with 50-100 token overlap
  • •Metadata filtering: grade band, standards framework, workspace
  • •Optional reranking for improved relevance quality
  • •Guardrails: avoid hallucinated standards codes

Structured Output Contract

JSON schema for consistent, citation-backed lesson plan generation

Core Lesson Structure

  • •Title, grade level, duration
  • •Standards alignment (codes + descriptions)
  • •Learning objectives
  • •Materials and vocabulary
  • •Lesson sequence (Hook, Instruction, Practice, Assessment)

Differentiation & Support

  • •Struggling learners strategies
  • •Advanced learners extensions
  • •ELL supports and scaffolds
  • •IEP/504 accommodations

Assessment & Extension

  • •Exit ticket prompts
  • •Formative assessment checkpoints
  • •Homework or extension activities
  • •Success criteria rubrics

Citations & Metadata

  • •Source references (filename, page)
  • •Chunk attribution IDs
  • •Generation timestamp and model
  • •Prompt metadata for reproducibility

Example JSON Output

{
  "title": "Introduction to Photosynthesis",
  "grade_level": "4th grade",
  "duration_minutes": 45,
  "standards": [{"code": "NGSS 5-LS1-1", "description": "..."}],
  "learning_objectives": ["Students will explain..."],
  "materials": ["Plant specimens", "Chart paper", ...],
  "vocabulary": [{"term": "chlorophyll", "definition": "..."}],
  "lesson_sequence": [
    {"phase": "Hook", "minutes": 5, "teacher_actions": [...], ...},
    {"phase": "Instruction", "minutes": 15, ...}
  ],
  "differentiation": {
    "struggling_learners": [...],
    "advanced_learners": [...],
    "ell_supports": [...],
    "iep_504": [...]
  },
  "citations": [
    {"source": "District Science Guide", "page": 12, "chunk_id": "..."}
  ]
}

Key Benefits

Empower teachers with AI-assisted planning while maintaining pedagogical quality

Time Savings

Generate comprehensive lesson plans in minutes instead of hours, with all required components

Standards Alignment

Automatic alignment to district/state standards with citation-backed recommendations

Differentiation Built-In

Generate targeted supports for struggling learners, ELL students, and advanced learners

Citation Grounding

Every suggestion backed by source materials with page references for verification

Continuous Improvement

Learn from generation history, reuse templates, and refine over time

Instant Iteration

Quickly regenerate with different criteria or exemplars until perfect

Ready to Transform Lesson Planning?

Join educators who are saving hours each week while creating better, more personalized lesson plans with AI-powered RAG technology.

95% Time Reduction100% Citation-BackedStandards-Aligned

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