ACT/SAT
Exam Preparation Platform
AI-driven RAG search system with intelligent QBank generator for adaptive ACT/SAT learning, serving 125K+ students with personalized practice paths and achieving 15% average score improvement.
Leading test prep provider • 12-month project • $2.8M budget • 28-person team
Business Problem
PrepMaster EdTech faced limitations with their traditional question bank system that resulted in inefficient study patterns and suboptimal score improvements for students preparing for standardized tests.
Organization
Leading Test Prep Provider
Students Served
125K+ Active Learners
Infrastructure
AWS Cloud with RAG
Project Scope
$2.8M, 12 Months, 28 Team
Critical Business Challenges
Static question banks couldn't adapt to individual student weaknesses and strengths
Students wasted time on questions they already mastered or weren't ready for
Manual question search took 3-5 minutes per query across 50K+ questions
No intelligent recommendations for similar problems based on difficulty and topic
Limited ability to generate targeted practice sets for specific skill gaps
Slow content creation process requiring weeks to build new practice materials
Key Challenges
The legacy system created significant barriers to effective exam preparation and personalized learning experiences.
Inefficient Question Discovery
Students spent 25% of study time searching for relevant practice questions instead of actually learning, with poor keyword-based search returning irrelevant results.
Impact: Reduced effective study time and student frustration
Static Question Banks
50K+ questions organized by simple tags without understanding semantic relationships, difficulty progression, or prerequisite skills required for each question.
Impact: Students practicing wrong-level questions, hurting confidence
Slow Content Creation
Subject matter experts spent 3-4 weeks manually curating practice sets for each skill level and test section, limiting ability to respond to student needs quickly.
Impact: Delayed course updates and limited personalization
One-Size-Fits-All Learning
All students received the same practice questions regardless of their skill level, learning pace, or specific weaknesses in particular topics or question types.
Impact: 68% of students felt materials were too easy or too hard
No Adaptive Difficulty
System couldn't automatically adjust question difficulty based on student performance, leading to boredom for advanced students and overwhelm for struggling learners.
Impact: 22% dropout rate and poor score improvement trajectory
Limited Analytics
Lacked insights into which question types students struggled with most, preventing targeted intervention and personalized learning path recommendations.
Impact: Missed opportunities for early intervention and support
Our Solution
We architected an AI-driven exam preparation platform combining RAG search, intelligent content generation, and adaptive learning to transform the student experience.
RAG-Powered Semantic Search
Implemented retrieval-augmented generation with OpenAI embeddings and Qdrant vector database to enable natural language question search with 95%+ relevance accuracy.
Intelligent QBank Generator
Built AI system using GPT-4 to automatically generate adaptive practice sets based on student performance, skill gaps, and learning velocity with difficulty progression.
Adaptive Learning Paths
Created ML model to analyze student responses in real-time and dynamically adjust question difficulty, topics, and pacing for personalized exam preparation.
Real-Time Analytics Engine
Developed comprehensive analytics platform to track student progress, identify weak areas, and provide actionable insights for both students and instructors.
Content Enrichment Pipeline
Automated the enrichment of 50K+ existing questions with metadata, difficulty ratings, prerequisite skills, and learning objectives using NLP and subject matter expert validation.
Performance Prediction Model
Built predictive analytics to forecast student exam scores based on practice patterns, helping identify at-risk students early and recommend targeted interventions.
Measurable Results
The AI-driven platform delivered significant improvements in development speed, student outcomes, and operational efficiency.
Faster Development
AI-assisted content creation reduced development time from weeks to days
Score Improvement
Average SAT/ACT score increase compared to traditional prep methods
Active Students
Concurrent users with <200ms search response times at peak load
Practice Efficiency
Students complete relevant practice questions 3x faster with RAG search
Time Savings
Reduction in manual content curation time for instructors and SMEs
Satisfaction Rate
Student satisfaction with personalized learning paths and recommendations
Technical Architecture
Modern cloud-native architecture combining AI services, vector search, and scalable infrastructure for enterprise-grade performance.
Frontend & API Layer
- React 18 with TypeScript for responsive UI
- Next.js 14 for SEO-optimized pages
- Spring Boot REST APIs with JWT auth
- Redis caching for session management
AI & Search Layer
- OpenAI GPT-4 for content generation
- Qdrant vector DB for semantic search
- LangChain for RAG orchestration
- FastAPI microservices for ML models
Data & Analytics
- PostgreSQL for relational data
- MongoDB for unstructured content
- ELK Stack for real-time analytics
- Apache Spark for batch processing
Infrastructure
- AWS EKS for container orchestration
- CloudFront CDN for global delivery
- S3 for media storage and backups
- GitHub Actions CI/CD pipeline
Business Impact
The AI-driven platform transformation delivered measurable business value and competitive advantages in the test prep market.
Revenue Growth
Additional annual revenue from premium AI-powered features and increased student retention
User Growth
Increase in active users within 6 months of AI features launch
Completion Rate
Course completion rate up from 52% with personalized learning paths
Market Position
Ranking in competitive test prep market within first year
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