Case Studies/ACT/SAT Exam Preparation Platform
RAGAI SearchEdTech

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.

PrepMaster EdTech Inc.

Leading test prep provider • 12-month project • $2.8M budget • 28-person team

40%
Development Speed
Faster than traditional
+15%
Score Improvement
Average increase
125K+
Students Served
Active learners
3x
Practice Efficiency
More adaptive

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.

OpenAI EmbeddingsQdrantLangChainFastAPI

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.

GPT-4PythonPostgreSQLRedis

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.

Scikit-learnTensorFlowApache KafkaSpring Boot

Real-Time Analytics Engine

Developed comprehensive analytics platform to track student progress, identify weak areas, and provide actionable insights for both students and instructors.

ELK StackGrafanaApache SparkMongoDB

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.

spaCyBERTAirflowMySQL

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.

XGBoostPythonJupyterAWS SageMaker

Measurable Results

The AI-driven platform delivered significant improvements in development speed, student outcomes, and operational efficiency.

40%

Faster Development

AI-assisted content creation reduced development time from weeks to days

+15%

Score Improvement

Average SAT/ACT score increase compared to traditional prep methods

125K+

Active Students

Concurrent users with <200ms search response times at peak load

3x

Practice Efficiency

Students complete relevant practice questions 3x faster with RAG search

85%

Time Savings

Reduction in manual content curation time for instructors and SMEs

92%

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.

$8.2M

Revenue Growth

Additional annual revenue from premium AI-powered features and increased student retention

185%

User Growth

Increase in active users within 6 months of AI features launch

78%

Completion Rate

Course completion rate up from 52% with personalized learning paths

#2

Market Position

Ranking in competitive test prep market within first year

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