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Letting Data Speak, AI Act!
Case Study
AI Driven Course Curriculum Map Generation and Searching System
About the Client
A fully accredited, nonprofit medical school training physicians for practice in the United States and Canada. The institution positions itself as an exceptional alternative for qualified students who face limited medical school seats in North America. The client intended to utilize Generative AI technologies on Amazon Web Services (AWS) to implement an AI-driven system for ingesting IMSCC (IMS Common Cartridge) files and automatically generate a course curriculum map aligned to the AAMC framework.
Challenge
The client faced a time-intensive and inconsistent manual process for mapping course content to the AAMC (Association of American Medical Colleges) competency framework. With 22 courses and IMSCC files containing heterogeneous content — HTML pages, PDFs, PowerPoint slides, QTI assessments, and images — manual mapping was both error-prone and unscalable. Some IMSCC files exceeded 1.3 GB in size with 200+ resources, requiring processing times far beyond what Lambda's 15-minute execution limit could accommodate. The platform also lacked a searchable, structured store for generated curriculum maps and had no mechanism for aligning content across courses to surface prerequisite dependencies.
Key Results
Delivered a fully automated, end-to-end curriculum mapping pipeline processing 22+ courses from raw IMSCC files to structured, AAMC-aligned curriculum maps
Achieved intelligent AAMC competency alignment across all four domains: Interpersonal, Intrapersonal, Thinking and Reasoning, and Science Competencies
Solved large-file processing challenge — files up to 1.3 GB processed within Lambda's 15-minute timeout using dynamic batch sizing and Step Functions orchestration
Implemented prerequisite-aware module sequencing via two-pass dependency analysis, ensuring correct course ordering in final curriculum maps
Deployed hybrid semantic and keyword search API over all processed curriculum content, accessible via API Gateway
Built automatic model fallback from Claude Sonnet 4.5 to Claude Sonnet 4 when daily Bedrock token quotas are reached, ensuring pipeline continuity
Delivered full infrastructure-as-code using Terraform across 50+ AWS resources for repeatable, multi-environment deployment
Solution
Event-Driven Batch Processing Pipeline
Built a fully serverless, event-driven pipeline on AWS that processes entire cohorts of courses once to generate structured curriculum maps — not a chatbot or RAG system
IMSCC files uploaded to S3 automatically trigger the pipeline after a 10-minute debounce window, ensuring all cohort files are present before processing begins
Content Extraction and Hierarchical Chunking
Unpacked and parsed IMSCC files across all content types: HTML, PDF, PPTX, DOCX, QTI/XML, and images (via Claude vision OCR)
Applied hierarchical chunking at 800 tokens with 100-token overlap across three levels: Course → Module/Unit → Resource/Item
Large PDFs split into 25-page parts; images compressed to reduce Bedrock token usage by 60–70%
Generated Titan Embed Text v2 embeddings (1536 dimensions) per chunk, stored in Aurora PostgreSQL with pgvector
AAMC Competency Mapping and Course Summarization
Claude extracted learning objectives per resource and mapped them to the four AAMC competency domains during chunking
Generated structured JSON course summaries capturing key topics, prerequisite topics, AAMC domain coverage percentages, and module breakdowns
Two-Pass Curriculum Map Generation
Pass 1: Claude built a prerequisite dependency graph by cross-referencing each course's topics against others
Pass 2: Dependency map injected as ordering constraints to generate a correctly sequenced module blueprint
Pass 3: Two full curriculum maps generated from the blueprint — Core (required competencies) and Supplementary (additional learning) — output as Excel and JSON to S3
Hybrid Semantic Search API
Hybrid search combining Titan v2 vector similarity and PostgreSQL ILIKE keyword matching; results appearing in both ranked highest
Claude generates a natural language narrative over top results; exposed via API Gateway GET /search with cohort-level filtering
Step Functions Orchestration for Scale
Step Functions orchestrated the pipeline with parallel Map states — up to 5 courses and 3 batches per course concurrently
ThreadPoolExecutor (3–5 workers) within each Lambda maximized Bedrock API throughput within the 15-minute timeout ceiling
Built-in retries, checkpoint recovery, and idempotent reprocessing ensured resilience across long-running cohort runs
Technologies Used
Amazon Bedrock — Claude Sonnet 4.5 (primary), Claude Sonnet 4 (fallback)
Amazon Aurora PostgreSQL Serverless v2 with pgvector
AWS Step Functions
AWS Lambda (Python 3.12)
Amazon S3
Amazon DynamoDB
Amazon EventBridge and EventBridge Scheduler
API Gateway (HTTP v2)
Amazon Titan Embed Text v2
AWS Secrets Manager
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