Prism-LMS: AI-Powered Learning, End-to-End
A real-time AI-powered LMS that transforms static course content into interactive learning experiences by automatically generating quizzes, flashcards, mind maps, and summaries.
Platform
SaaS LMS — Web (Admin + Student)
Duration
3 Months
~60s
Generation time
4
AI artifacts per chapter
5
Parallel LLM executions
Project overview
Demonstrated that AI can fundamentally transform how learning content is created and consumed. Eliminated manual content creation while maintaining quality.
Platform
SaaS LMS — Web (Admin + Student)
Duration
3 Months
Type
AI & Education
Stack
10 technologies
The challenge
Traditional LMS platforms rely heavily on static content delivery, offering limited engagement and requiring instructors to manually create supporting learning materials.
Static content leads to passive learning experiences
Manual creation of quizzes and flashcards is time-consuming and unscalable
No unified system to synthesize knowledge from multiple content formats
Complex course structures lack intuitive management and UX
No real-time feedback or adaptive learning mechanisms
What we set out to do
- 01
Automate generation of quizzes, flashcards, mind maps, and summaries from course content
- 02
Deliver AI-generated artifacts in real time with streaming UI updates
- 03
Build a scalable multi-tenant LMS architecture
- 04
Provide intuitive course management for admins and structured learning for students
- 05
Support multiple LLM providers for flexibility and cost optimization
How we solved it
Hierarchical Content Architecture
Structured model: Course → Chapter → Chapter Items (PDF, Video, Quiz).
Key decision
Structured hierarchical content model
Result
Scalable and intuitive course management.
Two-Stage AI Generation Pipeline
Pipeline that first extracts structured knowledge from PDFs then generates artifacts from a unified knowledge base.
Key decision
Knowledge-first generation using LangChain
Result
Higher quality and consistent AI outputs.
Parallel LLM Execution
Multiple AI generations triggered simultaneously using parallel execution.
Key decision
Parallel processing using Promise-based execution
Result
Reduced generation time to ~30–60 seconds.
Real-Time Streaming Architecture
Streaming AI outputs using WebSockets and reactive programming.
Key decision
Streaming over batch processing
Result
Improved perceived performance and UX.
Flexible AI Infrastructure
Abstracted LLM providers to support both cloud (OpenAI) and local (Ollama) models.
Key decision
Multi-LLM abstraction layer
Result
Cost optimization and deployment flexibility.
Measurable impact
~30–60s
Total generation time
4
AI-generated artifacts per chapter
0
Manual effort for content creation
70–80%
Estimated learner interaction rate
400K+
Characters processed reliably
Tech stack
What we learned
This project demonstrated that AI can fundamentally transform how learning content is created and consumed. By embedding AI directly into the LMS workflow, we eliminated manual content creation while maintaining quality.
- 01
Structuring knowledge before generation significantly improves AI output quality
- 02
Streaming partial results enhances user experience compared to batch processing
- 03
Multi-LLM support provides flexibility in cost and performance
- 04
Separating generation from publishing ensures quality control in AI-driven systems
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