PrismLMS: AI-Powered Learning, End-to-End
An LMS that generates its own supporting material. Upload a chapter and get a quiz, flashcards, a mind map, and a summary back in under a minute, streamed as they finish.
~60s
Generation time
4
AI artifacts per chapter
5
Parallel LLM executions
WhatsApp messaging gets complicated at scale.
Traditional LMS platforms rely on static content delivery. A course is a set of files: PDFs to read, videos to watch, and a quiz someone wrote by hand. Learners move through it passively, and nothing converts what they read into something they practice against. The supporting material is where it breaks. Quizzes, flashcards, mind maps, and summaries are what move a learner from reading to recall, and each one is authored manually, chapter by chapter. The work is slow, it scales with course size rather than independently of it, and it is the first thing cut when a course ships under deadline. Two structural gaps compounded that. Course content arrives in several formats with no unified system to synthesize knowledge across them, so a PDF and a video covering the same material stay disconnected. And complex course structures had no intuitive management layer, which pushed the authoring burden back onto admins who were already the constraint. Underneath both sat the same absence. Without real-time feedback or any adaptive mechanism, the platform could not respond to how a learner was actually doing.
Challenge 1
Static content leads to passive learning experiences
Challenge 2
Manual creation of quizzes and flashcards is time-consuming and unscalable
Challenge 3
No unified system to synthesize knowledge from multiple content formats
Challenge 4
Complex course structures lack intuitive management and UX
Challenge 5
No real-time feedback or adaptive learning mechanisms
Structuring knowledge before generation significantly improves AI output quality
Streaming partial results enhances user experience compared to batch processing
Multi-LLM support provides flexibility in cost and performance
Separating generation from publishing ensures quality control in AI-driven systems
Built with technologies we trust.
Project Goals
The Outcome
We built a multi-tenant SaaS LMS with two front ends, one for admins who build courses and one for students who take them, and put AI generation inside the authoring workflow rather than alongside it as a separate tool. An admin uploads a chapter. The platform extracts structured knowledge from it, then generates four artifacts against that knowledge: a quiz, flashcards, a mind map, and a summary. Five LLM calls run in parallel, and the set completes in roughly 30 to 60 seconds. Results stream into the UI as each one lands rather than appearing together at the end, so the admin watches the chapter fill in. Manual effort for supporting content went to zero. Generation stays separate from publishing, so nothing reaches a student until an admin has reviewed it. The pipeline processes chapters of 400,000 characters and more. It runs against OpenAI GPT-4.1 or a local Llama 3.1 through Ollama, selected per deployment rather than fixed in code. Built in three months.
Conclusion & Impact
"The finding that mattered was not that a model can write a quiz. It was that output quality tracked how well the input had been structured. Extracting knowledge first and generating second produced better and more consistent artifacts than generating straight from the source material, and that ordering ended up shaping the whole pipeline. Streaming changed the perception of speed more than any optimization did. The same 30 to 60 seconds reads differently when partial results appear throughout instead of arriving in one batch. Abstracting the LLM provider paid off twice. A deployment can choose a hosted frontier model or a local one on cost or data-residency grounds, and the generation code does not change either way. The last decision was the least technical and the one we would keep first: generation is separate from publishing. AI drafts, a person approves, and only then does a student see it. That boundary is what makes an AI-driven system safe to put in front of learners."
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Bring up the problem others won't attempt.