Education & Platforms

EdTech Curriculum Engine

The shift from static lectures to adaptive learning exposed a critical flaw in the client's infrastructure: content was siloed, and student progress data was fragmented.

We built a unified learning engine that treats curriculum as a flexible data graph, allowing for personalized learning paths and real-time intervention for struggling students.

Project Manifest

Role
Strategy + UX + Dev
Scope
Platform Build
Timeline
5 Months
Tech Stack
React / Next.js
GraphQL API
AWS MediaConvert
LTI 1.3 Standards
Abstract visualization of adaptive learning paths and curriculum data nodes
01

The Constraints

  • Hybrid Delivery

    The system had to support live-streamed classes and asynchronous self-paced modules simultaneously.

  • Legacy Content

    Migrating 5,000+ hours of video content from a static file server to an adaptive streaming architecture.

  • Assessment Logic

    Tests needed to be dynamic; questions should adapt based on the student's previous answers (IRT).

02

The Approach

Modular Architecture

We broke the monolithic course structure into atomic "Learning Objects." This allowed instructors to remix content into new degree programs without duplication.

Adaptive Video

Implemented AWS MediaConvert to transcode legacy video into adaptive bitrate streams (HLS), ensuring smooth playback even on 3G connections in remote areas.

Unified Student Profile

Created a single "Truth Source" for student progress. Whether they watched a video, took a quiz, or attended a live session, it all fed into one progress bar.

Instructor Dashboard

Designed a "Red Flag" system for teachers. Instead of reviewing all grades, they get alerts for students who are falling behind the cohort average.

03

The Outcome

25%

Increase in student course completion rates.

2 New

Degree programs launched using existing content assets.

40%

Reduction in streaming bandwidth costs via optimized encoding.

Real-time

Intervention alerts for at-risk students.