NSGA-II Multi-Objective Design for an Edtech Curriculum Recommender
Overview
What this challenge is about.
You receive a synthetic student cohort (300 students with skill profiles), a content catalog (around 1,200 items with difficulty + duration + topic tags), and a simulator that estimates learning-gain per item per student. Encode a 7-day study plan (up to 60 minutes per day) as a variable-length sequence. Optimize four objectives with NSGA-II: maximize expected learning gain, minimize total time, maximize content variety, minimize variance in daily difficulty. Build an interactive Pareto-front explorer (Streamlit) and write a 2-page memo on which trade-offs to expose in the product.
The Brief
What you'll do, and what you'll demonstrate.
Use NSGA-II to generate a Pareto front of weekly study plans across four conflicting objectives and produce a product-ready trade-off explorer.
Earning criteria — what you'll demonstrate
- Implement NSGA-II for a real multi-objective design problem
- Quantify Pareto-front quality with hypervolume and spacing metrics
- Translate Pareto trade-offs into product UI choices
- Communicate multi-objective trade-offs to product and pedagogy leadership
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
Careers
Roles this prepares you for.
Real titles. Real skill bridges. Pick the one closest to your trajectory.
Career paths this builds toward
Canonical rolesAI Product Designer
Designing a UI on top of a Pareto front and writing the trade-off memo product needs to ship is exactly the day-one work of an AI product designer at an edtech-AI startup.
This challenge sharpens
- product-design
- streamlit
- multi-objective-optimization
AI Product Manager
Translating four conflicting objectives into a product narrative parents and pedagogy can accept is core AI PM work.
This challenge sharpens
- multi-objective-optimization
- product-design
- metaheuristics
Applied AI Scientist
Owning NSGA-II implementation plus Pareto-front quality metrics is core applied-AI-scientist work in any optimization-heavy product team.
This challenge sharpens
- nsga-ii
- evolutionary-algorithms
- multi-objective-optimization