Overview
What this challenge is about.
You will plan and run a 5-day remote co-design study with eight pilot users (a mix of plant operators and middle managers). Sessions 1-2: discover where trust breaks down. Sessions 3-4: co-sketch the trust cues with users (paper or FigJam). Sessions 5: validate the high-fidelity Figma prototype. Output a prototype that surfaces source citations, answer confidence, and a 'disagree' affordance; plus a 5-page study report with verbatim quotes, design decisions, and three open questions for the engineering team.
The Brief
What you'll do, and what you'll demonstrate.
Design and validate a trust layer for an enterprise RAG assistant so pilot users stop double-checking every answer.
Earning criteria — what you'll demonstrate
- Plan and facilitate a co-design study with non-designer participants
- Translate trust-and-transparency research into concrete interaction patterns
- Design for AI uncertainty without overwhelming non-technical users
- Communicate qualitative findings persuasively to engineering teams
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
Owning a co-design study for a high-stakes AI surface and translating it into a defensible prototype is core AI product designer work at any consultancy or in-house AI team.
This challenge sharpens
- co-design
- rag-interaction-design
- figma-prototyping
AI Product Manager
Running the study, synthesizing it, and writing the report for engineering mirrors the AI PM's job at any enterprise-AI vendor.
This challenge sharpens
- user-research
- trust-and-transparency
- human-ai-interaction
AI Solutions Architect
Knowing how trust cues land with pilot users gives an emerging solutions architect a credible voice in the citation/grounding choices baked into a RAG architecture.
This challenge sharpens
- trust-and-transparency
- rag-interaction-design
- human-ai-interaction