Design a Trust-Calibration UI for a Domestic Assistive Robot
Overview
What this challenge is about.
You will write the interaction patterns first (in a Notion brief), build a paper or Figma prototype, run 4 study sessions in week 2, iterate, and re-test in week 3. The interaction should let the robot say things like 'I think this is your pill bottle, want me to bring it?' instead of silently grabbing or refusing. Deliver: the interaction-pattern spec, the prototype, a study report with quotes per participant, and a one-page rollout recommendation for the product team.
The Brief
What you'll do, and what you'll demonstrate.
Design and validate a trust-calibration interaction layer that helps older adults delegate the right tasks to an assistive robot.
Earning criteria — what you'll demonstrate
- Design interaction patterns that calibrate user trust in physical AI
- Run iterative paper-prototype studies with older-adult participants
- Apply inclusive-design heuristics to embodied AI interfaces
- Translate qualitative findings into a rollout recommendation
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.
AI Product Designer
Designing for trust calibration on an embodied AI is the work AI product designers do at any consumer-robotics company.
This challenge sharpens
- trust-calibration
- interaction-design
- figma-prototyping
AI Safety Researcher
Iterative evaluation of an interaction layer for over- and under-trust failure modes mirrors the safety researcher's daily work on deployed AI.
This challenge sharpens
- trust-calibration
- user-study
- human-robot-interaction
AI Product Manager
Owning the rollout recommendation backed by qualitative evidence is core AI PM work at consumer-AI startups.
This challenge sharpens
- user-study
- interaction-design
- inclusive-design