How might campus transit adapt to student wellness without becoming surveillance? Ran co-design workshops and prototyped an app grounded in the key finding: people want systems that adapt for groups, not individuals.
Timeline
4 Weeks, Fall 2025
Team
3 People
My role
UX Design & Prototyping, Futures Scenario Development, Workshop Documentation & Synthesis
Wearable health data is becoming mainstream, sleep scores, stress monitoring, heart rate variability. We asked what happens when this personal data intersects with public infrastructure like campus transit. Can mobility systems adapt to how students feel without creating surveillance?
We used futures thinking to develop two contrasting 2050 scenarios: one where wellness-integrated transit supports collective care, one where it becomes an optimization trap, then backcasted to design what needs to exist in 2027.
We ran three rounds of participatory workshops with students, faculty, and practitioners. Four activities moved participants from individual reflection to collective future-building: mood check-ins, happy/frustrated commute moments, pick-a-path co-design, and future postcards. Here are the key findings:
1. Utility first, wellness second. Students are always rushing. "Will I make it to class?" matters more than "Am I stressed?" Personalization must be optional.
2. Comfort is micro-sensory. Itchy noses from vibrations, bad smells, losing balance on turns. Comfort goes beyond seats and temperature.
3. Uncertainty creates cognitive load. "I just want to know where to go." Stress comes from confusing signs and not knowing if you're heading the right way.
4. Social features must be opt-in. People liked low-pressure connection but didn't want to feel watched.
5. Collective adaptation over personal optimization. The strongest finding. Participants consistently designed systems that adapt for groups — better air filtration for everyone, reduced wait times across the network — and pushed back against individual profiling.
AuraRoute offers personalized route recommendations while working fully for users who share no data.
Core features include preference-based onboarding with accessibility settings, a skippable daily state check-in, intelligent route recommendations with match percentages and one-line reasoning, multimodal options (bus, bike, walk, hybrid), live tracking with real-time updates, and accessibility throughout, voice guidance, haptic alerts, quiet zones, air quality monitoring.
Every major iteration traced back to a specific finding:
Students are always rushing → Made check-in skippable, added "In a rush" and "No preference" options, ensured the app works with zero personalization data.
Buses smell bad sometimes → Added "Fresh air system" tags to route cards and air quality to accessibility preferences.
I just want to know where to go → Set voice guidance to ON by default with real-time announcements at every step.
We want systems for groups, not individuals → Added community-level insights ("Popular with 200+ students who prefer calm commutes"), crowd distribution messaging that optimizes for collective benefit, and environmental adaptation indicators on the live tracking screen ("Lighting adjusted for morning commuters · Air filtration active").
Participatory design reveals what surveys can't. Being in the room, watching people gesture about losing balance, hearing "I just want to know where to go", surfaced insights no questionnaire would capture.
People resist being optimized. Participants rejected individual profiling and designed for collective adaptation instead. This challenges the default "personalization" narrative in tech and shaped every design decision we made.
