Problem Space:  Educators and families struggled to:

• Trust and evaluate AI outputs

• Integrate AI into learning environments

• Define boundaries for safe and responsible use

Existing AI learning materials were:

• Too technical

• Not grounded in real classroom or community contexts

• Missing guidance on how to use AI responsibly

My Role:

• Led end-to-end UX research and participatory design

• Designed and facilitated two co-design workshops (18 educators + parents)

• Owned research → synthesis → design translation → program implementation

Process: Three phases of data collection: 

1. Semi-structured Interviews with informal and formal educators (n=18).  

2. Two consecutive co-design workshops with educators and parents (n=18). 

3. Two-week critical AI literacy program for youth, and two workshops with their caregivers. 

Problem Space: Public transit systems rely on operational metrics (e.g., on-time performance) that fail to reflect real user experiences, such as reliability, safety, accessibility, and transfers. As a result:

• Critical user pain points remain invisible

• Communities lack tools to advocate effectively

• Decision-making is disconnected from lived experience

My role: 

I co-designed and implemented Community Data Labs (CDLs)—a participatory, human-centered system that enables communities to collect, interpret, and act on their own data in collaboration with policymakers.

CDLs function as the experience and decision-making layer of a broader data ecosystem, integrating:

• Mobile app to capture real-time transit experiences

• Dashboard to aggregate and visualize community data

• Facilitated UX layer (CDLs) to support interpretation, sensemaking, and action