Roommate App

The conflict was never really about dishes in the sink. Research revealed that roommate friction starts long before move-in, in the gap between what people expect and what they never thought to say. AI-assisted prototyping helped us close that gap faster, turning research findings into high-fidelity concepts that four domain experts helped pressure-test.

Client

HCI Design Foundations

Roommate Finding

My Role

UX Researcher

Mixed Methods

Duration

5 months

2025

Inputs

Research Design

Research Design

Ethnographic Study
Interviews
Interviews
Heuristic Evaluations

I conducted in-person observations and took field notes in online communities to round out the data from interviews and surveys; developing a rich understanding of behaviors across cultures.

Synthesis

Synthesis

journey mapping
thematic analysis
thematic analysis
thematic analysis
personas
personas

I leveraged AI synthesis tools to map themes across users, uncovering a key insight that mismatched expectations influence roommate conflict.

Influence

product requirements
feature priorities

In conversation with industry experts, I chose to prioritize the matching features of the app over geographic features. Evaluations showed a critical need for users to have long-term understanding of their living choices.

Insight

When looking for roommates, users desire a deeper understanding of the person they might live with, but the real differentiator is in the app's ability to guide users through what it would look like to live with someone long-term before the lease is signed.

Insight

When looking for roommates, users desire a deeper understanding of the person they might live with, but the real differentiator is in the app's ability to guide users through what it would look like to live with someone long-term before the lease is signed.

OUTCOME

The "Side" Mobile app focuses on user self-discovery; turning traits into actionable scores to help filter potential matches with. The future design of the system would include augmented lease concepts to help users identify healthy matches for the long-term.

full case study → on request