ALIGNED
MY ROLE
Research, System Design, AI Agent Build, Usability Testing (Solo)
TECHNOLOGIES
Figma, Python, AI/ML, GitHub
TIMELINE
August 2025 – Present
MS THESIS
Interface as a tool for physical and digital synchrony
A design system that builds your phone around you, not the other way around. ALIGNED surfaces cross-app usage patterns and helps users create custom interfaces through AI collaboration — making the phone fit the person without asking the person to become someone else.
The Problem
Phones are built around apps, not the people using them. The data you need lives across apps in silos — a timer doesn't know the bus schedule, a calendar doesn't know the kiln is almost done — leaving you to do the mental work of connecting it all day.
Widgets were meant to close that gap. They didn't. Official ones are limited and depend on a company building them; third-party ones break consistency and trust. Both share one flaw: they're built by someone other than the person using them.
“The core frustration is functional mismatch, not aesthetics. People don't need prettier widgets — they need interfaces that fit how their lives are actually structured.”
Problem Statement
How can mobile interfaces move beyond static, app-defined layouts to become adaptive systems that reflect the user's actual habits, context, and intent — without offloading the design to someone who doesn't live their life?
Scoping the Space
Two tracks in parallel, starting August. Field track — three methods, twelve participants, twenty-four surveyed: semi-structured interviews (how people use quick-access interfaces and why the gap persists), activity-based workshops (participants built a widget for a real daily need while thinking aloud), and surveys to quantify effort, confidence, and discoverability. Literature track: scoped across UX-AI/ML, human-AI co-design, cognitive load, and mobile interface constraints — grounding decisions in evidence, not intuition.
What I Found
The frustration is functional mismatch, not aesthetics — and the numbers are decisive.
Underneath the numbers, the qualitative pattern was four problems that form a sequence, not a list. Each one feeds the next, and the current system never interrupts the cycle.
“Users weren't passive in their frustration — they adapted around the limits and carried the coordination cost mentally. ALIGNED isn't introducing a need. It's meeting one they'd already been absorbing.”
The System
ALIGNED interrupts the cycle at the start. Instead of “pick a widget from this library,” it asks: where is your setup costing you effort, and what could a custom interface do instead?
Identify
The user brings their own screen-time / usage data; the system surfaces cross-app patterns worth consolidating, so they don't have to diagnose the problem themselves. Privacy by design: nothing is sensed passively.
Collaborate
Through whatever input feels natural (sketch, description, screenshot), the user directs an AI agent to generate an interface built around their actual habits, not a template.
Implement
The user decides whether it goes on their phone. Nothing is added without a deliberate choice. What they build gets saved and maintained as habits evolve.
Role of AI
Collaborator, not decision-maker. It enters at exactly two points: generating a visual prototype from usage patterns + input, and translating the approved design into code. Everything else stays with the user. AI reduces the cost of a process the user is already leading.
What I Built
Mockups and prototyping in Figma across four input modalities; a working AI agent hosted on GitHub, running on desktop, that turns identified patterns + user input into interface prototypes and implementation-ready front-end code.
Four ways to tell the agent what you want — text + sketch combined tested strongest.
Testing & What's Next
Phase 2 sessions ran. Three findings landed.
Input Modality
80%+ preferred combining text + sketch when directing the agent. The more telling signal is which mode people reach for first, revealing their default mental model.
Identification Accuracy
Recognition was high. People aren't surprised by descriptions of their own behavior; they're surprised by features they didn't know existed. The open question is the jump from recognizing a pattern to acting on it.
Implementation
A split. Some moved through smoothly; others stalled, not because the output was wrong but because committing to a layout change carries weight a prototype doesn't. Points toward a low-stakes “trial placement” option.
Next
Move data processing on-device so identification works automatically while keeping privacy intact.
Build a real (non–Wizard-of-Oz) implementation pipeline.
Test whether an intent-driven co-design framework like ALIGNED generalizes beyond the mobile screen to spatial and physical computing interfaces.
“The claim isn't that AI designs a better phone. It's that a system surfacing what you already know about your own life — then helping you act on it — makes the phone fit the person, without asking the person to become someone else to use it.”