2025-2026
Digital wellness built by students, for students
mobile app
digital health
ai
Role
Lead product designer
timeline
June 2025 - August 2025
January 2026 - present
Team
3 Product designers
1 Graphic designer
4 Data engineers
1 Project manager
Additional research, content, & engineering teams
contributions
Product design
Interaction design
Design systems
Visual design
at a glance
Evergreen is a $16.5M, research-driven wellness initiative at Dartmouth. Informed by insights from 100+ students, we are creating an AI-powered mobile platform designed to support everyday student wellbeing through personalized reminders and conversational guidance.
As the lead designer, I’m creating end-to-end experiences across onboarding, chat interface, Explore, and Insights features, while mentoring two other designers and collaborating with engineers, content teams, and institutional stakeholders.
impact
V1 is projected to launch for internal testing in Spring 2026, and has received media coverage from outlets including Forbes and MassLive.
made in collaboration with
students impacted upon release
problem
Busy college life makes wellness hard to prioritize.
College students, especially in Dartmouth’s fast-paced quarter system, juggle demanding coursework, relationships, and extracurricular commitments, often deprioritizing their health and wellbeing.
2024 Dartmouth Health Survey Report
2024
2014
Stress
40%
31%
Anxiety
31%
20%
Depression
24%
12%
Sleep difficulties
30%
24%
factors affecting academic performance
opportunity
AI is creating new opportunities for personalized support.
Researchers are exploring how AI can complement human wellness resources by providing accessible, personalized support outside of traditional counseling settings.
campus resources
counseling
peers
mobile platforms
wearables
human support resources
AI support (24/7)
continuous student support
context
Advancing student wellness through a multi-year collaboration
Multiple teams are working across dialogue design, AI model development, mobile engineering, research, and UX to build a campus-specific wellness platform. My team and I are designing the student-facing mobile experience.
what we’re doing
We’re building a wellness tool designed specifically for Dartmouth students.
Evergreen is designed for Dartmouth’s culture and pace. Instead of replacing counseling services, it supports students in everyday moments, helping them reflect, build habits, and manage stress early through timely interventions.
solution
Data patterns turned into personalized suggestions
Evergreen integrates passive sensing data from a student’s everyday activities, schedule, and voluntary information like sleep, mood, or steps to provide behavior and schedule-based suggestions.
solution
Guided conversations that match student needs
Conversations are designed to feel personal and relevant to campus life, and are written by a team of 100+ Dartmouth undergraduates. Topics range from building healthy habits to making better connections on campus.
solution
Integrated chat tools for everyday support
Students can track their wellbeing, explore their environment, and manage their time, all in one place. Each chat module is designed to be flexible, interactive, and integrated into the chat experience.
2:26pm
Evie
Jan 01, 2026
1:26
100
Drag to select your mood
Confirm your mood
Start Timer
60:00
seconds
Minutes
Drag around the Focus Fookie to set a timer
solution
Reflect on daily habits over time
The app visualizes trends in mood, sleep, and steps, turning raw data into actionable insights to spot patterns and track progress.
Wait. How did we get here?
process
A foundation of research at Dartmouth
Researchers at Dartmouth have explored how mobile sensing, AI, and behavioral science can support mental health. Studies have shown that smartphones can capture behavioral signals like sleep, activity, and daily routines, to better understand student wellbeing.
research to concept
What if a campus-specific app could proactively support student wellbeing?
Building on the idea of behavioral sensing, we wondered if a campus-specific AI platform could help students reflect on their habits and receive supportive guidance informed by their data.
understanding users
Focus groups explored how AI could support student wellness.
Behavioral researchers conducted focus groups with Dartmouth students to understand attitudes toward AI and wellness support. Due to human-subject research constraints, we didn’t have access to full transcripts, but we received quotes and high-level insights.
Key insights
There were so many suggestions and perspectives. We learned that overall, students value AI support that feels convenient, personalized, and well integrated into their everyday life. These informed our early design explorations and discussions on product direction.
Students want to see patterns, not just raw data
Students were interested in learning insights about their behavior, instead of just seeing metrics
Support should fit naturally into daily routines
Students preferred tools that integrate into their existing habits rather than requiring additional effort
AI should be transparent and user-controlled
Students expressed interest in AI features, but emphasized clarity and control over how their data is used
design explorations
Early chat and module designs
A chat interface felt natural for introducing dialogues in the form of conversations. My team and I created early concepts and simple prototypes for the interface and supporting modules, testing different approaches with students.
While initial designs established functionality, they lacked polish and cohesion. I led a redesign of the entire UI and design system, improving consistency and aligning visual language with Dartmouth’s environmental color palette.
chat experience
Guiding students through conversation flows
Evie, Evergreen’s conversational assistant, interacts with students through guided dialogues. Designing the chat experience required balancing two goals: maintaining safe, research-backed conversations while enabling natural interactions.
Version 1: Structured conversations
Evergreen’s first release uses a structured dialogue system with selection chips to ensure conversations are reliable, safe, and aligned with research goals during early testing.
How conversations start
Passive sensing signals
Student selects a dialogue topic
How responses work
A pre-written dialogue tree curated by student dialogue writers
Student selects a response chip option or inputs an occasional free response to continue conversation flow
Why this approach
Safe version for initial testing in a fall 2026 research trial
Version 2: Generative, context-aware conversations
As the engineering team fine-tunes a generative LLM, future versions will have generative capabilities, where Evie can respond more flexibly and contextually to student messages.
How conversations start
Passive sensing signals
Student initiates a free text message
Student selects a dialogue topic
How responses work
Dynamic real-time responses based on context
Student replies freely with their own input
Why this approach
More natural conversations and greater flexibility for students
explore page
An entry point to start meaningful conversations
The Explore page allows students to browse and start guided conversations on a variety of topics like study habits, social connections, and digital wellness.
As the project evolved, expanding dialogue content and shifting directions required rethinking both the structure and visual design. I redesigned the page to improve clarity, support a fast growing conversation library, and introduce more expressive visual language with category-specific characters and card designs.
The dialogue team so far has produced 220+ peer-reviewed conversations, spanning across multiple wellbeing topics.
insights page
Helping students reflect on patterns
The Insights page visualizes passive sensing and self-reported data, helping students understand patterns over time.
Guided by research showing that students prefer patterns over raw data, we moved beyond simple wellness scores to richer visualizations that support the specific data types being collected. I collaborated with another designer to explore ways to visualize mood, integrate passive sensing data, and show correlations of different metrics through time-based graphs.
Reflections
What I’ve learned
Systems thinking is critical
The largest project ecosystem I’ve worked in yet! Collaborating with content and data teams to know what and how information is accessed helped clarify the system and kept our designs realistic
Constraints are part of the process
We’re balancing research trial limitations and changing product directions, while advocating for the needs of a real student population. It adds complexity, but makes our work grounded.
Your first idea isn’t your best
We went through dozens of iterations over months, from design system elements and colors to chat UX and page layouts, refining every detail more and more along the way
Push for the work you believe in
I learned to really advocate for the features I believed in, and how to best justify my decisions to stakeholders and a non-design audience
Looking ahead
Continuing towards a V1 launch
We’re continuing to refine core features as we move toward an initial limited release in Spring 2026. I’m so grateful to be building alongside an incredible, supportive team!
Most of the DALI design + development team (+ Michael) at Technigala, Dartmouth’s quarterly tech showcase, March 2026
Want to learn more about this project? I’m happy to chat!
Reach out to me at rachael.huang.27@dartmouth.edu
Next project
Deserto
Removing barriers to finding campus events, so students can connect to what matters
in progress designs
