Luna Onboarding
- An AI Case Study
Luna is a concept productivity and wellness app designed to help users build and maintain healthier daily routines through lightweight tracking, guided setup, and personalized insights.
Summary:
This case study explores how AI tools can strategically enhance onboarding design—not by automating creativity, but by accelerating iteration, refining microcopy, and identifying friction points earlier in the process. The focus is on human-led decisions amplified by AI capabilities.
40-60% of users abandon wellness app onboarding before completion. The issue isn't complexity—it's that traditional flows front-load decisions without establishing value first. Users arrive motivated but uncertain. If onboarding feels demanding rather than supportive, they leave before experiencing the product's core benefits.
Design an onboarding flow that reduces cognitive load while collecting necessary data for personalization. The experience must demonstrate value early, adapt to different user confidence levels, and feel conversational rather than interrogative—all while accommodating both visual and audio learners across 8-10 essential steps.
AI tools accelerated production but didn't replace design thinking. Each AI output was evaluated against core UX principles:
For example, Midjourney generated 50+ character concepts, but I selected the final direction based on accessibility considerations and emotional resonance with target users.
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Introduce AI companion character (Luna) Addresses:
Abandonment, anxiety, lack of guidanc
Enable conversational UI with voice + text input Addresses:
High fatigue, accessibility needs
Implement smart progress indicators Addresses:
User anxiety about time commitment
How AI Enhanced Research
Pattern Recognition: Used Claude to analyze 30+ onboarding flows, identifying common friction patterns across 14-15+ step experiences
Content Analysis: Employed AI to extract pain points from user reviews across competitor apps
Synthesis: Generated initial opportunity frameworks that were then validated and refined through traditional UX methods
Key Insight: AI helped accelerate pattern recognition, but human judgment was essential for determining which patterns were truly problematic versus industry-specific requirements (like insurance verification in health apps).
I followed a user-centered design approach, moving iteratively through research, ideation, design, and testing phases. Each stage informed the next, ensuring decisions were grounded in user needs and validated through testing.
The design phase transformed conceptual sketches into a living, breathing character. Using a multi-tool AI workflow, I built Luna from the ground up: exploring visual identity, crafting emotional expressions, and bringing her to life through animation and voice.
Explore Character in Midjourney
Create character variations
Create character variations
Design Decisions Behind Luna
Why this character form?
Moon-inspired design: Connects to nighttime wellness routines and calm evening reflection
Soft, rounded shapes: Psychologically associated with approachability and safety
Minimal facial details: Allows users to project their own emotions; avoids uncanny valley
Consistent color palette: Purple/blue tones align with wellness app conventions while differentiating from competitors
Emotional Range Development:
Created 8 core expressions matching key onboarding moments
Each expression serves a specific UX function (welcoming, encouraging, celebrating)
Tested readability at 80px size to ensure expressions communicate clearly on mobile
Illustrations style creation
Chat Interface Screen:
"Chat Pattern Rationale: Traditional form fields feel transactional and create pressure to answer 'correctly.' The conversational pattern reframes data collection as a friendly dialogue, reducing cognitive load and completion anxiety. Users can edit any previous response by tapping it—removing the fear of making irreversible mistakes."
Voice/Text Toggle:
"Dual Input Modes: Not just an accessibility feature—fundamentally changes user engagement. Voice feels more natural and faster for some users; text provides more control and privacy for others. Both format answers consistently for data collection."
Progress Indicator:
"Mid-flow Encouragement: After question 5, Luna provides a progress update acknowledging completion of the hardest part. This reduces abandonment at the psychological midpoint where users are most likely to drop off."
Planned Testing Approach:
Moderated usability testing with 8-10 target users
Key metrics: completion rate, time-to-complete, subjective satisfaction
Specific questions: Does Luna feel helpful or intrusive? Is voice/text choice intuitive?
Anticipated Challenges:
Voice recognition accuracy for diverse accents
Users who prefer neither voice nor extended typing
Privacy concerns about AI processing personal wellness data
Design Iterations Already Made:
Initially placed Luna's voice prompt at top; moved to center after realizing users expected it to animate
Reduced Luna's dialogue by 30% after finding initial scripts felt too chatty
Added explicit "edit" buttons after concern that tapping previous responses wasn't discoverable
