Case Study
Blind Box
Blind Box
Make confident decisions in the moment after unboxing.
Make confident decisions in the moment after unboxing.
Featured Highlights
Value Interpretation
Most platforms present fragmented and often misleading price listings, leaving users to make sense of inconsistent data on their own.
This app synthesizes listed and sold prices into a narrowed, realistic range, highlighting a “most likely” value. Instead of exposing users to raw data, it translates market activity into something immediately understandable and actionable.
Compares listed vs. sold prices
Filters out inflated or outdated listings
Surfaces a clear, realistic value range
Confidence Indicator
Understanding value is only useful if users can trust it. Each valuation is paired with a confidence level based on data consistency and availability. This helps users quickly assess whether the market is stable, volatile, or uncertain—supporting more informed decisions.
High: consistent, recent sales data
Medium: moderate variation in pricing
Low: limited or inconsistent data
Behavioral Signals
Rather than relying on traditional community features, the app captures lightweight input on what users are actually doing with their items.
These signals are aggregated to reflect real-world behavior, giving users contextual insight without the noise of comments or discussion threads.
Quick inputs (e.g., sold quickly, holding, traded)
Aggregated into a simple, scannable visual
Optional price input adds depth without friction
Featured Highlights
Value Interpretation
Most platforms present fragmented and often misleading price listings, leaving users to make sense of inconsistent data on their own.
This app synthesizes listed and sold prices into a narrowed, realistic range, highlighting a “most likely” value. Instead of exposing users to raw data, it translates market activity into something immediately understandable and actionable.
Compares listed vs. sold prices
Filters out inflated or outdated listings
Surfaces a clear, realistic value range
Confidence Indicator
Understanding value is only useful if users can trust it. Each valuation is paired with a confidence level based on data consistency and availability. This helps users quickly assess whether the market is stable, volatile, or uncertain—supporting more informed decisions.
High: consistent, recent sales data
Medium: moderate variation in pricing
Low: limited or inconsistent data
Behavioral Signals
Rather than relying on traditional community features, the app captures lightweight input on what users are actually doing with their items.
These signals are aggregated to reflect real-world behavior, giving users contextual insight without the noise of comments or discussion threads.
Quick inputs (e.g., sold quickly, holding, traded)
Aggregated into a simple, scannable visual
Optional price input adds depth without friction
View Figma Prototype
Overview
Roles
Researcher, Interaction Design, Visual Design, Wireframing, Prototyping
Timeline
6 months
Tools
Figma, Illustrator
Interpreting Value Right After the Unboxing Moment
This project focuses on that gap. It introduces a decision-support tool designed for the moment users are faced with an item they didn’t necessarily want, or don’t fully understand the value of. By interpreting market data, indicating confidence, and reflecting real user behaviour, the app helps collectors make sense of their options quickly and with more certainty—without taking away from the spontaneity that makes blind boxes appealing in the first place.
Overview
Roles
Researcher, Interaction Design, Visual Design, Wireframing, Prototyping
Timeline
6 months
Tools
Figma, Illustrator
Interpreting Value Right After the Unboxing Moment
This project focuses on that gap. It introduces a decision-support tool designed for the moment users are faced with an item they didn’t necessarily want, or don’t fully understand the value of. By interpreting market data, indicating confidence, and reflecting real user behaviour, the app helps collectors make sense of their options quickly and with more certainty—without taking away from the spontaneity that makes blind boxes appealing in the first place.
Context
Blind boxes are designed around anticipation and uncertainty, which drives excitement but also creates friction. When collectors don’t get the figure they want or are unsure of the value of what they pulled, they face confusion and inaction. Existing tools, like resale platforms or marketplaces, provide fragmented pricing, inconsistent data, and little context, leaving users to navigate decisions on their own. This gap turns a moment of excitement into frustration, duplicate accumulation, and overconsumption.
Context
Blind boxes are designed around anticipation and uncertainty, which drives excitement but also creates friction. When collectors don’t get the figure they want or are unsure of the value of what they pulled, they face confusion and inaction. Existing tools, like resale platforms or marketplaces, provide fragmented pricing, inconsistent data, and little context, leaving users to navigate decisions on their own. This gap turns a moment of excitement into frustration, duplicate accumulation, and overconsumption.
Research
Research
Peer Reviewed
More than 60% of purchases are influenced by creator content.
Blind boxes have grown from a niche collectible into a global trend, largely fueled by social media. Unboxing content across TikTok, Instagram, and YouTube has turned the act of opening a box into a shared, performative experience for millions of viewers.
With collectors discovering new series and rare figures through viral posts and influencer-driven hype. This visibility has helped fuel massive growth. In 2025 alone, billions of blind box units were sold globally, with demand driven by limited editions, collaborations, and the appeal of rare “hidden” figures.
At its core, the popularity of blind boxes is psychological. The experience taps into anticipation, surprise, and reward, creating a repeat purchase loop where the uncertainty itself becomes the product. For many collectors, the act of opening the box can feel just as valuable as the item inside.
That momentum creates a less visible downside.
Consequences of Unwanted Pulls
As blind boxes scale in popularity, so does the volume of items that users didn’t actually want. Because outcomes are random, duplicates and lower-demand figures are inevitable, leaving users with items they don’t value and aren’t sure what to do with.
This creates a friction point. What begins as a moment of excitement often leads to a backlog of items with unclear worth and no obvious next step. Users are left navigating resale platforms, inconsistent pricing, or informal trades, all of which require time, effort, and interpretation.
Consumerism and Accumulation
The uncertainty that drives repeat purchases means users often buy multiple boxes in pursuit of a single desired item. As a result, more products are circulated than are meaningfully kept or valued. Items become secondary to the experience of opening them, leading to excess accumulation and, in some cases, waste.
Without clear tools to understand value or facilitate decisions, unwanted items are more likely to sit unused, be undervalued, or be discarded altogether. The system encourages continuous buying, but offers little support for what happens after.
Peer Reviewed
More than 60% of purchases are influenced by creator content.
Blind boxes have grown from a niche collectible into a global trend, largely fueled by social media. Unboxing content across TikTok, Instagram, and YouTube has turned the act of opening a box into a shared, performative experience for millions of viewers.
With collectors discovering new series and rare figures through viral posts and influencer-driven hype. This visibility has helped fuel massive growth. In 2025 alone, billions of blind box units were sold globally, with demand driven by limited editions, collaborations, and the appeal of rare “hidden” figures.
At its core, the popularity of blind boxes is psychological. The experience taps into anticipation, surprise, and reward, creating a repeat purchase loop where the uncertainty itself becomes the product. For many collectors, the act of opening the box can feel just as valuable as the item inside.
That momentum creates a less visible downside.
Consequences of Unwanted Pulls
As blind boxes scale in popularity, so does the volume of items that users didn’t actually want. Because outcomes are random, duplicates and lower-demand figures are inevitable, leaving users with items they don’t value and aren’t sure what to do with.
This creates a friction point. What begins as a moment of excitement often leads to a backlog of items with unclear worth and no obvious next step. Users are left navigating resale platforms, inconsistent pricing, or informal trades, all of which require time, effort, and interpretation.
Consumerism and Accumulation
The uncertainty that drives repeat purchases means users often buy multiple boxes in pursuit of a single desired item. As a result, more products are circulated than are meaningfully kept or valued. Items become secondary to the experience of opening them, leading to excess accumulation and, in some cases, waste.
Without clear tools to understand value or facilitate decisions, unwanted items are more likely to sit unused, be undervalued, or be discarded altogether. The system encourages continuous buying, but offers little support for what happens after.
Problem Statement
Problem Statement
Blind boxes create excitement but also friction when collectors don’t get the figure they want or aren’t sure of its value. How might we empower collectors to make sense of these items, provide clarity on value, build trust in the data, and help them feel confident about what to do next?
Blind boxes create excitement but also friction when collectors don’t get the figure they want or aren’t sure of its value. How might we empower collectors to make sense of these items, provide clarity on value, build trust in the data, and help them feel confident about what to do next?
Solution
NAME __ is a decision-support tool for the moment immediately after unboxing. It interprets market data, shows confidence in pricing, and reflects how other users are handling similar items, helping collectors make informed decisions quickly and confidently.
Solution
NAME __ is a decision-support tool for the moment immediately after unboxing. It interprets market data, shows confidence in pricing, and reflects how other users are handling similar items, helping collectors make informed decisions quickly and confidently.
User Research
User Research
Personas
I mapped personas across different irregular lifestyles. For each of these users, the core use cases include sleep recovery guidance, fatigue alerts, flexible goal setting, and wellness tracking that aids messy, unpredictable schedules instead of penalizing them.
Personas
I mapped personas across different irregular lifestyles. For each of these users, the core use cases include sleep recovery guidance, fatigue alerts, flexible goal setting, and wellness tracking that aids messy, unpredictable schedules instead of penalizing them.
Personas
I mapped personas across different irregular lifestyles. For each of these users, the core use cases include sleep recovery guidance, fatigue alerts, flexible goal setting, and wellness tracking that aids messy, unpredictable schedules instead of penalizing them.
Competitive Analysis
Existing platforms offer fragmented and often misleading pricing, with little context to guide interpretation. As a result, users are left navigating uncertainty not just in what they pulled, but in what to do next.
Competitive Analysis
Existing platforms offer fragmented and often misleading pricing, with little context to guide interpretation. As a result, users are left navigating uncertainty not just in what they pulled, but in what to do next.
Design Process
Design Process
Design Goals & Principles
SYSTEM FRAMEWORK
Adaptation Over Discipline
Plans adapt to the user’s context, energy, and life circumstances with no punishment for deviation, only gentle guidance. The system's adaptive pattern recognition will learn and adjust as the user continues.
Meaning over metrics
Data is interpreted and translated into actionable insights. Users understand why something matters, rather than being overwhelmed by raw numbers.
Normalization of rest
Pausing, taking breaks, or changing plans is expected and encouraged. Rest is framed as a natural part of progress, not failure. Fatigue alerts will provide early warnings before exhaustion sets in.
Supportive anticipation
The system predicts fatigue or stress patterns and responds proactively with personalized recommendations paired with encouragement and guidance, ensuring inclusivity for diverse needs.
Information Architecture
Aira's Taskbar
NAVIGATION
Aira's Taskbar
Given the user's irregular schedule, the hierarchy focuses on surfacing the most critical information with immediate accessibility.
Aira's Taskbar
Information Architecture
Aira's Taskbar
NAVIGATION
Aira's Taskbar
Given the user's irregular schedule, the hierarchy focuses on surfacing the most critical information with immediate accessibility.
Aira's Taskbar
Journey Map
A user journey map is essential because it contextualizes the reality of a shift worker down to the thoughts and emotions that are rarely seen or captured. In a world that never sleeps, it exposes the hidden biological friction of the post-shift cognitive lag and the profound disorientation of waking up in a dark room mid-day. By mapping these raw survival cycles, the journey identifies exact points of improvement, ensuring the app solves for the actual lived experience of the worker rather than trying to fit them into a standard 9-to-5 health framework.
Journey Map
A user journey map is essential because it contextualizes the reality of a shift worker down to the thoughts and emotions that are rarely seen or captured. In a world that never sleeps, it exposes the hidden biological friction of the post-shift cognitive lag and the profound disorientation of waking up in a dark room mid-day. By mapping these raw survival cycles, the journey identifies exact points of improvement, ensuring the app solves for the actual lived experience of the worker rather than trying to fit them into a standard 9-to-5 health framework.
User Flow
This flow illustrates how a user moves from reviewing their health data to selecting a plan within the app. Starting on the Dashboard, the user logs their sleep and navigates to the Insights page to review their weekly insight, helping them understand patterns in their sleep and recovery. From there, the user taps “Adjust Plan,” which directs them to the Plans page where available and recommended plans are presented. The user can browse these options, review plan details, and evaluate which routine best supports their needs before selecting a plan to activate.
Within this process, Aira uses predictive insights generated from the user’s logged data to highlight relevant plan suggestions, guiding the user toward routines that better support their sleep and overall health.
User Flow
This flow illustrates how a user moves from reviewing their health data to selecting a plan within the app. Starting on the Dashboard, the user logs their sleep and navigates to the Insights page to review their weekly insight, helping them understand patterns in their sleep and recovery. From there, the user taps “Adjust Plan,” which directs them to the Plans page where available and recommended plans are presented. The user can browse these options, review plan details, and evaluate which routine best supports their needs before selecting a plan to activate.
Within this process, Aira uses predictive insights generated from the user’s logged data to highlight relevant plan suggestions, guiding the user toward routines that better support their sleep and overall health.
Usability Testing
Initial testing with users, particularly nurses, provided critical insights:
USABILITY TESTING CONCERN #1
Proactive vs Reactive
Users emphasized the need for insights before fatigue set in, not after they were already exhausted.
USABILITY TESTING CONCERN #1
Reduced Cognitive Load
I simplified AI explanations to ensure they felt intuitive during high-stress work blocks.
Usability Testing
Initial testing with users, particularly nurses, provided critical insights:
USABILITY TESTING CONCERN #1
Proactive vs Reactive
Users emphasized the need for insights before fatigue set in, not after they were already exhausted.
USABILITY TESTING CONCERN #1
Reduced Cognitive Load
I simplified AI explanations to ensure they felt intuitive during high-stress work blocks.
Challenges
Challenges
1: Plans Framework
The Overall Health category became a microcosm of the entire project’s complexity. Initially, the volume of content made it difficult to maintain a distinct identity for each plan without them feeling redundant or clinical, this was particularly true for Plan C due to its low pressure approach.
From Categories to Concerns: I shifted the nomenclature from rigid "Health Categories" to user-focused "Health Concerns," making the plans feel more immediate and relevant to daily life.
The Triad Structure: To manage the volume, I established a consistent structure for every category: Steady, Fluctuation, and Strain. This allowed for three distinct plans per category (e.g., Emotional Awareness, Mood Stability, Daily Decompression), ensuring the system could support a user in any state, without overlap
Cohesion without Overlap: Each individual plan was reworked to focus on "rhythms over rules," ensuring they remained cohesive as part of the AIRA ecosystem while maintaining clear boundaries to prevent overlapping guidance.
1: Plans Framework
The Overall Health category became a microcosm of the entire project’s complexity. Initially, the volume of content made it difficult to maintain a distinct identity for each plan without them feeling redundant or clinical, this was particularly true for Plan C due to its low pressure approach.
From Categories to Concerns: I shifted the nomenclature from rigid "Health Categories" to user-focused "Health Concerns," making the plans feel more immediate and relevant to daily life.
The Triad Structure: To manage the volume, I established a consistent structure for every category: Steady, Fluctuation, and Strain. This allowed for three distinct plans per category (e.g., Emotional Awareness, Mood Stability, Daily Decompression), ensuring the system could support a user in any state, without overlap
Cohesion without Overlap: Each individual plan was reworked to focus on "rhythms over rules," ensuring they remained cohesive as part of the AIRA ecosystem while maintaining clear boundaries to prevent overlapping guidance.
2: Choice Fatigue
Problem: Originally, users had to manually navigate a "Masterlist" to find plans. Testing revealed this was overwhelming for tired shift workers.
The Solution: I pivoted to Smart Discovery, where the AI performs the heavy lifting by recommending plans based on real-time health signals. This shifted AIRA from being a "manual tool" to a "responsive companion".
2: Choice Fatigue
Problem: Originally, users had to manually navigate a "Masterlist" to find plans. Testing revealed this was overwhelming for tired shift workers.
The Solution: I pivoted to Smart Discovery, where the AI performs the heavy lifting by recommending plans based on real-time health signals. This shifted AIRA from being a "manual tool" to a "responsive companion".
Final
Final
Outcome
Aira was a very thought provoking and complex project where I integrated AI to better inform health choices and decision making.
Outcome
Aira was a very thought provoking and complex project where I integrated AI to better inform health choices and decision making.
Reflection
Healthcare + Design
Originally, I chose this topic and specified it because of my lived experience as a healthcare worker and being on an irregular schedule compared to my friends and family. I felt that this was a population that was overlooked often because they were essential workers and I wanted to translate my experience into a digital solution to address real-world fatigue and recovery needs. This project allowed me to utilize and combine both fields of study and grow as a professional in both regards.
Less Equals Calm
I learned that "calm is a feature" and that the most difficult part of AI design is knowing when to hold back. Moving away from over-engineered, prescriptive logic, I prioritized providing users with meaning and agency over raw data.
Redefining Success Metrics
In healthcare and design, success is often measured by rigid compliance, but this project taught me to value a system that is adaptable to individual needs. Which is fundamental to holistic design and user experience solutions.
Reflection
Healthcare + Design
Originally, I chose this topic and specified it because of my lived experience as a healthcare worker and being on an irregular schedule compared to my friends and family. I felt that this was a population that was overlooked often because they were essential workers and I wanted to translate my experience into a digital solution to address real-world fatigue and recovery needs. This project allowed me to utilize and combine both fields of study and grow as a professional in both regards.
Less Equals Calm
I learned that "calm is a feature" and that the most difficult part of AI design is knowing when to hold back. Moving away from over-engineered, prescriptive logic, I prioritized providing users with meaning and agency over raw data.
Redefining Success Metrics
In healthcare and design, success is often measured by rigid compliance, but this project taught me to value a system that is adaptable to individual needs. Which is fundamental to holistic design and user experience solutions.
What's Next
Plans Adjustment: Based on the user's interactions with the plans and their retention I would re-evaluate if the plans I created are appropriate. I think it can get quite confusing with the overwhelming amount of plans and text so I intend to do further refinement on which plans are most effective and supportive for shift workers.
Long-term Insight Loops: I will shift the focus from daily performance to long-term trends. By helping users reflect on patterns over weeks and months, the app reinforces self-awareness and effort instead of relying on the pressure of daily consistency.
What's Next
Plans Adjustment: Based on the user's interactions with the plans and their retention I would re-evaluate if the plans I created are appropriate. I think it can get quite confusing with the overwhelming amount of plans and text so I intend to do further refinement on which plans are most effective and supportive for shift workers.
Long-term Insight Loops: I will shift the focus from daily performance to long-term trends. By helping users reflect on patterns over weeks and months, the app reinforces self-awareness and effort instead of relying on the pressure of daily consistency.