Koinoike
Koinoike is a mobile app concept designed to help koi fish owners track water quality, feeding schedules, and fish health in one place.
I created this project to explore how design can simplify specialized hobbies, blending research insights with interface design.
My Role: UX Researcher & Designer
Timeline: 4 weeks (solo project)
Deliverables: Research synthesis, wireframes, high-fidelity prototype
Problem & Audience
Farmers, hobbyists, and nature lovers with koi ponds often face challenges that feel endless especially if they’re new to pond care or managing a small ecosystem without professional support.
Common frustrations include:
Persistent water quality issues: Ammonia and nitrite spikes are the leading causes of koi health decline, sometimes only noticed once symptoms appear, like lethargy or gill damage.
Invisible threats to sensitive fish: Rapid pH fluctuations, especially those tied to low carbonate hardness, can trigger unstable pond cycles. Even when water seems clear.
Overwhelming maintenance needs: Smaller ponds demand frequent attention so tasks like filter cleaning, algae control, and water testing can become confusing or time-consuming.
Koi ponds often look calm on the surface, but murky water can hide harmful conditions and leave owners unsure how healthy their fish really are
Process
To ground the design in real challenges, I reviewed existing koi care guides and hobbyist forums, and conducted interviews with five koi pond owners. I also analyzed competing pond management apps to identify gaps. This helped me focus the design on simplifying logging, interpretation, and daily routines.
Research goals
Understand koi owners’ routines and pain points around water testing, feeding, and fish records.
Learn how novices interpret (or misinterpret) pH/°F/mg / L values.
Identify opportunities to reduce logging friction and increase confidence.
Key insights from research
1.Logging is inconsistent.
Many pond owners skip water tests or forget to log results because the process is too long.
3. Fish-first care.
Owners think of their koi as individuals, with names, birthdays, and health notes, not just as part of a pond system.
Snapshot of koi hobbyists interviewed (n=5), showing their experience levels, pond contexts, and care behaviors.
Affinity snapshot: sample clusters from a synthesis of interviews (P##) and Reddit threads (R##)
2. Numbers don’t equal understanding.
Users often don’t know whether readings like “pH 6.8” are good or bad without extra research.
4. Overwhelmed by maintenance.
Care tasks pile up, and people benefit from gentle reminders and contextual recommendations.
Design
Based on my research insights, I created a set of design principles to guide decisions throughout the project. These ensured consistency and kept the focus on koi owners’ real needs rather than just visual polish.
Clarity over complexity
Water test results are displayed with a simple health gauge and clear ranges so owners don’t have to interpret raw numbers.
Efficiency in logging
The design minimizes the steps needed to add a new feeding or water log, making daily tracking feel lightweight
Guided learning
Tips and recommendations (like feeding advice) help newer owners gain confidence without overwhelming them.
Fish-first care
Instead of only focusing on the environment, the app highlights individual koi, reflecting how owners naturally think.
Solution
The final design simplifies koi-pond care by reducing steps to log data, interpreting water quality with clear ranges, and centering the experience around individual fish. The following screens illustrate these solutions in action.
Flow 1
Intent: Make routine logging fast so owners keep up with care.
What happens: From Home, tap Update water log, enter values, save.
→
Flow 2
→
→
Intent: Reflect how owners actually think, by individual fish.
What happens: View fish photos, notes, birthdays, and recent health or behavior.
→
Feedback
I shared the prototype with five koi hobbyists and asked them to walk through a few everyday tasks (log a water test, check pond health, calculate feeding, review a fish profile) while thinking aloud.
What resonated
Clear Gauges: The status gauge with “good / caution / action needed” ranges made water chemistry understandable without extra research.
Fast logging: The Home → Update water log path felt quick and repeatable for daily use.
Fish-first organization: Managing care by individual koi (photos, birthdays, notes) matched how owners actually think.
Tone & readability: Friendly microcopy and large tap targets reduced anxiety around “getting it wrong.”
Consistency: Cards/tiles/buttons felt like one system, which made the app feel trustworthy.
Opportunities
Tighten microcopy & units: Standardize things like 60 °F, mg/L, and capitalization (pH, Dissolved Oxygen).
Surface context where it’s needed: Add small “i” tooltips for metrics and a “Last updated” timestamp on water logs.
Reduce duplication: Keep one primary CTA per screen (e.g., remove duplicate “Log New Data” buttons).
Accessibility polish: Re-check contrast on orange/light-blue labels and keep touch targets ≥ 44×44.
Next Steps
Ship the microcopy/units pass across screens.
Add a lightweight clickable prototype for the Log a water test flow.
Explore reminders and weekly summaries; consider import from Bluetooth test kits as a future enhancement.
What I Learned
Designing Koinoike taught me how to turn a niche, chemistry-heavy hobby into clear daily decisions. Talking with koi enthusiasts made it obvious that owners don’t want raw numbers, they want to know “Is my pond okay, and what should I do?” That insight pushed me to prioritize a single health gauge with plain-language ranges, a fast logging path from Home, and a fish-first structure that reflects how people actually think about care. Prototyping those flows forced rigor in microcopy, units (°F, mg/L, pH), and touch targets—small details that decide whether logging becomes a habit. If I had more time, I’d validate the ranges and alert thresholds with a broader set of ponds, run a short diary study to see if logging stays consistent, and explore reminders and weather-based tips or Bluetooth test-kit imports. Overall, this project strengthened my research-to-design loop: learn the language of the field, reduce steps, and let the system guide confident action.
