Flamingo Journey
An AI-Assisted Game Design Project Exploring Conservation and Bilingual Learning
Design and development: Kathryn Kong, with AI assistance
Design Question
How might a game encourage players to understand conservation as ongoing care rather than a series of correct answers?
Flamingo Journey is an illustrated web game that invites players to read a wetland landscape, make conservation decisions, and see how their choices unfold over three changing seasons. It explores how interactive systems, bilingual content, and visual storytelling can turn ecological information into an experience of curiosity and care.
Players consider the connections between water, food, and nesting conditions, then decide which needs to prioritize. English, Spanish, and paired bilingual modes offer different ways to engage with the same experience.
Player Experience
Observe the Landscape
Begin as a Chilean flamingo and explore five flamingo homelands. Collect habitat observations in a field journal and discover the relationships between each species and its environment. These destinations form an educational journey rather than a literal migration route.
Choose How to Help
Observations unlock conservation actions. With two action points each season, players decide where to direct their attention. Protecting water flows, reducing pollution, and supporting nesting safety involve different costs and benefits.
Reflect on What Changes
Seasonal pressures affect the habitat alongside the player's actions. A final report highlights remaining vulnerabilities and encourages another attempt. Optional vocabulary practice connects words to the environments players have explored.
Design Decisions
Make Observation Matter
Reading the landscape unlocks actions, connecting ecological information directly to play. The field journal gives players a place to revisit observations and useful words.
Make Trade-offs Visible
Water balance, food availability, and nesting safety remain separate indicators. A strong result in one area cannot conceal a vulnerable habitat elsewhere. Limited seasonal action points make prioritization part of the experience.
Support Language Learning Without Punishment
Players can use English, Spanish, or both together. Vocabulary practice is optional, and language mistakes do not damage the habitat.
Keep Entry Simple
Guest play requires no account, and progress saves on the device. Optional account features support saving and restoring progress. The installable web app supports offline play after its resources have been cached.
The habitat indicators are an illustrative teaching model, not measurements of real ecosystems or predictions of the effectiveness of conservation interventions.
AI-Assisted Game Design
This project also explores AI-assisted making as a design practice. AI assistance supported the revision process, while the wetland illustration was generated and refined through an iterative visual workflow. The artwork moved from a heavier, sepia-toned scene toward a lighter palette of mint, turquoise, and pink.
The central design work involved deciding how these materials should serve the experience: what players notice, which choices become available, and how consequences are communicated. The illustrated habitat creates a setting for exploration; it does not function as a scientific map.
Iterate With Intention
Generating an image was one step in a larger process of selecting, revising, and integrating material. The visual revision emphasized a lighter atmosphere and open space for the game's observation markers.
Keep the Evidence Visible
Conservation content draws on identified natural-history sources. AI-generated artwork and simplified numerical mechanics are labeled according to their role, so an appealing representation is not mistaken for ecological evidence.
Distinguish Making From Playing
“AI-assisted” describes the making process. The conservation experience uses authored bilingual content and defined game rules, rather than live AI-generated responses.
Making the Game
Flamingo Journey is built with React and Vite and published on GitHub Pages. It is an installable progressive web app, with device-based progress saving and optional Firebase authentication and cloud storage.
The revised game connects three seasonal decisions with a field journal, conservation feedback, and optional word practice. Each homeland has an independently saved expedition, allowing players to explore different habitats at their own pace.
Reflection
The redesign shifted Flamingo Journey from presenting information and checking answers toward asking players to observe, prioritize, and reconsider. Conservation became a continuing relationship between several habitat needs, rather than a single successful choice.
The project also makes AI literacy part of the design process: evaluating generated material, documenting its use, and deciding when visual appeal or simplified mechanics need qualification. The intended learning experience is grounded in design choices; its educational effects still need to be examined through playtesting.
Future playtesting could explore whether players understand the ecological trade-offs, find the bilingual presentation helpful, and carry their curiosity beyond the game.
Sources and Credits
Conservation references include BirdLife International’s flamingo overview, the Natural History Museum’s discussion of rising water levels and lesser flamingos, and the San Diego Zoo Wildlife Alliance flamingo guide.
The featured wetland artwork was AI-generated and revised for this project. Species photographs are retained within the game. The illustrated landscape and habitat indicators are educational representations.