plantment
overview
gamifying plant care with ai
overview
gamifying plant care with ai
plantment
client
bootcamp student project
my role
user researcher | ux designer
project time
2 weeks
collaborators
team of 4
plantment started with a simple question: why is it so easy to forget to water your plants and so hard to know what they actually need? our team of five wanted to turn that everyday frustration into something playful. inspired by virtual pet games like webkinz, we imagined an app that makes caring for real plants feel like caring for digital companions.
the goal was not just to make something cute. we wanted to help plant parents actually learn how to keep their plants thriving. we built an ai-assisted care platform that uses game-like interactions to teach users about watering schedules, sunlight placement, and common plant issues.
understanding the problem
we started by mapping out what makes plant care difficult. through early conversations, it became clear that most frustrations came from uncertainty. people were not confident about what their plants needed, and the information online was often generic or overwhelming.
we identified several recurring issues:
• figuring out where to place plants for the right light
• forgetting to water or accidentally overwatering
• dealing with pests, mites, and other unwelcome visitors
• not knowing how to diagnose what is wrong when a plant looks unhealthy
• struggling to find reliable information tailored to their specific plant
• not having the time or attention to give plants consistent care
solution
• create one easy-to-use platform where users can see and manage all their plants
• give users a virtual space to care for their plants anytime, anywhere
• add fun, game-like features to keep users engaged and motivated
• personalize the experience to help users identify and solve specific plant issues
• suggest ideal plant placement and share helpful care tips tailored to each plant
building empathy
our team used a mix of qualitative and quantitative research methods to understand user needs. on the qualitative side, we conducted 12 interviews to uncover real pain points, emotions, and goals. on the quantitative side, we distributed an online survey and collected 37 responses to validate those patterns.
after gathering the data, we mapped it out visually to see the emotional and behavioral layers behind plant care.
our team used a mix of qualitative and quantitative research methods to understand user needs. on the qualitative side, we conducted 12 interviews to uncover real pain points, emotions, and goals. on the quantitative side, we distributed an online survey and collected 37 responses to validate those patterns.
after gathering the data, we mapped it out visually to see the emotional and behavioral layers behind plant care.
in the pains and gains map, we captured what users struggled with most — uncertainty, lack of clear guidance, and frustration when they could not find specific information. we also noted the small wins they valued, like having trustworthy resources or seeing positive feedback when a plant was thriving.
the second framework, think-feel-say-do, helped us understand motivations on a deeper level. it showed that users often think they should already know how to care for plants, feel guilty or frustrated when things go wrong, say they want to learn more, but often do nothing until a visible problem appears. this gap between intention and action became a key insight.
many users said things like “i want to know what’s wrong, but i can’t tell just by looking.” others mentioned spending hours scrolling through forums, hoping to find someone with the same issue. this told us that the emotional weight of plant care — the guilt, the guessing, and the relief when things improve — was just as important to design for as the functional side.
design
low-fidelity wireframes
plant profiles
the plant profile became the emotional center of the product. each plant gets a dedicated page where users can see watering needs, sunlight status, care tips, and their plant’s progress over time.
diagnosis flow
the ai assistant was designed to feel like a calm companion rather than a technical chatbot. users can ask questions, upload photos, or check symptoms, and the responses appear in simple, friendly language.
my room: creating a little world
the room builder is where the experience becomes personal.
healthy plants result in happy virtual plants. neglected plants show subtle signs like slight drooping or low energy.
this mirror system creates a gentle emotional loop that supports behavior change.
users can place items, rearrange furniture, and customize their world. it becomes a visual reflection of their attention and progress.
coin economy and rewards
takeaway & learnings
#1 simplicity is the hardest part to design
paper sketches, half finished screens, rough clickable flows. testing early saved time and exposed blind spots way faster than polishing screens in isolation.
#2 small emotional cues can change long-term behavior
students do not need fancy features. they need fast, the subtle reactions of virtual plants, gentle task animations, and calm ai responses created a positive loop that brought users back. small moments of delight had a measurable impact on motivation.
#3 motivation grows when progress is visible
adding small visual reactions — healthier virtual plants, subtle room upgrades, tiny coins dropping in — created a feeling of momentum. users stayed engaged because they could see their care paying off, both in data and in their virtual space.