Project Overview
AZ Water Chatbot is an important part of the Arizona Water Innovation Initiative at ASU, created to help address the state’s water challenges.
We saw ways to make the chatbot better at building trust and sharing reliable information with Arizona residents.
I led the design and planning for the quarter-screen interface of the chatbot, focusing on features and improvements to make the chatbot easy to use and trustworthy for water information.
The Challenges
Seamlessly integrating the quarter-screen chatbot into the Arizona Water Blueprint website without obstructing key content or navigation.
Ensuring continuous and engaging user interaction as visitors browse, with prompts and interactive features to enhance learning.
Providing clear, accurate, and trustworthy water information with transparent data sources and reputable affiliations.
Minimizing user friction by keeping the chatbot intuitive, accessible, and non-intrusive across all devices.
User Needs
Business Goals
Research Insights
Initial Wireframes
After the research phase, each team selected a specific section of the chatbot to develop. One team worked on the full-screen maximized version, while my team focused on the quarter-screen chatbot. Both teams collaborated closely to ensure a seamless and consistent transition between the quarter-screen and maximized versions, providing users with a smooth, cohesive experience no matter how they used the chatbot.
Final Designs
Final Prototype
Outcomes
The AZ Water Chatbot has the potential to make a significant difference for Arizona residents by making reliable water information more accessible and easy to understand. With a user-friendly quarter-screen design, the chatbot can:
My Learnings
Designing the AZ Water Chatbot offered valuable insights into creating an AI tool for a diverse public audience. These key learnings shaped both the project and my approach to user experience design:
Building user trust is essential, especially for critical topics like water management, by providing reliable, transparent information.
Understanding different information habits: Rigorous research revealed that some communities find and trust information in unique ways, which we might have unintentionally overlooked. This insight helped us design the chatbot responses to be more inclusive and effective.
Value of qualitative data: Conversations and interviews with users and stakeholders provided deep insights that guided better design decisions and helped solve core problems.
