Timeline
August to December 2024 (14 Weeks)
My Role
UX Researcher, Conversation Designer, Usability Tester
The Goal: To design and evaluate a conversational AI chatbot aimed at providing Arizona residents with real-time water information and resources, enhancing user satisfaction and accessibility through an intuitive interface.
“I would like that if there is one like that. All information in one spot, easily accessible…”
The Problem
Arizona residents needed clear, trustworthy answers about their water, but the information was scattered and hard to find.
My initial research confirmed that residents faced common pain points when trying to access consolidated water information. This uncertainty led to frustration about water quality, conservation, and billing, highlighting the need for a central, user-friendly platform.
The Solution
Before Text
After Text
I designed a more welcoming, user-friendly chatbot experience, starting with a revised splash page. The final chatbot provides a single point of contact for users to get answers on key topics, validated through a full UX process.
Key Features:
-
Provides real-time information on water quality and safety.
-
Answers common billing and account inquiries.
-
Offers troubleshooting for common residential water issues.
-
Presents information in a simple, conversational interface.
My Process
From Insights to Impact
My design process was guided by an iterative cycle of user feedback and continuous refinement. Here’s how I approached the challenge.
1. Talking to the Community: User Interviews & Persona Development
To ground the project in real-world needs, I conducted user interviews with Arizona residents... I used this data to develop a detailed user persona that served as a constant reference, ensuring user needs remained the focal point of every design decision.
2. Finding the Patterns: Collaborative Survey Analysis
I collaborated with a team of six to conduct a qualitative data analysis workshop on survey results... Our data organization procedure involved using tools like Google Docs and Airtable to manage the information, which was vital for revealing patterns and informing our final recommendations.
3. Learning from the Experts: Heuristic Evaluation
I performed a heuristic evaluation by benchmarking the chatbot against conversational AI models like ChatGPT and Bard... This analysis uncovered key usability issues, including the chatbot’s inability to provide clear and concise responses in certain scenarios.
4. Observing Real Behavior: Usability Testing
Observing their interactions using the "think aloud" method offered immediate, unfiltered feedback... This testing was crucial, as it highlighted that the chatbot often gave irrelevant responses, especially about indigenous populations—an important finding that might have been overlooked in traditional surveys.
Some Quotes
What They Said...
"The indigenous responses felt copy-pasted"
"This is exactly what I was looking for"
"The navigation is extremely user friendly"
The Final Design
Section 1
Before Text
After Text
Section 2
Before Text
These resources provide valuable information on Arizona’s water situation and conservation efforts.
After Text
These resources offer valuable insights into Arizona's water situation and conservation initiatives.
Or…
Check out these resources for more valuable information about Arizona’s water!
OR
Section 3
Before Text
After Text
Section 4
Before Text
Free Research Preview. Arizona Water Chatbot may produce inaccurate information about people, places, or facts.
After Text
Arizona Water Chatbot may produce inaccurate information.
To effectively communicate the project’s progress and outcomes, I compiled all findings into a detailed slide deck. This presentation highlighted the entire research process, identified the challenges we faced, and showcased the final chatbot design and revised splash page, creating a clear narrative of the chatbot's iterative evolution.
Reflections & Lessons Learned
This project was a significant accomplishment because it exemplified the application of human-centered design principles to solve real-world problems. It broadened my understanding of conversational UI design and the complexities of developing AI-powered solutions.
The insights gained from direct user feedback were pivotal in making iterative design improvements, enhancing my problem-solving skills and reinforcing the importance of user advocacy in product development. It was a rewarding opportunity to learn and grow, and I look forward to applying these insights in future projects.