Project
Ask UXR
Faster Research Conclusions
while empowering the designer.
Info
Role
UX and AI Designer
Timeline
January 2024-May 2024
Tools/Lanuguages
Figma
Python
Streamlit
Overview
Problem
Researchers have a hard time efficiently synthesizing and analyzing large amounts of qualitative data, leading to time-consuming and labor-intensive processes that hinder their ability to provide timely insights and recommendations.
Process
We created a canvas-style interface that combined chatbot functionality with an iterative loop between the AI and the human. The chatbot back-end and interface was developed to showcase the functionality.
Product
The research platform integrated responsible Generative AI with an innovative interface, allowing UX researchers to sort through their data in a matter of minutes, not days.
The AI x Human System
The Iterative Human and AI Loop
The first step of the project was to understand what the system between the human and AI needed to look like before applying it to the research methodology. The AskUXR team worked together to come up with the following diagram that uses a feedback loop to empower the researchers to create and edit findings while incorporating AI as an efficient and helpful tool.
Applying the Loop
To understand how the feedback loop is applied, we analyzed the individual process in one research step, using the Observation stage as an example. The AI analyzes and synthesizes data into a card, a process that can be tedious and time-consuming.
The feedback loop is then integrated into four distinct actions, leveraging AI and human input to empower users. This process is part of a larger research methodology, broken down into four components, with the goal of continually refining data into actionable insights. With this interface, researchers can track progress, save and build upon their work, and iterate until satisfied with the results.
The Interface
Visualizing the Research Workspace
The canvas-style UI approach was to help create a collaborative environment between the researcher and AI. Creating a unified workspace was the priority, with everything accessible in one interface.
The interface is divided into two distinct sections: the conversation space and the assets space. The assets space contains AI-generated cards, which are created from previous data or cards and serve as the foundation for user actions.
The Interface
Final application designs
The final design includes a chatbot interface on the left, which is accompanied by a list of all cards. Each card offers three options: converse with the AI, edit manually, or approve. Once all cards are edited and approved, they are further synthesized for the next process, generated, and added into the next list of cards for the user to parse through. This design allows users to efficiently edit and approve cards, and then synthesize the results for further analysis.
Intuitive Actions
Action Buttons
The four actions are designed for seamless navigation through AI-generated cards, with easy-access tabs at the top to further categorize and organize the content.
Editing Through Conversation
Chatbot Interface
The interface enables users to submit a card and initiate a conversation about it, with the card's layout seamlessly integrated into the chatbot to facilitate exploration and collaboration with the AI.
Streamlined Methodology
Tabular Exploration
The tab navigator on both the Asset section and top nav allows users to peruse through the cards and track their progress and work in each tab.
Development
Designed and developed the UXR Playbook Chatbot
While the final interface wasn’t developed in this sprint, the chatbot was created to understand the backend of using WatsonX along with a data source, in this case, the UXR Playbook by IBM. In this portion of the project, I designed the dark mode version of the site using Carbon components and developed the site using Python and Streamlit.
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