From confusion to clarity:
Emotional intelligence insights for real life
Title
Founding Product Designer
Time frame
Aug 2023 - Present
Summary
I led the creation of a generative AI chatbot that helps people improve their emotional intelligence. The chatbot leverages CBT, DBT, ACT, somatic practices, and attachment theory to help users address their mental health. The AI chatbot UI design was evaluated through usability testing to improve the interaction with 150 people. A system usability scale of 92% was given to the chatbot experience.
This first wave of positive interactions is just brimming with potential to truly help people transform their mental health by leveraging the collective intelligence of machines + subject matter experts in psychology.
It was a massive undertaking to combine the subject matter expertise of multiple psychologists with generative AI chatbot to help people master their emotions at a higher level.
When the AI bot is paired with online courses, group support, 1:1 coaching, and progress trackers, the AI chatbot becomes part of a mental health ecosystem that helps users strengthen emotional intelligence so that they can have a better relationship to themselves and other people.
Identifying user needs: Insecure attachment styles experience the most dysfunction in relationships, with unique insecurities
Pattern recognition: The chat needed to reveal insecurities, habits, and beliefs that influence mental health of the user
Adapting to user needs: Users could choose a psychology modality that is best for them in how ChatBot responds
Holistic Integration: Beyond chatting with the AI bot, users needed a program to apply emotional intelligence to their lives
Differentiation: To craft a unique chatbot experience that is different, specialized, and helpful for end users.
All too often people lack emotional support systems to help them navigate challenges. With therapy being out of reach out of cost or shame, the AI bot comes in as a tool for self-therapy.
With a brand new startup in the seed phase, the UX strategy needed to move from 0 to 1. This result was a more in-depth and complex process to create the user experience.
With clarity around user needs and business needs, I began the UX process to close the gap between vision and implementation.
Secondary research: The initial data gathering came from studying attachment style questions from 17,000 members
Competitive analysis: An evaluation of major generative AI players was done to pinpoint opportunities for unique impact
Empathy mapping: To build a shared sense of understanding, I mapped out how the support process typically unfolds for users
Execution plan: This included creating a design system, UX processes, accessibility standards, and UX research protocols.
Business goals: The AI chatbot is the 3rd revenue stream, as a low ticket SaaS product that complements the business model.
The data I gathered from usability testing helped to create meaningful improvements that improved the AI experience for the end users.
Design system: With an emphasis on color psychology, design components balanced branding with usability and delight
Ideation: The brainstorming phase allowed many ideas to come forth for tailoring the AI for user mental health needs
Wireframes: The new ideas were mapped out layouts for desktop and mobile devices and accessibility elements built in
Mockups: The design system was applied to device interfaces to bring to life the solutions that were carefully thought through
Usability testing: With a Google UX testing protocol in place, I used unmoderated usability tests to evaluate the interactions.
To bring the UX strategy to life, we followed Enterprise Design Thinking practices. This is where I worked very closely with the UX team to ensure that we aligned our efforts to improve UX.
Learnings
With creating an AI chatbot, I learned so much about emotional sentiment in generative AI. Beyond facts and figures, the AI bot need to demonstrate empathy and life enhancing insights.
This custom built AI chatbot is now in the development phase. It is leveraging machine learning to gather collective intelligence on mental health, psychology, and compassionate user support.
Purpose: It is incredible to think about how mental health can become more accessible, helpful, and transformative with AI.
Autonomy: The choice to choose when, how, and where to get mental health truly empowers people to get the help they need.
Mastery: AI can truly shorten the learning curve from decades to days by creating a clearer path to improving mental health.
