AI-Powered Emotional Check-Ins for Equanimity

Equanimity, my proprietary self-awareness and emotional insight product, is currently in pre-launch. As the lead designer and founder of Equanimity, I created an AI-powered emotional check-in feature designed to support users in cultivating self-awareness and emotional resilience. Unlike traditional mood trackers that rely on passive self-reporting, this check-in experience actively engages users in reflection, reframing, and mindful connection — using natural language processing and sentiment analysis to build a personalized well-being profile over time.

The Problem

Many mental health apps collect mood data, but few help users make sense of what they’re feeling in the moment. Most tools feel transactional or burdensome — another thing to fill out. Users often drop off because the emotional labor of tracking isn’t met with meaningful insight or support.

The Goal

Design an AI-driven feature that transforms mood tracking into a moment of self-care, insight, and agency — helping users feel seen, supported, and capable of making intentional shifts in their mindset.

The Solution

The Equanimity Check-In uses a series of thoughtfully crafted multiple-choice prompts grounded in behavioral psychology. Rather than asking users to journal or rate their mood on a scale, it gently guides them to:

  • Name their current emotional state

  • Reflect on contributing factors

  • Surface subconscious needs or stressors

  • Select empowering reframes or affirmations

  • Receive personalized content and trends based on AI-driven insights

The interaction feels more like a moment of guidance than data entry. Over time, the system builds a nuanced emotional fingerprint that powers smarter recommendations and a deeper understanding of the user’s inner landscape.

My Role

  • Founder of Equanimity

  • UX Research

  • Interaction & Product Design

  • Behavioral Psychology Integration

  • Prompt Engineering (for NLP + AI logic)

  • Prototyping & Testing

  • Branding + Messaging

Design Process

1. Research & Insight Gathering

Interviews with individuals experiencing burnout, anxiety, and overwhelm

Analysis of drop-off behavior in existing wellness apps

Review of CBT and mindfulness-based therapeutic models

2. Ideation & Prompt Development

Created a taxonomy of emotional and cognitive states

Crafted 50+ multiple-choice prompts with embedded cognitive reframes

Mapped out response flows for dynamic AI-based feedback

3. Prototyping & Iteration

Built mid-fidelity wireframes and interactive prototypes

Tested with users for emotional clarity, tone, and usability

Refined AI output to reflect compassionate, non-judgmental guidance

4. Personalization Logic

Integrated NLP to detect sentiment trends over time

Designed a system for surfacing “insight moments” based on user patterns

Developed a content matching engine for personalized support

Key Features

  • Emotionally intelligent multiple-choice check-ins

  • AI-generated affirmations and reframes

  • Personalized well-being trends and emotional insights

  • Optional journaling expansions with sentiment analysis

Success Metrics — Reframed for Human Impact

We intentionally moved away from shallow engagement metrics. Instead, we focused on measurable indicators of emotional well-being, resilience, and self-awareness:

Metric Definition Why It Matters
Emotional Clarity Increase % of users who report greater ability to identify what they’re feeling over time Helps users build language around their emotions, supporting mental health literacy
Cognitive Reframe Usage # of users who opt into suggested reframes or affirmations after check-ins Indicates that the tool is actively helping users shift mindset patterns
Insight Frequency Average # of “aha” or self-awareness moments surfaced per user per week Measures emotional insight — a key driver in mental and emotional growth
Mood Volatility Reduction Change in user-reported emotional highs and lows across 30+ days Suggests increased emotional regulation and resilience
Self-Reported Relief % of users who report feeling better or more grounded after check-ins Captures immediate emotional impact of using the tool
Reflection Adherence % of users who complete more than 3 check-ins per week consistently over 1 month Signals trust in the tool as part of their emotional routine
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