🎯 Goal

Increase user engagement and retention by introducing AI-driven personalization into a brain-training application.

🧠 Problem

• Generic training flows → low long-term engagement
• Diverse user needs (ability, motivation, pace) not addressed
• Lack of personalization → user drop-off

⚙️ What I Did

• Conducted 15 user interviews and usability tests to identify engagement barriers
• Analyzed behavioral data (retention, completion, drop-off points)
• Applied AI clustering to segment users (e.g., fast learners, casual users, struggling users)
• Designed personalized dashboards and adaptive training flows in Figma
• Introduced explainable AI features to improve user trust (“why this recommendation”)

🤖 AI Integration

• Behavioral clustering → user segmentation
• Predictive modeling → engagement & churn forecasting
• Data-driven personalization → difficulty, timing, content adaptation

📊 Outcome

• Identified key engagement patterns across user segments
• Defined AI-driven personalization strategies
• Delivered prototype + insights to support data-driven product decisions

🧠 Key Insight

Personalization increases engagement only when users understand and trust AI decisions.

🚀 Next Steps

• Validate personalization impact through real-world A/B testing
• Iterate on adaptive learning models with live user data

Dashboard – Personalized Training Overview

• Personalized daily training plan: This screen provides users with a personalized daily training plan based on their recent performance and engagement patterns.
• Recommended for you: The “Recommended for you” section reduces decision-making friction by suggesting the most relevant session, helping users get started quickly without overthinking.
• AI-driven selection: AI analyzes user behavior and dynamically selects the most suitable training, increasing engagement and session start rate.

Training Session – Adaptive Learning Experience

This screen represents the active training session, where tasks adapt dynamically to the user’s performance.

AI adjusts difficulty based on recent sessions, ensuring the user remains challenged without frustration.

Real-time feedback (e.g., performance improvement) reinforces motivation and supports continuous engagement.

Progress & Insights – AI-Driven Feedback

This screen visualizes user performance over time and translates data into actionable insights.

Users can quickly understand strengths, weaknesses, and trends (e.g., improvement or decline patterns).

AI generates personalized recommendations (e.g., optimal training time), helping users improve outcomes through data-driven guidance.