Product Designer | Jul 2022 – Dec 2023
Ringle AI Analysis
English Proficiency Diagnostic
Role
Product Designer
Timeline
Jul 2022 – Dec 2023
Team
Design, ML Engineering, PM
Skills
Overview
How might we help English learners understand their proficiency objectively?
Ringle users wanted to understand their English proficiency beyond subjective feedback. The challenge was to create an AI-powered diagnostic system that analyzes real speaking data and presents insights in a way that motivates continued learning.
Problem
Learners struggled to understand their actual English level
Before AI Analysis, Ringle users received qualitative feedback from tutors, but lacked objective, data-driven insights into their proficiency. This made it difficult to track progress over time and identify specific areas for improvement.
Solution
AI-powered diagnostics across 4 key dimensions
I designed a comprehensive diagnostic system that analyzes speaking data across Complexity, Accuracy, Fluency, and Pronunciation. Each dimension provides a level score (1-9) with detailed breakdowns and personalized recommendations.
Outcome
Measurable impact on user engagement and conversion
The AI Analysis feature became one of Ringle's most-used features, with over 350,000 diagnostics delivered. Users who viewed their reports showed 2-3x higher conversion rates, demonstrating the value of data-driven learning insights.
350,000+
Diagnostics delivered
42%
Report view rate
2-3x
Higher conversion for report viewers
Reflection
What I learned
Data visualization is key to user understanding
Complex AI outputs need to be translated into intuitive visual formats that users can quickly understand and act upon.
Balancing detail with clarity
Finding the right level of detail was crucial - too much information overwhelms, too little fails to provide actionable insights.