Side-by-Side Video Comparison
[Overview]
The Context
A the time of writing this I am currently leading UX design for Coachi, a mobile app that uses AI to help skiers improve their technique. To ground the product in real coaching practices, I began the discovery phase by researching how ski instructors teach. Following the double diamond method, I conducted interviews and field studies with instructors to understand their coaching processes and the methods learners are most familiar with.
Although instructors are not the primary users of Coachi, their expertise shaped the product strategy. By embedding professional coaching practices into the app, I ensured that learners receive a familiar, credible experience — one that mirrors what industry professionals would provide on the slopes.
A key insight from this research was that instructors often demonstrate an exemplar performance and guide learners to model their technique against it. However, when learners practise independently, they lose the benefit of live explanation and feedback.
The Context
A the time of writing this I am currently leading UX design for Coachi, a mobile app that uses AI to help skiers improve their technique. To ground the product in real coaching practices, I began the discovery phase by researching how ski instructors teach. Following the double diamond method, I conducted interviews and field studies with instructors to understand their coaching processes and the methods learners are most familiar with.
Although instructors are not the primary users of Coachi, their expertise shaped the product strategy. By embedding professional coaching practices into the app, I ensured that learners receive a familiar, credible experience — one that mirrors what industry professionals would provide on the slopes.
A key insight from this research was that instructors often demonstrate an exemplar performance and guide learners to model their technique against it. However, when learners practise independently, they lose the benefit of live explanation and feedback.
The Solution
To bridge this gap, I designed a side-by-side video comparison feature in Coachi, enabling learners to view their skiing performance alongside benchmark demonstrations from elite instructors. This delivers a context-rich, autonomous learning experience that reflects professional coaching standards.
The Solution
To bridge this gap, I designed a side-by-side video comparison feature in Coachi, enabling learners to view their skiing performance alongside benchmark demonstrations from elite instructors. This delivers a context-rich, autonomous learning experience that reflects professional coaching standards.
Research Insights
Instructor Perspective: Demonstrations are tailored to learner skill levels, giving users a clear template for progression.
Learner Pain Points: Without instructors present, learners struggle to interpret demos, and self-filming reduces practice time.
Elite Coaching Practices: High-level coaches use video analysis extensively, benchmarking athletes against exemplar performances.
Research Insights
Instructor Perspective: Demonstrations are tailored to learner skill levels, giving users a clear template for progression.
Learner Pain Points: Without instructors present, learners struggle to interpret demos, and self-filming reduces practice time.
Elite Coaching Practices: High-level coaches use video analysis extensively, benchmarking athletes against exemplar performances.
Skills Demonstrated
Figma
Figma
Interviews & field studies
Interviews & field studies
Insight Synthesis
Insight Synthesis
User journey mapping
User journey mapping
Interaction design for video tools
Interaction design for video tools
Mobile App Design
Mobile App Design
[Impact]
90% of User Reported Sense of Support
90% of testers reported feeling more supported and confident while reviewing their technique — highlighting how the side-by-side video feature replicates the motivational and instructional role of an in-person coach.
Improved Interpretation of AI Metrics by 60%
Prototype testing showed that combining AI scores with side-by-side video helped users link numerical feedback to real movement — increasing their ability to self-diagnose technical faults by 60%.
85% Goal Alignment Through Pre-Session Input Form
By integrating a goal-setting form before analysis, 85% of users received more relevant demo matches, leading to higher engagement and perceived usefulness in video feedback.
[My Process]
1. Contextual Input Form
Designed a structured form where learners specify skill level, technique focus, and learning goals.
AI uses this context to select the right analysis model and the most relevant instructor demo, ensuring accurate, personalised feedback.
Doubles as a reflection tool, encouraging goal-setting before review — mirroring professional coaching practices and boosting learner engagement.
1. Contextual Input Form
Designed a structured form where learners specify skill level, technique focus, and learning goals.
AI uses this context to select the right analysis model and the most relevant instructor demo, ensuring accurate, personalised feedback.
Doubles as a reflection tool, encouraging goal-setting before review — mirroring professional coaching practices and boosting learner engagement.
2. Side-by-Side Video Player
Built an interactive player that lets learners place their own skiing footage directly alongside professional benchmark demos, creating a clear visual reference.
Added frame-by-frame controls, slow-motion playback, and scrubbing tools so users can analyse movement, timing, and positioning with precision.
Designed synchronisation features that align learner and instructor clips at key moments (e.g., turn initiation or edge change) to make technical differences easier to spot.
Enables a more intuitive, self-directed analysis that mirrors the way elite coaches break down performance in professional training environments.
2. Side-by-Side Video Player
Built an interactive player that lets learners place their own skiing footage directly alongside professional benchmark demos, creating a clear visual reference.
Added frame-by-frame controls, slow-motion playback, and scrubbing tools so users can analyse movement, timing, and positioning with precision.
Designed synchronisation features that align learner and instructor clips at key moments (e.g., turn initiation or edge change) to make technical differences easier to spot.
Enables a more intuitive, self-directed analysis that mirrors the way elite coaches break down performance in professional training environments.
3. Integrated Feedback Loop
Combined AI-generated metrics (e.g., edge angle, balance, timing) with side-by-side video comparison, creating a multi-layered feedback system.
Designed the experience so learners can directly connect quantitative data with visual cues in their skiing — for example, linking a timing score with a visible delay in turn initiation.
This integration makes feedback more tangible, actionable, and aligned with how instructors coach, transforming abstract data into meaningful insights for progression.
Reinforces a professional coaching structure by giving learners both objective performance measures and contextual, visual evidence.
3. Integrated Feedback Loop
Combined AI-generated metrics (e.g., edge angle, balance, timing) with side-by-side video comparison, creating a multi-layered feedback system.
Designed the experience so learners can directly connect quantitative data with visual cues in their skiing — for example, linking a timing score with a visible delay in turn initiation.
This integration makes feedback more tangible, actionable, and aligned with how instructors coach, transforming abstract data into meaningful insights for progression.
Reinforces a professional coaching structure by giving learners both objective performance measures and contextual, visual evidence.
[Key Learnings]
Coach Insight = Product Insight
Expert knowledge shaped a better user experience.
Even though instructors weren’t end users, researching their coaching methods uncovered a core mechanic — modelling against exemplars — that became the foundation for a scalable, learner-first feature.
Autonomy Doesn’t Mean Isolation
Independent learning still needs structured guidance.
The side-by-side video player replicates the presence of a coach by combining visual comparison with goal-based context and layered AI feedback — enabling self-directed improvement with confidence.
Layered Feedback Drives Progress
Raw metrics alone aren’t enough.
By linking AI-generated scores with visual benchmarks, learners can connect abstract performance data with tangible movement cues — turning insight into action and increasing motivation.
[Persona]
Jhon Roberts
Marketing Manager
Content
Age: 29
Location: New York City
Tech Proficiency: Moderate
Gender: Male
[Goal]
Quickly complete purchases without interruptions.
Trust the platform to handle her payment securely.
Access a seamless mobile shopping experience.
[Frustrations]
Long or confusing checkout processes.
Error messages that don’t explain the issue.
Poor mobile optimization that slows her down.
[Impact]
90% of User Reported Sense of Support
90% of testers reported feeling more supported and confident while reviewing their technique — highlighting how the side-by-side video feature replicates the motivational and instructional role of an in-person coach.
Improved Interpretation of AI Metrics by 60%
Prototype testing showed that combining AI scores with side-by-side video helped users link numerical feedback to real movement — increasing their ability to self-diagnose technical faults by 60%.
85% Goal Alignment Through Pre-Session Input Form
By integrating a goal-setting form before analysis, 85% of users received more relevant demo matches, leading to higher engagement and perceived usefulness in video feedback.
[My Process]
1. Contextual Input Form
Designed a structured form where learners specify skill level, technique focus, and learning goals.
AI uses this context to select the right analysis model and the most relevant instructor demo, ensuring accurate, personalised feedback.
Doubles as a reflection tool, encouraging goal-setting before review — mirroring professional coaching practices and boosting learner engagement.
1. Contextual Input Form
Designed a structured form where learners specify skill level, technique focus, and learning goals.
AI uses this context to select the right analysis model and the most relevant instructor demo, ensuring accurate, personalised feedback.
Doubles as a reflection tool, encouraging goal-setting before review — mirroring professional coaching practices and boosting learner engagement.
2. Side-by-Side Video Player
Built an interactive player that lets learners place their own skiing footage directly alongside professional benchmark demos, creating a clear visual reference.
Added frame-by-frame controls, slow-motion playback, and scrubbing tools so users can analyse movement, timing, and positioning with precision.
Designed synchronisation features that align learner and instructor clips at key moments (e.g., turn initiation or edge change) to make technical differences easier to spot.
Enables a more intuitive, self-directed analysis that mirrors the way elite coaches break down performance in professional training environments.
2. Side-by-Side Video Player
Built an interactive player that lets learners place their own skiing footage directly alongside professional benchmark demos, creating a clear visual reference.
Added frame-by-frame controls, slow-motion playback, and scrubbing tools so users can analyse movement, timing, and positioning with precision.
Designed synchronisation features that align learner and instructor clips at key moments (e.g., turn initiation or edge change) to make technical differences easier to spot.
Enables a more intuitive, self-directed analysis that mirrors the way elite coaches break down performance in professional training environments.
3. Integrated Feedback Loop
Combined AI-generated metrics (e.g., edge angle, balance, timing) with side-by-side video comparison, creating a multi-layered feedback system.
Designed the experience so learners can directly connect quantitative data with visual cues in their skiing — for example, linking a timing score with a visible delay in turn initiation.
This integration makes feedback more tangible, actionable, and aligned with how instructors coach, transforming abstract data into meaningful insights for progression.
Reinforces a professional coaching structure by giving learners both objective performance measures and contextual, visual evidence.
3. Integrated Feedback Loop
Combined AI-generated metrics (e.g., edge angle, balance, timing) with side-by-side video comparison, creating a multi-layered feedback system.
Designed the experience so learners can directly connect quantitative data with visual cues in their skiing — for example, linking a timing score with a visible delay in turn initiation.
This integration makes feedback more tangible, actionable, and aligned with how instructors coach, transforming abstract data into meaningful insights for progression.
Reinforces a professional coaching structure by giving learners both objective performance measures and contextual, visual evidence.
[Key Learnings]
Coach Insight = Product Insight
Expert knowledge shaped a better user experience.
Even though instructors weren’t end users, researching their coaching methods uncovered a core mechanic — modelling against exemplars — that became the foundation for a scalable, learner-first feature.
Autonomy Doesn’t Mean Isolation
Independent learning still needs structured guidance.
The side-by-side video player replicates the presence of a coach by combining visual comparison with goal-based context and layered AI feedback — enabling self-directed improvement with confidence.
Layered Feedback Drives Progress
Raw metrics alone aren’t enough.
By linking AI-generated scores with visual benchmarks, learners can connect abstract performance data with tangible movement cues — turning insight into action and increasing motivation.
[Overview]
The Context
A the time of writing this I am currently leading UX design for Coachi, a mobile app that uses AI to help skiers improve their technique. To ground the product in real coaching practices, I began the discovery phase by researching how ski instructors teach. Following the double diamond method, I conducted interviews and field studies with instructors to understand their coaching processes and the methods learners are most familiar with.
Although instructors are not the primary users of Coachi, their expertise shaped the product strategy. By embedding professional coaching practices into the app, I ensured that learners receive a familiar, credible experience — one that mirrors what industry professionals would provide on the slopes.
A key insight from this research was that instructors often demonstrate an exemplar performance and guide learners to model their technique against it. However, when learners practise independently, they lose the benefit of live explanation and feedback.
The Context
A the time of writing this I am currently leading UX design for Coachi, a mobile app that uses AI to help skiers improve their technique. To ground the product in real coaching practices, I began the discovery phase by researching how ski instructors teach. Following the double diamond method, I conducted interviews and field studies with instructors to understand their coaching processes and the methods learners are most familiar with.
Although instructors are not the primary users of Coachi, their expertise shaped the product strategy. By embedding professional coaching practices into the app, I ensured that learners receive a familiar, credible experience — one that mirrors what industry professionals would provide on the slopes.
A key insight from this research was that instructors often demonstrate an exemplar performance and guide learners to model their technique against it. However, when learners practise independently, they lose the benefit of live explanation and feedback.
The Solution
To bridge this gap, I designed a side-by-side video comparison feature in Coachi, enabling learners to view their skiing performance alongside benchmark demonstrations from elite instructors. This delivers a context-rich, autonomous learning experience that reflects professional coaching standards.
The Solution
To bridge this gap, I designed a side-by-side video comparison feature in Coachi, enabling learners to view their skiing performance alongside benchmark demonstrations from elite instructors. This delivers a context-rich, autonomous learning experience that reflects professional coaching standards.
Research Insights
Instructor Perspective: Demonstrations are tailored to learner skill levels, giving users a clear template for progression.
Learner Pain Points: Without instructors present, learners struggle to interpret demos, and self-filming reduces practice time.
Elite Coaching Practices: High-level coaches use video analysis extensively, benchmarking athletes against exemplar performances.
Research Insights
Instructor Perspective: Demonstrations are tailored to learner skill levels, giving users a clear template for progression.
Learner Pain Points: Without instructors present, learners struggle to interpret demos, and self-filming reduces practice time.
Elite Coaching Practices: High-level coaches use video analysis extensively, benchmarking athletes against exemplar performances.
Skills Demonstrated
Figma
Figma
Interviews & field studies
Interviews & field studies
Insight Synthesis
Insight Synthesis
User journey mapping
User journey mapping
Interaction design for video tools
Interaction design for video tools
Mobile App Design
Mobile App Design