GameSense

UI/UX Project

Duration:
3 Month + Ongoing

Tools:


gOALS
‍‍Design a system that translates gameplay into clear, actionable improvement steps.

Problem Statement


Players struggle to understand why they lose and how to improve.Existing tools provide stats, but lack actionable feedback.

This project explores how gameplay data can be transformed into clear, actionable feedback that guides player improvement.

Viability

‍70% of gamers express interest in tools that can help them improve performance, as per surveys in competitive and casual gaming communities. "Source"

Platforms like Twitch and YouTube Gaming have grown by over 75% in viewership in recent years. Content creators need tools to automate and enhance highlight generation and overlays, a feature GameSense AI can offer. Source

Around 65% of gamers report they would spend time on personalized training to improve their skills if given the opportunity, GameSense AI’s personalized training modules align with this demand, creating a clear value proposition.

Online polls

Polls were crucial to the development of the direction on this application and trying to intergrate the paradox of specificity.

How often would you use an AI coach to improve your gaming skills?
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Which feature would you find most valuable in an AI coach?
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Concolusion
The results narrowed down the research topics, these topics will have to be further researched to find if other application offer this service and to what degree could we improve if nesssery on existing systems.

Competitive Benchmark

To get an idea of the Space we were entering into we did some competitive benchmarking with a SWOT analysis. this helped with insites on were to aim our efforts, what areas to build on and features that may need growth in order to incapsulate the market.

Research Synthesis

Key Insight 1
Players rely on guesswork to improve because feedback is unclear or missing.
Implication: The product must clearly explain why mistakes happen
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Key Insight 2
Stats are not actionable.
Users see data (kills, accuracy)But don’t know what to do with it
Implication: Translate stats into specific actions
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Key Insight 3
Improvement lacks structure.
No clear path from gameplay. Training is disconnected from performance
Implication: Create a guided improvement loop


How this shaped the product
Introduced Video Coach to analyse gameplay and highlight mistakes
Added targeted training recommendations based on performance
Designed a Play - Analyse - Train - Improve loop to guide behaviour

Design Decisions

1. Prioritise actionable feedback over raw statistics
Players were exposed to performance data but lacked clear direction on how to improve.
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‍Decision: Replace stat-heavy views with insight-driven feedback
Result: Users are guided toward specific actions rather than interpreting data themselves
‍2. Introduce a structured improvement loop
Improvement was fragmented, with no clear connection between gameplay and training.
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‍Decision: Design a clear loop — Play → Analyse → Train → Improve
Result: Creates a repeatable system that supports continuous skill development
‍3. Focus the dashboard on a single primary action
Initial concepts presented multiple equal features, increasing cognitive load and decision friction.
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Decision: Surface one key insight with a primary CTA (“Fix this now”)
Result: Reduces overwhelm and directs users toward immediate improvement
‍‍4. Translate gameplay into targeted training
Training tools were disconnected from actual player performance.
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‍Decision: Link gameplay analysis directly to recommended drills
Result: Ensures training is relevant, personalised, and outcome-driven
‍5. Surface mistakes, not just performance metrics
Players need to understand why they fail, not just how often.
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‍Decision: Highlight specific mistakes through Video Coach
Result: Provides clarity on behaviour, enabling faster learning

Supporting Design System


A lightweight design system was created to support consistency and reinforce a clear user flow across features.

Lo-Fi Prototype



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design flaws and solutions

When developing these low-fi designs of the onboarding process i came across some flaws i wanted to build solutions to fix, these included the colapsable menu. while i thought this was a good idea at the time for a product with multiple features, this design choice just wasnt as usefull as i wanted it to be, i screpped this for the tab toolbar menu, stylised to fit the products personallity.

Secondly was the format of the feature butons the square buttons were to clunky and unnessesery, yes they were an easy tap target, but these types of butons made it hard to expand on more features if needed and just did not fit the feal of the product. in this case o opted for a more sleek bar buton system with smaller icons.


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Hi-Fi Iterations
Onboarding Process


Final UI focuses on surfacing key insights and guiding users toward actionable improvement.