​Capture Math Mind
Product Overview
Capture Math Mind is a mobile prototype that supports college students in mastering advanced math concepts through interactive, AI-generated games. The tool transforms problems like Bayes’ Theorem into detective-style story games tailored to each learner's level, helping students understand not just the answers—but also the why behind them.
My contributions: I led prototype design, built the core GenAI interactions using My GPT, and aligned gameplay mechanics with cognitive learning theories such as situated learning and retrieval practice.
Tools:
Figma​​
ChatGPT
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Team
​Fuqing Ye
Yingqi Wu
​Yiwei Liu
Audience
College students who find advanced math concepts hard to grasp, especially non-STEM majors.
Product Design Process
01
Empathize & Define
We started by identifying common struggles college students face in learning probability and statistics — especially abstract ideas like Bayes’ Theorem. Many students lacked engagement, found traditional problem sets too dry, and struggled to transfer concepts into real situations.
02
Define
We defined our core problem as:
"How might we help students make sense of complex math through more engaging, meaningful, and interactive experiences?"
We focused on designing a tool that could:
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Lower the entry barrier to hard math concepts
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Provide instant and adaptive feedback
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Embed learning in relatable or playful scenarios
03
Ideate
We explored a range of solutions — from card games to simulations — and decided to build a detective-themed GenAI math game. Each "case" would be powered by prompts that break down a math problem into:
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A story-based setup
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Player choices (aligned to mathematical reasoning)
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Feedback and reflection moments
I contributed to brainstorming the game format, refining use cases, and mapping user flow across different learner types.​​​​​​​​​​​​​​
04
Prototype
👉
We built a working prototype in Figma, with AI-generated examples and gameplay simulations.
My work included:
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Drafting GenAI prompts for math storytelling
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Creating question logic for adaptive learning
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Reviewing UI for clarity and accessibility
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Connecting each story with a follow-up summary and reflection task
05
User Testing & Result
We ran internal testing with peers and iterated based on feedback. Key insights:Users loved the story format and found it motivatingSome confusion existed around math clarity in branching pathsWe improved layout spacing, added more explicit instructions, and simplified choice designs
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A playful and personalized GenAI prototype that helps students “solve” math through mystery. This project showed how theory-driven prompting and intentional design can unlock deeper learning with AI.