MathMind

Search Math smarter with AI

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Inspiration

Learning mathematics online can be frustrating when you do not know the correct terminology for what you are trying to study.

Students often search for things like:

  • "integration e power x square"
  • "triangle angle thing"
  • "matrix thing with diagonal"

Traditional educational platforms usually expect clean, formal queries. We wanted to build a system that understands how real students actually ask questions. That became the core idea behind MathMind: An AI-powered math learning assistant that converts vague natural language into structured mathematical learning. Instead of forcing users to adapt to mathematical jargon, MathMind adapts to the user.

What it does

MathMind allows users to type incomplete, messy, or informal math-related prompts. The system then:

  1. Detects the mathematical topic
  2. Identifies the category and difficulty level
  3. Converts the query into properly formatted mathematical notation
  4. Explains the concept in simple language
  5. Generates worked examples
  6. Recommends resources for deeper learning

How we built it

We built MathMind using MeDo, which is a no-code platform for building full-stack applications.

Challenges we ran into

Often, we would face errors while building the app. MeDo's built-in Analyzer was really helpful for resolving those errors, and their versioning system was crucial for reverting back to the last working version.

Accomplishments that we're proud of

  • Built a system capable of understanding vague and informal mathematical queries instead of relying on exact keywords or strict syntax.
  • Successfully converted messy natural language inputs into properly formatted mathematical expressions.
  • Created an intuitive user experience where students can interact with mathematics conversationally rather than through rigid search formats.
  • Improved accessibility for beginners who may struggle with mathematical terminology but still want to learn complex concepts.
  • Turned a common frustration in education of "not knowing how to ask the question" into the central problem our platform solves.

What we learned

During development, we learned:

  • How difficult mathematical NLP actually is
  • How important structured prompting is for reliable AI outputs
  • How much UI clarity matters in educational products
  • How no-code platforms like MeDo are useful for rapid prototyping

We also learned that students value understanding more than just receiving answers. Providing explanations and examples made the experience significantly more meaningful.

What's next for MathMind

We want to expand MathMind with:

  • handwritten equation recognition
  • voice-based math queries
  • graph visualizations
  • quizzes

Built With

  • medo
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