Is Math even useful IRL? Problem Recontextualization+Solving

Transform traditional math problems into real-world scenarios tailored to students' future career aspirations using Wolfram & ChatGPT4. Motivate and deepen understanding by contextualizing learning.

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Link to Submission:

https://drive.google.com/file/d/10Vd5DmaDslP3jIkdNRkti9VYUmyVpwxb/view?usp=share_link

Inspiration

Ever found yourself slogging through a math problem, wondering, "When will I ever use this in real life?" The gap between abstract mathematical concepts and real-world applications often feels vast. This disconnection can hinder motivation, especially for university students on the cusp of entering the workforce. The idea was to bridge this gap, making math problems more relatable, engaging, and relevant to students' future aspirations.

What it does

This use case transforms conventional math problems into personalized, industry-specific scenarios using Wolfram Alpha and ChatGPT4. Students can input their desired future job or industry, and the system will recontextualize the problem to fit that domain. Whether it's quantitative trading, sustainable farming, or any other field, students get a glimpse of the mathematical challenges they might face post-graduation, all while receiving personalized tutoring.

How we built it

We leveraged the computational prowess of Wolfram Alpha combined with the conversational capabilities of ChatGPT4. By crafting specific prompts that instruct ChatGPT to consider the user's desired future industry, we ensured problems are transformed in a relevant context. A series of iterative tests and refinements helped in optimizing the user experience, ensuring clarity and accuracy in problem transformation.

Challenges we ran into

  1. Ensuring the transformed problems retain their mathematical integrity while being relevant to the chosen industry.
  2. Overcoming occasional discrepancies between solutions provided by Wolfram Alpha and results from Sage Math.
  3. Crafting prompts that are concise yet comprehensive, given the token limitations of ChatGPT.

Accomplishments that we're proud of

  1. Successfully bridging the gap between abstract math and its real-world applications.
  2. Providing a dual benefit of mathematical understanding and industry-specific insight.

What we learned

  1. The power of context in learning: Tailoring problems to a student's interest can significantly enhance engagement and comprehension.
  2. The nuances and intricacies of integrating multiple AI tools to achieve a unified goal.
  3. The importance of continuous iteration and refinement in achieving an optimal user experience.

What's next for "Is Math even useful IRL? Problem Recontextualization+Solving"

  1. Expansion to cover more industries and domains, ensuring even broader relevance.
  2. Integration with other AI tools to offer even more personalized learning experiences.
  3. Feedback loops to refine and improve problem contextualization based on user feedback and industry advancements.

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