What kind of AI projects do students build at BetterMind Labs?

Students build real-world AI projects such as predictive models, data analysis tools, recommendation systems, and socially impactful AI applications. Every project is designed to solve a real-world problem, helping students understand how AI is applied beyond theory.

What is a passion project in AI?

An AI passion project is a student-driven project where learners apply AI concepts to a topic they genuinely care about. These projects demonstrate curiosity, initiative, and depth, allowing students to explore AI in areas aligned with their academic or career interests.

Are these projects suitable for college applications?

Yes. BetterMind Labs projects are well-suited for college applications. Many students use their AI projects in college essays, portfolios, research supplements, and interviews to showcase originality, problem-solving skills, and technical depth.

Do students work on individual or group projects?

Most students work in small groups, allowing for collaboration while maintaining individual ownership and originality. Mentors ensure each student has a clearly defined role and can articulate their personal contributions.

Can students publish their AI projects?

Yes. Students are encouraged to publish their projects on GitHub, personal portfolios, or websites, helping them build a public profile and demonstrate their work to colleges and future opportunities.

How are projects evaluated?

Projects are evaluated holistically based on problem understanding, learning process, implementation quality, creativity, and real-world impact. The focus is on growth, depth of understanding, and meaningful application rather than rote performance.

Student's AI Projects | BetterMind Labs 2/32

Projects built by our Students

Explore our Live Projects

At BetterMind Labs, our students choose a problem they care about and turn it into a Passion Project.

With a dedicated mentor guiding them at every step they’ll build a project that reflects their passion, not just another generic activity.

The result is work that feels authentic, meaningful, and strong enough to set you apart in applications.

Apply for Consideration

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Student Projects

AI & ML Projects developed by our students

How we choose Projects & Teams?

Aniket Kumar

AI + Finance

Next-Day Stock Price Prediction

AI system that analyzes market data and sentiment to predict next-day stock prices for smarter trading decisions.

See Working on YT

See Documentation

Harinii Ramiah

AI + Finance

City Cost AI Agent

City Cost AI Agent is my project, which is a AI-based tool which helps people compare the cost of living in different U.S. cities. Depending on where you live and the lifestyle, it'll compare the cost. It uses actual data and machine learning technology to show how expensive a city is and explain what factors such as the rent or the food affect the cost. People are usually confused on where to move, and find mixed opinions on different websites as to which city to move. I created this project City Cost AI Agent hoping to solve this problem.

See Working on YT

See Documentation

Rushi Shah

AI + Finance

Budget Buddy AI

An intelligent personal finance assistant that categorizes expenses, suggests saving strategies, and helps users stick to budgets.

See Working on YT

See Documentation

Student's Project

AI + Finance

Portfolio Risk Analyzer

Quantify investment risk for a user’s asset mix. Computes portfolio VaR (Value at Risk) and visualizes risk over time.

See Working on YT

See Documentation

Ananya Gangwar

AI + Finance

Finance Buddy

AI system that tracks expenses, analyzes spending patterns, and offers insights for smarter financial management.

See Working on YT

See Documentation

Annika Malik

AI + Finance

Able Finance

AI-driven financial planning assistant that helps manage investments, track spending, and optimize savings.

See Working on YT

See Documentation

Impact first practices we use

Our Project Philosophy

Problem First

pick a real user problem or community need.

Research & Brief

Students interview users, collect data, and write a brief that states impact goals.

Deliver & Measure

final deliverable (app, report, model, demo) + simple impact metrics.

How We Form Teams

Step 1

Student Selection Based on Profile

~12 students who are interested in
finance, healthcare, finance and other domains.

Step 2

Grouping 12 Students by Major

Computer Science

Olivia Reddy

Noah Mehta

Emily Sharma

Liam Patel

Ava Nair

Ethan Kapoor

Business/Finance

Sophia Iyer

Jackson Verma

Sanya Nair

Neel Kapoor

Healthcare

Maya Iyer

Aarav Patel

Step 3

Team formation based
on skillset

We divide students into teams of 2 to 4 members, and assign a different mentor to every team, ensuring that no mentor is responsible for more than two teams.

Prior Programming Knowledge

Prior
Programming
Experience

Some Programming
Experience

No Programming
Experience

Team 1: Olivia Reddy, Noah Mehta

Team 2: Emily Sharma, Liam Patel

Team 3: Ethan Kapoor, Ava Nair

Step 4

Selecting Personal Mentors according to the teams

Mentor 1: Aman

Generative AI Expert