Week 0: Scaling Personalized Education!

Hi everyone!

I’m excited to kick off my senior research project, which combines two passions of mine: machine learning (ML) and personal finance. Personal finance is something that affects all of us, but it’s often overlooked in schools. Concepts like budgeting, saving, and investing can feel overwhelming, especially for teenagers. At the same time, I’ve always been fascinated by the power of technology to solve real-world problems. That’s what inspired me to merge these two areas and explore how AI models like GPT-4o can make personal finance more accessible and engaging for teenagers – by acting like a personalized tutor.

Why GPT-4o?

Large Language Models (LLMs) like GPT-4o are a huge milestone in the artificial general intelligence (AGI) race where we seek to build intelligence that rivals or surpasses humans. They’ve gone from simple text generators to tools that can hold complex conversations, understand nuanced questions, and even create entire websites that look like they have been designed by humans. But on their own, these models aren’t optimized for teaching subjects at a deep content level, just scratching general surface knowledge. Being a tutor is not just regurgitating information when specifically prompted, it’s about guiding students with limited initial knowledge into a path where they can learn more.

That’s where my project comes in. I’m designing a custom GPT-4o-powered assistant that can teach advanced finance concepts to teenagers. The goal is to test different strategies, how the AI is “taught” (prompt engineering) and how the content is delivered (content structure), to see what works best. This is the first time in human history we have been able to scale human-human interaction and that extends into education through human-like tutoring.

My Journey So Far

This isn’t my first dive into machine learning. I have developed an image recognition tool, Tomatgenius, which identifies diseases on tomato leaves: a project that earned me a spot at a symposium. I’ve even published and presented an ML based novel cash-flow underwriting algorithm at IEEE.

More recently, I worked on an internship where I developed a chatbot using GPT-4 that’s set to be featured on the company’s website. These experiences gave me hands-on exposure to how ML and LLMs work and showed me their immense potential.

What’s Next?

Over the next few months, I’ll be testing two distinct prompt engineering techniques and two content structure strategies. I’ll be working with teenagers to evaluate how well they learn financial concepts through this AI-powered assistant. My hope is that this research will not only make personal finance more approachable but also provide valuable insights on how we can use LLMs to personalize education.

Stay tuned as I share updates, challenges, and breakthroughs along the way. This journey is just getting started, and I can’t wait to see where it leads!