Human-AI Co-Mentorship in Project-Based Learning: A Case Study in Financial Forecasting
2026-05-06 • Machine Learning
Machine LearningComputers and Society
AI summaryⓘ
The authors describe a project where high school and early college students learned about AI and finance by actually doing a research project with help from AI tools and mentors. Instead of learning theory first, the students figured out the steps they needed to solve a problem about predicting ETF prices and used AI tools to write code and analyze data. The mentors helped by answering questions and debugging during daily meetings. The authors say this approach helped the students learn better by focusing on deeper understanding and their interests, even though they started with little background in AI or finance.
AI toolsETF price predictionmarket analysisworkflow designtechnical mentorshipcoding iterationconceptual understandingfinancehigh school educationcollaborative learning
Authors
Freyaa Chawla, Ahan Chawla, Rishi Singh, Joe Germino, Grigorii Khvatskii
Abstract
This paper reflects on a AI research project carried out by a team of high-school and early-undergraduate students under the mentorship of graduate researchers and ably assisted by AI tools. We share our experience in not only on the learning experience for the high school students, but also on how AI tools accelerated the process that enabled the high school students to focus on higher order problem formulation and solution. Although the participants entered the project with limited background in both AI and finance, they showed strong enthusiasm for technical market analysis and ETF price prediction. Traditional learning settings would first teach the necessary methods in a classroom setting and only later let students apply them. In contrast, our project emphasized workflow design: students identified the sequence of steps needed to address the problem and then used AI-driven tools to execute each step. We note that the high school students developed the necessary code through iterating with the AI tools, and we used our daily stand-ups to debug and answer conceptual questions. Each of the student was able to dig deeper into their area of interest whether computer science or finance, while collaboratively making a significant advance over the summer of 2025. This project was an important pedagogical exercise on how AI tools can be used for mentoring high school students, allowing them to focus on their specific interests and using the daily stand-ups to focus on problem definition and conceptual understanding. Despite their limited technical qualifications, the students were able to leverage AI tools to build meaningful models with real-world application.