2026 ASEE Annual Conference & Exposition

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.

During the project the team tackled the challenge of forecasting ETF price movements from historical news data. They began by constructing a web scraper that collected the full text of financial articles together with key metadata (title, URL, and publication date). The scraped articles were subsequently processed with large language models (gpt‑4‑mini and gpt‑5‑mini), which assigned sentiment scores on a –10 to +10 scale and provided a brief justification for each rating. These sentiment time series were used to train a number of forecasting models, including both classical statistical approaches and deep‑learning architectures such as LSTM networks.

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.