Sophia Lin Introduction 2/7/2025
Sophia L -
Hi everyone! My name is Sophia Lin, and I’m a senior at BASIS Scottsdale. In this blog, I’ll be documenting my Senior Research Project over the next few months. At BASIS Scottsdale, seniors spend their last trimester interning and performing research, ending with a presentation in May. Specifically, my project will be focusing on using artificial intelligence and machine learning to help reduce high school dropout rates in Arizona and America.
Before I start talking about my project, I’ll introduce myself and my interests briefly. I have always been very interested in public service, specifically public policy and government. I’m really interested now in the intersection of technology and policy and how artificial intelligence can be used both ethically and efficiently. I have a lot of hope for the future of government if technology like artificial intelligence can be incorporated correctly. Having worked closely with the Arizona State Board of Education, I have witnessed how much high school dropouts affect students across the state. That is why I now aim to use state-of-the-art technology to help promote specialized policies.
For a little bit more information about my project specifically, here is a brief introduction. High school dropouts pose a significant problem for America, not only affecting the individuals themselves but also the country’s economic, social, and civic well-being. Numerically, each high school dropout costs the United States economy $272,000, and while the national status dropout rate has been slowly decreasing in the past 20 years, it is still alarmingly above 5%. When students fail to complete high school, they are more likely to face limited job opportunities, resulting in lower wages and higher unemployment rates compared to their peers with diplomas. Professors Ming Zhao and Huolong Zhuang and the Arizona State University School of Computing and Augmented Analysis provide me the assistance and resources to code an AI model using RNN (Recurrent Neural Network) and Python code through a LSTM (Long-Short Term Memory) network and train it by feeding it data from the NCES (National Center of Education Statistics). After learning the patterns of the data, it will be able to identify the most significant predictors of dropouts and be able to predict dropout rates 10 years into the future. Based on our findings, we will discuss how to focus on at-risk factors and how corresponding intervention programs and policy choices need to be adjusted to improve the high-school graduation rate more efficiently, including reaching out to the areas at high risk, planning government budgets, and increasing awareness of dropout prevention resources for families and educators.
I’m just getting started on my project, but I’ll be sharing more details soon. Every week, I’ll post updates on my work in the lab and the progress I’ve made. If you’re interested in seeing what my classmates are working on, check out their blogs linked on the right. See you in next week’s update!
Thank you for reading,
Sophia Lin
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