A Financial Literacy AI-Enabled Voice Assistant System for Educational Use; An Actor-Network Theory Analysis of the Financial Literacy Gap in the U.S.

My technical work and STS research focused on the topic of financial literacy. Financial literacy refers to the skills, information, and tools that enable people to make individual financial decisions and actions in order to achieve their objectives. Both papers addressed the need for improvement of American financial literacy rates; however, they differed in purpose. My technical paper detailed the steps that my capstone team took in order to build a virtual voice assistant system that aimed to improve the financial understanding of K-12 students in the US. My STS research paper explored deeper into the root causes of why certain populations within the US are more vulnerable to financial illiteracy. Each work diverges into different routes and takes on separate intentions, but together they both address the broader goal of improving financial literacy levels in the US and reaching financial inclusion for all.

My technical work focused on the educational side of financial literacy; our proposed solution was an AI-enabled voice assistant system that aimed to improve the financial literacy skills of students. The three main phases of this project were lesson plan content generation, cloud platform implementation, and system evaluation. For the first phase, we consulted the National Standards for Personal Finance Education to see what students were expected to learn and used that as a guideline to structure our own lesson plans for various topics regarding personal finance. During the second phase, we chose Google Dialogflow as our preferred cloud platform, which we used to implement a chatbot that would guide the user through each lesson plan. The voice assistant was then connected to a phone number, where students could call anytime and choose which lesson they wanted to begin. The lesson plans were eventually all moved into Dialogflow, with new conversational flows created for each one. Finally, our final phase was used to test the robustness of our system; we did this by calling the designated number a certain number of times and gathering the data from the phone calls to analyze our results.

My STS research explored the financial literacy gap between different demographics within the US, and why these gaps exist. Using Actor-Network Theory as the conceptual framework, the paper analyzed and identified the human and non-human actors that contributed to the American financial system and connected them to systemic barriers that hinder marginalized groups from reaching financial success. It then described the financial barriers for two marginalized populations, women and people of color, and concluded by stating that the common denominators of these barriers are that they are systemic injustices that have been created by those who built the American financial system themselves. The goal of my research was to shed light on the determinants of the financial literacy gaps, as well as to promote diversification among the governing bodies within the financial system so that minority groups are better represented.

Working on my technical project and STS research concurrently added significant value to both. My technical work allowed me to build out a concrete solution that helped to achieve a goal that was similarly set out by my thesis, while my STS research provided insight into the social, political, and economic factors that play into the central topic that we built our voice assistant off of. Together, both helped me to gain a better understanding of financial literacy in both a broader and more detailed sense. Additionally and perhaps more importantly, both projects gave me the passion and motivation to continue on the path of finding ways to act upon injustices and advocating for change that will improve the well-being of all.

School of Engineering and Applied Science

Bachelor of Science in Systems Engineering

Technical Advisor: Tariq Iqbal

STS Advisor: Benjamin Laugelli

Technical Team Members: Jesilyn Gopurathingal, Matthew Thompson, Vineeth Thota, Niels van Beek