Enhancing Customer Support with AI: Balancing Automation Efficiency and Human-Centered Interactions; Balancing Efficiency and Equity: Analyzing the Societal Impacts of AI Integration

My capstone and STS research projects investigate the impact of artificial intelligence (AI), not only as a tool for technological progress, but also as a force that shapes social, economic, and ethical landscapes. While my capstone focused on using AI to support environmental decision-making in the residential energy sector, my STS research examined how AI-driven automation affects labor markets, particularly for low-wage and marginalized workers. Though they differ in their technical scope, both projects interrogate how AI is integrated into society—and highlight the importance of considering its human consequences.
My capstone addressed a growing need among homeowners to make informed, data-driven decisions about solar energy adoption. Many potential users lack the tools and technical knowledge to estimate how solar panel installation would affect their electricity production or financial savings. To bridge this gap, our team developed an AI-powered web tool that uses GIS data from the Northern Virginia Solar Map to generate solar energy production estimates based on factors such as rooftop orientation, shading, and surface area. By providing personalized system recommendations and comparative outputs for different solar setups, the tool empowers users—especially in under-resourced communities—to participate in the clean energy transition. This tool is part of a broader effort to democratize access to renewable technologies and reduce informational inequities that often limit residential solar adoption. While the technical challenge involved data processing, algorithm selection, and interface design, our project was grounded in real-world impact: how technology can be used to support environmentally and socially sustainable decision-making.
However, while AI can be used to expand access and improve efficiency, it can also produce or reinforce deep social inequalities. My STS research explored the darker side of AI integration—specifically, how it contributes to job displacement and economic insecurity among low-wage workers. Drawing on case studies in the manufacturing and customer support sectors, I analyzed how companies like Foxconn and Bank of America implemented AI systems that led to large-scale layoffs. I used the frameworks of technocracy and post-positivism to understand how AI adoption is often framed as an inevitable or objective improvement, when in reality it reflects human decisions, priorities, and embedded power structures. Workers, especially those in routine or low-skill jobs, are frequently excluded from decision-making processes about technologies that directly affect their livelihoods. My research revealed that while AI creates new jobs in technical fields, these roles typically require advanced education and digital literacy—qualifications that many displaced workers cannot access. I also explored potential policy solutions, such as reskilling programs and ethical AI development, but found that political resistance and uneven implementation continue to limit their impact.
Taken together, these two projects demonstrate that the work of an engineer or technologist cannot be separated from the social systems in which technologies operate. My capstone showed me how AI can be used to promote environmental justice and consumer empowerment, while my STS research reminded me that these tools are never neutral—they are shaped by who designs them, who funds them, and whose interests they serve. Considering these perspectives in tandem reinforces the value of sociotechnical thinking: building systems that not only perform technically, but also advance ethical, inclusive, and sustainable outcomes. As AI continues to influence every aspect of society—from clean energy access to employment—my research affirms the need to design with people in mind, not just performance.

School of Engineering and Applied Science

Bachelor of Science in Computer Science

Technical Advisor: Rosanne Vrugtman

STS Advisor: Sean Murray