Space Debris Tracking CubeSat; OpenAI’s Influence on User Perspectives of Large Language Models

Introduction
My Capstone project and STS research both explore critical interactions between advanced technologies and their human implications. Although seemingly distinct in context—one focused on orbital debris tracking through CubeSat technology, and the other analyzing user interactions with artificial intelligence platforms—both projects emphasize how technological design significantly shapes human experiences and responses. My motivation for pursuing these projects was rooted in understanding not only technological solutions but also the broader societal and ethical implications these technologies entail.
Capstone Project Summary
The increasing accumulation of space debris in low Earth orbit (LEO) represents a substantial risk to current satellite infrastructure and future space endeavors. To mitigate this threat, our team aimed to design and prototype a radar-based sensor for a CubeSat platform capable of detecting debris smaller than 10 centimeters, which remain untracked by current systems. Initially aiming to advance our sensor technology from Technology Readiness Level (TRL) 1 (basic principles observed) to TRL 3 (proof of concept), our focus shifted significantly towards perfecting the sensor subsystem using continuous-wave radar technology operating at 30 GHz. Extensive simulation models were developed to predict debris distributions, optimize satellite orbits for maximum ground station communication, and validate radar sensor performance. However, during prototype testing, environmental noise, hardware limitations, and interference hindered conclusive demonstration of the radar’s capability. Despite these setbacks, crucial insights emerged, especially regarding discrepancies between theoretical and experimental radar cross-section values, thus refining our simulations for future missions. This project laid significant groundwork in radar technology and simulation methodologies, essential for continued research in orbital debris tracking.
STS Research Paper Summary
As artificial intelligence increasingly integrates into daily life, understanding how technology companies shape user perceptions becomes imperative. My research employs the Social Construction of Technology (SCOT) framework to analyze how OpenAI strategically influences user perspectives toward its Large Language Models (LLMs). Drawing parallels between LLM algorithms and effective content-matching platforms, such as The New York Times' word games, the study emphasizes user engagement through alignment with user intent. Three specific case studies illuminate these interactions: OpenAI’s Super Bowl commercial, illustrating strategic narrative framing to position AI as transformative; embedded biases within OpenAI’s training methods influencing user expectations; and graphical user interface design fostering user trust and usability. This analysis underscores the necessity of critical user awareness regarding these persuasive strategies, empowering more informed, ethical, and democratic user interactions with AI technologies.
Concluding Reflection
Working simultaneously on these projects significantly enriched my understanding of how technical innovations interface with societal impacts. The direct engineering challenge of designing an orbital debris-tracking radar provided practical insights into solving real-world problems with tangible technical constraints. Conversely, the STS research sharpened my ability to critically examine how seemingly neutral technological decisions inherently carry social and ethical consequences. Integrating these perspectives taught me that effective engineering must always be paired with conscientious consideration of human impact. The combined experience significantly enhanced my analytical, technical, and ethical reasoning abilities, preparing me to approach future challenges in aerospace engineering and technology management with both technical proficiency and ethical awareness.