Abstract
Both my technical project and my STS research focus on the growing role of artificial intelligence in learning environments, and more importantly, how people respond to it. Across both projects, I explore a shared problem: as AI becomes more capable of providing feedback and guidance, why is it accepted as a helpful tool in some spaces but viewed as a threat in others? This question became especially interesting when comparing athletics and education. In sports, technologies that track performance and give feedback are often embraced as a way to improve. In classrooms, however, similar tools are frequently met with concern, especially around issues like cheating and academic integrity. This contrast led me to more clearly ask: how do social and institutional beliefs about what counts as “authentic” learning shape the acceptance of AI-enabled feedback systems in athletics while contributing to resistance against similar technologies in higher education? This made me realize that the issue isn’t just about what the technology can do, but about how different environments define what it means to actually learn.
For the technical portion of my project, I worked on designing Flying Birdies, a sensor-based system aimed at helping badminton players improve their technique. The system uses motion sensors (IMUs), along with signal processing and machine learning, to capture and analyze swing mechanics in real time. It provides feedback on things like timing, angular velocity, and consistency, giving athletes insight into their performance during practice. One of the main goals of this project was to make high-quality feedback more accessible, especially for players who do not always have access to a coach. At the same time, the system was intentionally designed not to replace effort or decision-making. Instead, it acts as a tool that supports learning by helping athletes better understand their own movements and make adjustments over time.
My STS research paper builds on this idea by examining why technologies like this are widely accepted in athletics but often resisted in higher education. Using frameworks such as the Social Construction of Technology (SCOT), sociotechnical imaginaries, and black-boxing, I analyze how different groups interpret AI based on their own values and priorities. In sports, learning is already seen as something that involves feedback, repetition, and coaching, so AI fits naturally into that process. In education, learning is more often tied to individual work and original output, which makes AI feel more disruptive. The paper also looks at how these differences impact students, especially when it comes to access to support. Overall, my research argues that the way AI is understood and accepted has less to do with what the technology does, and more to do with how institutions define authentic learning.
Working on both projects together helped me see how closely technical design and social context are connected. A system like Flying Birdies is not just about sensors or algorithms,it also reflects assumptions about how people learn and what kind of support is considered acceptable. Similarly, debates about AI in education are not just about the technology, but about deeper beliefs around effort, fairness, and independence. Bringing these perspectives together showed me that designing effective technologies requires more than technical accuracy; it also requires understanding the people and environments those technologies are meant to serve.
