Implementing large-scale AI solutions requires prioritizing user experience and usability to ensure adoption and effective use.

In an era where artificial intelligence (AI) technologies rapidly reshape how we live and work, the success of large-scale AI projects depends significantly on how readily people can adopt and effectively use them. While powerful algorithms and systems form the foundation of AI innovation, these tools often fail to deliver their promised benefits unless they account for real-world users—each with their own needs, habits, and potential hesitancies. My coursework in ITEC 444 (Human-Computer Interaction) underscored the importance of designing with the user in mind, equipping me with principles and methods that highlight usability, cognitive load management, and iterative feedback. Later, I had the opportunity to apply these lessons when I served as the technical lead for a pilot deployment of Microsoft Copilot AI to a group of 150 faculty and staff. Together, these experiences showed me that cutting-edge technology only gains traction when people feel comfortable, informed, and supported in integrating it into their daily workflows.

During my time in ITEC 444, I delved into foundational concepts of user-centered design and usability testing. We studied Nielsen’s Usability Heuristics, which is general principles of digital interface design, focusing on core elements like clarity of navigation, user control, and error prevention. Through hands-on assignments, I observed the immediate impact of various design decisions on potential user frustration—from overly complex layouts to poor error messaging. We conducted usability tests that revealed common issues such as disorganized interfaces, unclear visual hierarchies, and high cognitive load for first-time users. These activities cemented my appreciation for systematic user feedback, showing me that continuous testing and refinement are crucial for achieving a smooth user experience.

Soon after mastering these usability concepts in class, I applied them in a real-world setting while implementing Microsoft Copilot AI for a pilot group of 150 faculty and staff. It was during my first semester of my senior year working with Division of IT at USC. As the student technical lead, I oversaw the development and execution of an Awareness Plan, which included distributing newsletters, crafting feedback surveys, and analyzing usage analytics. The biggest hurdle was not the technology itself, but overcoming the unfamiliarity and hesitance many users felt toward AI. By actively collecting feedback and adapting our approach—whether by simplifying training materials or offering hands-on workshops—I witnessed firsthand how design and usability principles could mean the difference between a successful pilot and a product that failed to take off.