UPenn GSE Learning Analytics and Artificial Intelligence, M….

My understanding of learning changed during a moment of global uncertainty. When I entered university after high school, I was excited about the future and still viewed education as something that naturally happened in classrooms. Then the pandemic reshaped everything. For nearly two years, my college life unfolded entirely through a screen. I attended lectures from my bedroom, completed assignments alone, and often wondered whether I was truly learning or simply keeping up.

I remember repeatedly asking myself, Am I actually growing through this experience?

Without physical classrooms or peer interaction, my concentration weakened and my motivation faded. At first, I blamed myself for lacking discipline. Over time, however, I realized that the problem was not only personalit was structural. The online learning environment had been designed for access, not necessarily for understanding.

Instead of giving up, I began to observe myself as both a learner and a subject of study. I tracked when I focused best, how long I could sustain attention, and which study methods worked or failed. I reorganized my learning routine, changed how I consumed lecture content, and experimented with review cycles that matched my natural rhythms. Slowly, learning became manageable again. More importantly, I discovered that when the structure of learning changed, my behavior and confidence changed with it. This experience sparked a question that continues to guide me today: How does the design of a learning environment shape the way people think, act, and grow?

When in-person classes resumed, my undergraduate studies at the University of Washington iSchool gave language and theory to the questions I had been living through. Surrounded by students from different disciplines, I learned to see technology not simply as a tool, but as a system that influences human understanding and decision-making. I noticed that the same information could feel empowering or overwhelming depending on how it was organized and presented. These moments convinced me that learning experiences are not accidentalthey are carefully constructed. Technology should not lead learning; it should support it.

My professional experiences in user-centered design deepened this realization. I designed features and interactions to solve user problems and improve satisfaction. While this work was rewarding, I gradually felt something was missing. I had originally wanted to use technology to connect people in meaningful ways, yet I began to question whether the systems I built truly supported long-term understanding and growth. I could measure usability, but not learning. This gap left me searching for a more purposeful direction.

That search led me to think about those who struggle most to make sense of rapid technological change. I found myself drawn to younger learnersstudents who grow up surrounded by information and AI but often lack guidance in how to interpret it. They do not just need faster tools; they need learning environments that help them think critically, reflect, and find direction in a world that changes faster than they can process.

As AI became increasingly present in daily life, another concern emerged. While AI can personalize learning and expand access, it can also encourage passive dependence if poorly designed. I observed people turning to AI not only for homework, but also for emotional reassurance and decisions about their lives. These moments made me realize that the most important question is not what AI can do, but how and why it is used in learning contexts. The intention behind AI design determines whether it deepens understanding or weakens it.

Through these reflections, I came to believe that meaningful educational impact must be grounded in evidence, not intuition alone. To truly support learners, we must understand where they struggle and what kinds of interventions help them grow. This requires the ability to analyze learning data and interpret patterns of engagement, confusion, and progress. It also requires a thoughtful integration of AI into learning environments that respect human cognition and emotion.

This conviction is what draws me to the Learning Analytics and Artificial Intelligence program at UPenn GSE. Courses such as Core Methods in Educational Data Mining will equip me with the tools to transform learner interaction data into insights about how students learn. Adaptive Learning Systems will allow me to explore how AI can be used to create personalized learning paths that respond to individual needs rather than impose one-size-fits-all solutions. These courses resonate deeply with my own journey of reconstructing learning during the pandemiconly now, I seek to approach that process systematically and at scale.

Through this program, I hope to bridge my background in user-centered design with analytical methods that measure real learning outcomes. I want to design learning environments that do more than function smoothly; I want them to guide learners through confusion toward understanding.

Ultimately, my goal is simple but deeply personal: to help learners feel less lost in times of change. The pandemic taught me what it feels like to study without structure and direction. UPenn GSE offers the opportunity to transform that experience into purposeto become someone who designs learning systems that support clarity, growth, and resilience. I believe this program will prepare me to create educational experiences that honor both human needs and technological possibility, ensuring that learning remains a meaningful journey even in an age shaped by AI.