T127 Final Reflective Post

Looking back across the semester, my portfolio tracks a shift I didn’t expect to make. I started as a solutionist who reaches for AI first and ended as a designer who sits with the problem longer than feels comfortable.

My first reflection admitted I gravitate toward edge cases, especially newer and more experimental AI capabilities in learning. What I didn’t see at the time was the cost of pairing that instinct with a startup mindset of moving as fast as possible. In education, the five Ds aren’t equally weighted, and discovery is where I was cutting corners. I tended to use AI as a solution before checking whether there was a real problem to solve in the first place.

The data fluency team taught me what bottom-up actually costs. After what felt like a long and arduous discovery phase, we found that the problems Harvard staff were facing were very different from what we and the higher-ups had assumed the data fluency problem to be. That gap between the assumed problem and the surfaced one is the clearest piece of evidence I have that the semester changed how I work. I now understand that the concepts from class only become useful once enough data points from actual users are on the table. Otherwise I’m applying frameworks to a problem I made up.

This shaped my capstone. Working with adult learners, I wanted to cultivate awe and tap into intrinsic motivation, surfacing curiosity and a feeling of contribution that traditional digital learning tends to flatten. Generative UI as a use case is still very early, and finding the right models and tech stack required a lot of backward design so that what I built was something users actually needed, not something I was excited to build.

The portfolio itself became the source of truth for that process. It records how I thought about the data fluency course and how my thinking moved as I learned new concepts. In the spirit of visible thinking, I’ll keep using it the same way: as a place to showcase the artifacts I make in this class and others, so that the reasoning behind them is legible to me and to anyone learning alongside me.

For professional use, artifacts are what I want external viewers like employers and collaborators to see. They show what I can actually do, not just what I claim. This is also why I’m building bukti.ai, currently in pilot. It’s a platform where graduates can publish portfolios of artifacts and have their capabilities verified, so that employers can query whether someone can actually do something rather than infer it from a résumé. My own portfolio from this course is the first proof case. I plan to spread this use of portfolios further with my cohort and beyond.