What the work is teaching me.

A recent attempt to trace the origin of a viral video became my introduction to digital folklore. I could trace the clip to a 2016 repost, but not to the person who filmed it or an original upload. The video remained culturally visible even as its provenance disappeared.

We often say that the internet never forgets. But a digital artifact can survive while its creator, context, and transmission history become difficult—or impossible—to recover. Reposts preserve content while stripping away metadata; deletions and platform changes weaken the archival record; repeated edits can make the earliest surviving version look like the original.

That gap changed the questions I wanted to ask. Rather than stopping at what a meme means, I want to understand how it acquired that meaning: who created it, which communities and institutions circulated it, how audiences reinterpreted it, and what parts of its history were lost along the way.

This is where my interests in computing education and digital folklore meet. Both are human-centered inquiries into how technology mediates knowledge, meaning, and memory—and both can benefit from technical methods without treating technology itself as the final subject.

Two weeks is not enough to produce a mature research project. It is enough, however, to experience the rhythm of research: learning unfamiliar foundations, forming questions, testing novelty against the literature, running experiments, and deciding what the evidence actually supports.

When our group began, we did not all share the same background. I created shared onboarding materials and a workshop schedule so that everyone could begin from a common foundation. Giving the team a common starting point made better questions possible.

We tested early questions against the literature and the available evidence, setting aside directions that were not novel or well supported. By the end, we did not have a publication. We had something more appropriate for two weeks: a sharper question worth pursuing, a team that had learned how to work together, and a clearer understanding of what research progress can look like.

What I remember most are the small acts behind that progress—handling logistics, sharing resources, and helping teammates protect time for focused work. The workshop taught me that research is not only about results. It is also about building the conditions in which careful work can happen.

AI Ain’t Magic was my first teaching experience at Michigan, and it was unlike the recitations and office hours I had led before. Instead of supporting one semester-long course, I helped introduce machine learning to high school students with no prior experience in the field.

I taught mini-lectures on gradient descent, probability, AI ethics, and philosophy. I also led a mock trial asking whether an AI image-generation company had violated the rights of human artists. Moving between mathematics, humanities, and social questions made one lesson especially clear: AI education is inherently interdisciplinary.

Preparing those lessons gave me a more complete view of material I thought I already understood. Four years earlier, I had been a high school student learning R in a data-science summer program. Now I was the person standing at the front of a university classroom. That full-circle moment reminded me that teaching is not the final step after learning—it is one of the ways learning continues.

Working in computing education has made me more attentive to the difference between building a technically capable system and building something that genuinely supports learning.

The questions I return to are intentionally broad: Does a tool help people understand and reflect? Does it preserve learner agency? Can educators interpret what it provides and use it responsibly?

The project is ongoing, so I am keeping its mechanisms and findings private. What I can share is the principle guiding my contribution: educational technology should make reasoning easier to engage with, not less visible.