Jun 4, 2026 Logan Bogesvang Undergraduate Intern, Spring 2026
Logan Bogesvang is an undergraduate in Quantitative Analysis of Markets and Organizations at the David Eccles School of Business. As a Digital Matters Intern at the Marriott Library, he studies how artificial intelligence is reshaping work across the U.S. labor force and within University of Utah classrooms.
Briefly describe your project and the challenges, lessons learned, and obstacles overcome. What were the professional, academic, and personal motivations underlying your project?
My primary project is an IRB-approved survey examining how University of Utah undergraduates use AI tools across academic, work, and personal contexts, and how that usage relates to school and work hours. The goal is to produce a dataset that is representative across colleges and suitable for regression analysis. The main obstacle was Institutional Review Board approval, which took eight weeks. This delayed the launch to the end of the semester, a difficult time to begin survey distribution. Even so, the survey reached over 800 responses within the first two weeks and is approaching representation targets across colleges, with Fine Arts, Education, and Mines and Earth Sciences the only groups still below threshold.
What became clear early on is that collecting responses is not the difficult part. Producing interpretable data is. Small wording decisions change how people answer. Defining a “typical week” reduced ambiguity, and structuring the survey to capture both recent behavior and a baseline estimate required multiple iterations. These decisions determine whether the data answers the question. The second lesson is operational. Most progress came from direct outreach. Faculty and students across the university were willing to support the project when asked. That support is what made early traction possible. Alongside the survey, I developed a policy brief applying occupation-level AI Applicability scores to U.S. workforce data. The two projects approach the same question from different directions. One measures observed behavior. The other measures how work is structured. My motivation is simple. When I have a question, I want an answer that holds up to data.
How did the Digital Matters internship dovetail with your academic pursuits? What interested you in applying?
Digital Matters expanded both the reach and quality of the project. It enabled distribution into departments I would not have accessed independently and provided an environment to test ideas across disciplines. That combination improved both the sample and the analysis. I met Rebekah Cummings at the student-led AI Symposium. After learning about the program, I applied because it aligned directly with the work I was already doing.
What insights have you gained as a result of your project and internship experience?
Behavioral data is difficult to measure cleanly. Usage is context-dependent, and small design decisions compound into meaningful differences in outcomes. There is also a clear gap between tool availability and actual use. That gap matters.
What would you tell potential intern applicants to help them shape their own digital scholarship project?
Do the project that feels too difficult. If the problem is specific and demanding, know that you are supported by exceptional people who will meet you in that effort. With smart and committed collaborators surrounding you, you will find yourself more capable than you expect.