Makerspaces are built on a simple idea: the best way to understand something is to build with it. The maker ethos is about learning by doing. You start with a question or a problem, try something, see what happens, and improve it. Mistakes are part of the process. You learn as much from a failed attempt as from one that works, and you share what you figured out so the next person gets further. Campus makerspaces have long used this approach for 3D printers, electronics, and fabrication tools. They give people access to equipment they couldn’t get on their own, and a place to use it alongside others.

The AI Makerspace brings that same approach to artificial intelligence. AI tools are changing quickly, and reading about them only goes so far. Most people learn what these tools can do, and where they fall short, by trying them on their own work: a research question, a course assignment, a data set, or a task that takes up too much of their week. The Makerspace gives you the time, equipment, and support to do that hands-on work.

We also want it to be a safe place to experiment. In-room workstations can run AI models locally, so you can try new tools on real data without that data leaving campus. Engineering IT staff are available to help you get started, troubleshoot when things don’t work, and decide which tools fit your needs. Workshops and collaborative pod seating mean you don’t have to figure it out alone.

Whether you’re trying AI for the first time or building something ambitious, the Makerspace is a place to tinker, test ideas, and turn what you learn into practice.

What We Heard

This fall, 194 students, faculty, and staff answered our AI Makerspace interest survey: 107 students, 40 faculty, and 47 staff. Here’s what they told us.

At a glance

• 194 people responded
• 70% rated their interest in the AI Makerspace a 4 or 5 out of 5
• 85% of faculty rated their interest a 4 or 5, the highest of any group
• Hands-on, in-person was the top workshop format for students, faculty, and staff alike

Students already use AI often. About two-thirds use ChatGPT or Gemini at least weekly. They want to go beyond chat: machine learning fundamentals (55%), AI coding assistants (52%), building chatbots and agents (51%), and data analysis (51%) were their top workshop requests.

Faculty are most interested in using AI for research. About two-thirds already use it there, and 63% want a workshop on AI for literature review and data analysis. Teaching and course design, and building custom AI tools for courses, followed at 50% each. Their biggest concern is accuracy: 73% are worried about AI hallucinations.

Staff want practical skills they can use right away. Their top requests were AI basics and prompting (70%), building simple no-code workflows (66%), and automating repetitive tasks (64%). Data privacy and policy concerns were the biggest barrier for staff (51%), followed by not knowing where to start (32%). Lunchtime sessions work best for 77% of them.

What this means for the space: Across all three groups, people want hands-on time with AI, guidance on using it accurately, and assurance that their data is protected. That’s why the Makerspace is built around in-person workshops, in-room workstations that can run models locally, and staff support.