Fellowship Project

For my fellowship project, I want to build interactive virtual labs using generative AI and make them available to students through Brightspace. The idea is to use an AI tool to build simulators for departments — something students can practice on in a browser that works like the real equipment or systems — and package it as a SCORM module so it loads into Brightspace and reports scores to the gradebook automatically. That gives students hands-on practice on their own time, even when access to the physical equipment or systems is limited, and it counts toward their grade.

I’ve already built a working example to prove the approach: a virtual mixing console for our Sound and Light Technology program. I described what students needed to practice, tested it against how the real board behaves, and corrected it until it was right. I’m not a developer and didn’t buy any courseware — the AI did the building while I stayed in control of the content and what counts as a correct answer.

Through the fellowship, I’d take that same method and turn it into something other instructors can use. Almost any program that needs hands-on practice but is short on equipment or lab time could benefit — nursing scenarios, trades and shop simulations, science labs, and more. It only takes a generative AI tool and the Brightspace SCORM setup we already have, so the goal is a repeatable approach any instructor can follow, with finished labs shared through the AI Exchange for other campuses to use.

Responsible AI is built into the process. Because the AI generates the simulator and the answer logic, the instructor reviews and validates all content for accuracy and bias before it reaches students, and stays the final authority on what counts as correct. The labs are built to WCAG 2.2 AA accessibility standards, and auto-grading is designed transparently so students understand how mastery is measured, protecting academic integrity. The instructor steers the content; the AI just makes it move.

This use case is grounded in the TBR AI Literacy Competency Framework, supporting responsible and ethical AI use, applied problem-solving, and foundational AI fluency for both faculty and students.

I plan to pilot the approach in one course the following semester. The pilot will measure student practice attempts and time on task, lab completion rates, pre/post confidence and skill on the simulated equipment, and the instructor authoring time saved versus building or buying a comparable lab. Results and reflections will be documented and contributed back to the AI Exchange so the approach can be adopted and improved across disciplines and campuses.