The "Collaborative Wayfinding" Blueprint

Juilee Decker, Department of History
Juilee Decker, Professor, Department of History

In my museum studies courses, students learn by doing. I realized that to prepare my students, I had to actively 'get in the sandbox' myself. I went from being cautious to informed after realizing that AI is simply changing the nature of work in libraries, archives, and museums. I have pursued various professional development opportunities to learn how to approach AI in my discipline. I now view student AI use not as a cheating crisis, but as a 'wayfinding' journey, and have been working to design assignments that use AI's gaps to teach deep, critical thinking.

To adapt your teaching and guide students through generative AI like Juilee has done, follow these four simplified practices:

Find time with 2-3 colleagues to pursue professional development opportunities or share classroom use-cases. Studying as a cohort keeps you focused and prevents daily distractions from halting your progress.

Build low-pressure, networks with colleagues for live tool testing. Use simple visual self-assessments, like color-coded Post-it notes, to help participants feel safe sharing and discussing their AI anxieties.

Have students generate basic AI outputs (like a historical timeline) and critically critique what the tool leaves out. This teaches them to actively spot biases and historical omissions rather than using AI as a shortcut.

Reframe unapproved student AI use as an academic navigation trial rather than intentional cheating. Guide students using transparent syllabus conversations and restorative opportunities to redo and improve their work.

  Click here for another example of how RIT faculty have adapted their teaching methods in response to AI.