Faculty development
Helping colleagues work thoughtfully with AI
I work with colleagues who are figuring out what AI means for their teaching and their everyday work. That involves explaining the technology, trying it on a useful task, and making room for questions that a demonstration alone cannot answer.
Faculty supported through AI professional learning
Pages in a research-grounded prompting guide
The work
Professional learning with something to take back to work
I have designed and led AI professional learning for approximately 30 faculty across humanities, languages, mathematics, and sciences. I wrote a 30-page research-grounded guide and am helping lead faculty development through the 2026–27 school year. My work also includes briefings for admissions and college counseling colleagues, where the questions concern documents, sources, and administrative workflows.
In a session
Begin with a task a colleague actually needs to do
With Gemini and NotebookLM, I connect the explanation to a task: working through a set of sources, preparing materials, or reconsidering an assignment. We look at the context a tool needs and how to check its response. The colleague’s knowledge of the subject remains essential to deciding whether the result is useful.
Practical guidance
See, Remember, Reach, Do
My latest faculty briefing uses four questions to examine a tool: what can it see, what might it remember, which systems can it reach, and what can it do? These questions help a teacher or administrator understand what they are granting access to before using a tool with real work.
Ongoing responsibility
Support needs to continue after the workshop
A workshop introduces possibilities. Applying them raises questions about source quality, assignment design, privacy, and appropriate review. I am helping shape yearlong professional learning so colleagues have opportunities to revisit those questions as their experience and the tools change.
Connection to research
What are we asking teachers to take responsibility for?
My current research examines teacher judgment and the institutional responsibilities that accompany AI adoption. It informs how I approach faculty learning: guidance must account for the time, knowledge, and support a teacher needs to review the work.