AI Professional Development
Faculty, staff, and students continue to ask the same question: What does AI mean for teaching and learning? While no single workshop or tool can answer that in full, thoughtfully designed professional development can create the space for educators to explore possibilities, raise concerns, and imagine new futures for their practice.
Moving Beyond Tools to Conversations
Too often, professional development around technology becomes a demonstration of features: “click here, drag there, export this.” AI demands something more. Because it touches on assessment, ethics, creativity, and equity, professional development needs to foreground dialogue. The most powerful sessions are not about showing what a tool can do, but about asking: How does this change the way we teach? How does this alter what we value in student learning?
By reframing workshops around inquiry, faculty can collectively surface hopes, worries, and use cases that feel authentic to their disciplines. This shifts the conversation from adopting a tool to reconsidering pedagogy.
Professional Development as a Bridge
AI professional development serves as a bridge between rapid technological change and enduring educational goals. For example, a session on AI in writing instruction is not just about text generation. It becomes a chance to revisit fundamental questions:
What do we mean when we say students should “develop a voice”?
How do we assess originality in an age of ubiquitous machine assistance?
Where is the line between appropriate support and academic dishonesty?
In this way, AI becomes less of an intrusion and more of a catalyst—forcing reflection on long-standing teaching practices that may have gone unquestioned.
Fostering a Culture of Shared Experimentation
When done well, AI professional development fosters a culture of shared experimentation. Faculty members who might otherwise feel isolated in their exploration of new technologies can exchange strategies, pilot assignments, and debrief challenges together. This collective inquiry reduces the pressure to “get it right” immediately and instead normalizes iteration.
Moreover, AI professional development that includes students—whether through panels, co-facilitation, or feedback sessions—expands the conversation to include the very voices most impacted by pedagogical shifts. This not only strengthens trust but also ensures that teaching and learning innovations are responsive to real student experiences.
From Skills to Shifts
When educators gather to learn about AI, they are really gathering to ask: What do we want teaching and learning to look like in this new context?