Across higher education, conversations about AI have largely centered on policy, risk, and academic integrity, often overlooking the conditions faculty and students were already navigating before AI emerged. This interactive session starts from a different premise: AI may be less a disruption itself and more a signal of existing learning environments, asking participants to explore the question: What issues has AI served as an accelerant for, and why? Through facilitated dialogue and structured reflection, faculty will examine how institutional conditions shape help-seeking, participation, trust, and AI use, while identifying pathways for addressing those conditions in their own contexts.
Across higher education, conversations about AI have focused on policy, risk, and academic integrity. These conversations often leave unexamined the conditions faculty and students are already operating within when AI arrived.
This session begins from a different premise in that AI is a signal about the environments we have built for learning. This session is organized around a single question, shared with participants before the conversation begins:
What issues has AI served as an excellerant and why? When a student turns to AI instead of asking a professor or peer for help, that decision reflects more than convenience. It reflects what feels safe, accessible, and low-risk in a given environment.
This interactive session invites faculty to explore how existing classroom and institutional conditions shape students’ use of AI and how those same conditions influence help-seeking, participation, and trust.
The session is structured as a facilitated dialogue, opening with a live conversation between the facilitators and moving into participant reflection that focuses on the conditions rather than individuals.
This approach will then give participants a chance to better examine those conditional elements and find pathways to consider how they might address (though not necessarily resolve) them in their classes and on their campuses.
