Centering perspectives based in refusal has been the podcast's primary purpose, both to connect refusal to disciplinary trajectories and to showcase that, as with uptake and integration, there are a wide range of responses rooted in refusal. We want to engage in critical conversations about the consequences of generative AI incursion into writing classes and across various campuses, institutions, and disciplines. And we want to learn everything we can from writers, teachers, and scholars who have been thinking about writing, communities, technologies, and literacy. More than ever, it feels imperative that writing teachers and scholars think hard and deeply about how generative AI and EdTech are shifting the foci of our courses and our disciplinary conversations—and that we have conversations about whether we want to go with the flow or resist these shifts.
We've learned that this conversation is even bigger than we initially thought. Our knee-jerk refusal still feels correct in terms of impulse, but in speaking with scholars with varying perspectives and experiences of refusals, here are some of the things we've learned so far:
It's possible to adopt a refusal mindset without punishing students or proselytizing AI harms. We can focus on the work we do while calling attention to generative AI as a planet- and difference-destroying technology. We also need to talk about why students turn to generative AI in the first place; what material, educational, and social realities put students in a position to choose genAI, and what kinds of organizing and community building would be necessary to address those underlying conditions?
While teaching critical AI literacies is important, writing teachers and scholars need to have a nuanced conversation about responsibility, ethics, and complicity with tech fascism and climate collapse, as Mél Hogan and Théo Lepage-Richer (2024) remind us (PDF). While individuals may not be responsible for the harms caused by large language models, individual choices matter, as do disciplinary prescriptions when considered at scale.
We've also learned that we need to think about time in a few important ways:
we've lived through short-lived hype cycles before (refer to MOOCs; Bady, 2013) and don't need to shift everything we do to meet the moment;
we've lived through decades of public education under attack and recognize that is important to understanding past and present responses to austerity and fascism as well as alternative paths;
we don't have to speedrun big choices about AI; and
this has been a long time coming, and we can meet the moment in new ways than we have before.