Closing The Gap Part 1: Beginners and Advanced Users
The Atlantic article My Students Use AI. So What? had a bit of a moment in late October. There are redeeming parts, but one line caught us by surprise, “I have also found ways of posing questions that get past what AI can answer, such as asking for a personal take—How might we push society to embrace art that initially seems ugly?—that draws from material discussed in class.” This was a moment when we both realized how much bad and outdated advice about teaching with AI is floating around and even being propagated by elites. We have also noticed this in workshops, conversations, and in talks. In a room of 10 or 50 people you will be trying to have an advanced conversation about a topic and then realize that a meaningful percentage of the people in the room are living in Spring 2023 in terms of AI and their courses. Nik and I have started referring to this as “the gap.”
In any group you will find pockets of advanced users and novices, but AI is different. First, adapting takes real time. One of our favorite explorations of this journey was from May of this year and drew comparisons to the stages of grief cycle. Second, and perhaps even more significant, AI is itself an accelerant of productivity and change. Using AI to help in rapid course redesign or innovation makes the process go faster and it also accelerates the competence of the user. So yes, gaps always exist in groups, but this one is different–and it is widening.
In response to this realization we are going to do a mini-series on the blog addressing issues we (and kind of thought everyone) had already moved on from. Spring of 23 was awash in advice and adaptations that sort-of worked for a brief period of time and some of us are still living in that moment. This is not an attack on the people who delivered that advice close to three years ago or the people who took it as everlasting and moved on. It is an attempt to address persistent myths about AI and teaching to better equip us to move the conversation forward. Closing the gap together will move us towards what Brett Christie calls “herd AI literacy,” a nod to epidemiology, a time when high levels of mass literacy levels allow us to move quickly and more cohesively.
The first post will come out next week and address the persistent myth propagated by this Atlantic article, that the models are not capable of doing personal reflection work so asking students to incorporate their own experiences circumvents model usage.