Thinking with Colleagues: AI in Education
Grush: And can we find a report?
Wagner: With help from our colleague Karen Vignare, the vice president of Digital Transformation for Student Success and executive director of the Personalized Learning Consortium at APLU, we developed a summary of the meeting so we would have a baseline of our thoughts going forward. You can find the summary, along with the names of participants, posted at North Coast EduVisory.
[What Is Top of Mind for Higher Education Leaders about AI? - North Coast Eduvisory]
Grush: The idea of thinking with colleagues in less navigated waters seems to be key. I know that academics meet and share all the time, in many venues, but your format is distinctive. Could you comment on some of the main takeaways for you, not so much on the thoughts about AI, but on the process of thinking together? What can educators do, both to learn from and to support this process, especially as they explore a "big change" area like AI?
Wagner: For me, there were several takeaways. I'll list my top four here.
First, we all need to see ourselves as participants in the upcoming changes. There's a lot of noise about AI and change out there. One of the best ways I found to cut through the noise is to quit thinking about every possible thing likely to be different. Instead, I'm thinking more about how the AI in my own work environment is likely going to affect me. It's good to remind ourselves that AIs are going to take personalization to a whole new level. There is no reason to follow the crowd just because the crowd is going down a particular road. Sometimes the "road not taken" by others may be exactly right for you.
Second, keep up the narrative around dealing with change, for the benefit of all. Mostly, people aren't so much freaking out about AI as they are freaking out about changes they can't anticipate. Sharing our narratives will help others appreciate that nobody has a manual describing what we are supposed to do next (thank goodness for that, by the way). Helping the members of your community see themselves in the future and telling the stories of what that looks like is a great way to stimulate new thinking and motivate forward momentum.
My third, and perhaps favorite takeaway is to model the small steps that lead to sustained innovation and eventually to the big changes that stick. Showing people the approachable applications of AI will go far over time towards beneficial and ultimately transformational change.
And my fourth in a long list of takeaways is, surround yourself with people who are curious. Curious people will never let you get away with lazy or inattentive thinking. It's also important to be curious yourself. It is easy to get into a thinking rut. It's also easy to take one's own opinions a bit too seriously. Remember, we are all still exploring AI. If someone seems to know all the answers at this point, I just can't trust them.
We are all still exploring AI. If someone seems to know all the answers at this point, I just can't trust them.
Grush: Those are great and thoughtful suggestions. And exploring the link to the summit I see that there are so many things, to think about AI with your colleagues. What were just a few of your own top picks?
Wagner: Let me give you four.
First, I was fascinated by the "Moonshot or Road Trip" conundrum we encountered. It's kind of related to the third takeaway I just mentioned on process. You know the challenge: When a new innovation comes out, everyone is convinced it is going rock our world. Everyone is drawn to the energy of Moonshots. But when we discover all the things that don't work as we thought, or we realize just how much will change once we introduce the innovation more widely, then we need a different approach. Taking more of a "Road Trip" approach to AI adoption might give people a better opportunity to engage in the transformation. It is more affordable to immerse in the immediate, and to take the series of small steps that will move the team to its goal and maybe further.