Campus Technology Insider Podcast May 2024
Rhea Kelly 05:34
I think it's so interesting that, you know, that AI has a place across so many different areas. You mentioned teaching and learning, research, kind of that workforce readiness piece, and then the operations. So from a practical standpoint, how do you prioritize, you know, where to start with those things? Even, even how much time would you spend on generating policies versus, you know, helping integrate into operations in, let's say, a business office or something like that? Like, where, how do you know what to do first?
Shlomo Argamon 06:10
Well, to be honest, the answer to that question is it's an evolving answer, because it changes. And I think that the question of prioritization is something which, when, once I figure out an answer to how to prioritize things, within a week, I'm sure that those answers will change again. So it's important to be sort of agile, and to be responsive to conditions. You know, right now, what I and people across the university, what we're trying to do is to set some foundations. So looking at foundations, there are foundational questions of policy that need to be dealt with. And so that's, that is definitely an important priority. Setting up foundational educational programs across the university, in terms of, in terms of specific courses to try to start teaching fundamentals of AI to all of our students, as well as establishing programs in AI, focused programs, both on the more technical side as well as the more application side. That's, that's an important first step to building the foundations. And then another key priority is educating our faculty about AI, about its possibilities, about its risks, how to think about it, how to work with AI, that's, that's also a very high priority. One statistic shows that in most higher educational contexts, somewhere between 10% to 20% of faculty are familiar with and use generative AI in some fashion, whereas at least 100% of students use AI in some way. Now, that is a tremendous disconnect between the faculty and the students. It's fundamental that we ensure that all of our faculty are familiar with AI, are familiar with what it can do, familiar with what our students can do with it, and start rethinking how they teach to fit this new, this new world, really.
Rhea Kelly 08:25
I'm curious what your relationship is with IT leadership, because it seems like, you know, as a technology implementation, you know, across campus, it kind of goes hand in hand with IT.
Shlomo Argamon 08:40
Yeah, it does. And I work very closely with our, with our IT leadership, particularly on the, on the policy front, because a lot of, a lot of these questions in terms of, in terms of what our faculty, staff, and students can do with AI in the university, in the context of the university, depends to a great extent on what tools we have available, and also what the specific technological capabilities and risks are. So, for example, if we think about security risks of using AI systems, so one big risk that not everybody is aware of, is if you use one of these systems like ChatGPT or Bing Chat, anything that you type in potentially is going to be taken and used as training for the system to improve itself in the future. Which means that if you use it to, you know, to ask for help in, I don't know, designing university strategy, well, everything that you told it and asked about university strategy, all of that information, which is presumably private, now is sitting there at Microsoft or OpenAI or somewhere else, accessible, in principle, to someone else. So we need to know, and so I worked very closely with them to use their, I mean, they have the expertise in how do we assess these, these, these systems. You know, what are the options that are available? What can we install? Or what can we use and access and make available to our, to our faculty and to our students along these lines? So it's essential that anybody working on, on AI, really any technology-related area like this, but, but certainly AI, does work very closely with, with, with the IT people. At the same time, I think it's worth saying that my role, or any similar role in terms of dealing with AI in the university context, is, is not a subset of IT. Because we're dealing with not just purely questions of software installation and working with how people work with the technology, but we're looking at other issues as well. So we're looking at questions of, you know, how do we define plagiarism, for example; those sorts of issues also become important.