Campus Technology Insider Podcast March 2024
Rhea Kelly 08:54
Yeah I was just going to ask about the, the stakes, because you mentioned how like, there are some high-stakes things going on. So one of them obviously is the privacy issue. And then I suppose, you know, the need to serve students and support student success. Is there anything else that that contributes to that high-stakes feeling?
Jenay Robert 09:12
Yeah, so you've definitely hit on two of them, right? So thinking about serving our students to the best of our ability, making sure that they're actually learning. I mean, that's where the whole academic integrity discussion comes in. People are really fearful that they're not going to be effectively teaching students because students are using these tools in ways that sort of bypass the learning process. Privacy and security, of course, huge themes over the last few years. It's become even more and more important for our institutions because every single day, our society and our institutions are more data-rich, more data-reliant. Many times in the work that we do at Educause our members say that data is just as valuable as currency and we need to protect it accordingly. I would argue that it's even more valuable than currency, because now you're looking at human capital in a lot of cases. So yeah, those are definitely two of the big ones. And then third, looking at access and equity issues. So as — and I think that this has less impact in the early stages as it could as we progress farther — so as we see more and more AI tools being integrated into teaching and learning, we need to be concerned about reinforcing various types of biases. We need to be concerned about access to education — does it make it harder for some people to actually access education because they don't have access to these tools? Are we widening the digital divide? Right? The accessibility of these tools is still in early phases. So while that should be a foundational component of any tool that's created, we know that digital tools are not usually created with accessibility in the foundation of the creation. So that, that, that is then another layer. So I think in the, in the access and equity world, that's kind of the big third kind of high-risk area, in my mind.
Rhea Kelly 11:13
What does the survey, or the survey results tell us about what institutions need to be doing, like when they're formulating their AI strategy, developing policies, and sort of navigating implementation?
Jenay Robert 11:26
Yeah, so, you know, I already touched on this "I don't know" theme of the week. And I think that that's something that — I tend to joke when I present on these data where I say, okay, another presentation where we talk about silos in higher ed. You know, it's become a little bit of like the standard. But in this case, I think it's even, it's exacerbated even more. So you've got people in highly technical areas that really understand the technology and the capabilities, you've got folks in data privacy or data and data security areas who are really experts in those things, the teaching and learning folks who are trying to figure out what do we do with this on the ground, administrators who are trying to lay out policy and kind of guide the ship. It's just everyone across the institution has some stake in this — this is something that touches everybody. And so kind of bridging those gaps in communication and getting both top-down and bottom-up involvement in policy and guidelines and strategy is really, really important. And then connected to that but separate, I'm really encouraging folks to collect local data. So we have linked in the report the full survey instrument that we use. So that's one option for any institution to just take that instrument and use it at your institution — and I'll say if anyone does that, please email me because I want to know about it — and see what you get. But I'm always encouraging folks. At the, at the summit, we talked in some small groups about doing things like creating student advisory groups, which is great for any topic, not just AI. So yeah, those are the two big things: really shoring up communication across silos, and then collecting local data, figuring out where your stakeholders are, where your local community is. Every institution is so different and so unique that pairing local data to a larger study like ours is really important.