Bridget Burns
speaker
389 appearances
6 recordings
1 series
first heard Dec 2024
last heard May 2025
Bridget Burns’s voice in public audio — every appearance, attributed to the second.
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Appearances
We need to figure out the efficacy and the safety issues that are definitely going to happen and are popping up already. And instead, what you're having is A bunch of people who are working individually with their head down, separated, all figuring out what problem they want, how they want to use AI or whether they don't. And then there's a large swath of higher ed that is more risk averse.
And so they may or may not be using it at all. And so you're going to see a new like version of the haves and have nots. And for me, what I just always I'm predisposed to notice the big picture and to be a systems thinker on this. And so I just I see really big sector problems that affect community colleges, every type of institute, every type of university. And it's really about the students.
It's about how we can weaponize this for good. How do we make it so that. The people who work at a university who are, you know, front office that are being overwhelmed by repetitive questions or repetitive issues, how do they use AI so they can actually not have to do that and instead can provide more hands-on support for students? Now, we're seeing that with chatbots as such.
And how do administrators be more effective and efficient so that they can actually get through their days and be able to produce more things, to be able to accelerate speed? Because that's a real challenge for us. And for faculty, just like it's learning, it's, you know, how do you use this ethically when you're trying to one of the biggest impediments for your time is grading?
How do you use it from a, like, pedagogical perspective to make it so that what you're doing is better? These are big questions that are not particularly unique. These are, I've given you what, it's like three problems. Those are sector problems. And so... It's just sad when we only focus on my institution wants to be first. So University of Michigan, go get them or Arizona State.
They're definitely out front on AI. But I just think that there are very clearly like same problems, like same team. And we have to find a way that we are going to collaborate together. in an effort to make our use of AI safe, effective, efficient, and trustworthy, and going to be able to, again, I think at the end of the day, it's about personalized learning at scale.
And also make sure that what we're teaching today is not out of date because the future of work and how AI is disrupting the workforce and going to disrupt the workforce, that means that the things we're teaching now in certain classrooms today is no longer relevant. And there is I have little confidence that individual disciplines are going to be in real time keeping up with that.
And if they are, it's one dean or it's one chair or faculty member. It's not the whole discipline working together to figure out, OK, so I can see that the role of paralegal is going to be changing rapidly right now because of chat GBT. fundamentally, you can conduct a lit review with a well-trained model super effectively.
And what does that mean for how we can... I just think there's a lot that's happening so fast. So then... If you're training people in the legal profession or anything related right now, you should have a part of your curriculum about AI. You should be thinking about how the role of paralegal is changing rapidly now because of that. And so therefore, it's like...
We've always had a problem with our connection with workforce. And now it's like it's on steroids and steroids are AI. And so, again, every one of these is a sector wide problem.
And I just say that my problem is the architecture of this entire sector would make it so that we would hunker down and work alone independently and wait until we feel like we have a peer reviewed article to publish before others find out what we've been doing. And students cannot afford to waste that time.
Yeah. In 2017, we partnered with Strata Education Network to, as a next, we do a big change initiative. So like predictive analytics, chatbots, proactivizing, our whole thing is scale. So we take a model from one place and scale it on other campuses and we learn a method for scale. Like how do you need to adapt that idea so that it survives and thrives in a different ecosystem?
And then we create playbooks for the rest of the sector to learn from us. So That's been our model, scale. But we ran into this issue in 2017 of this issue of college to career. There's nothing to scale.
There are lots of little tiny things out there, but we recognize that the entire... We've come at this work thinking with the baseline belief that higher education was never designed around students. And that's the problem. And it was especially not designed around the students that we need to serve. Low-income, first-gen students of color. So...
Then we get to college career and it's, oh, my gosh, if we thought if we thought we had bad design once, watch out, because when you look at career services and just that model and that approach, it became very clear that was a manifestation of what we're talking about.
And we agree with you about the the students measure their success by it's much more nuanced and complex, but they want a job, of course. So we did a multi-year initiative to actually come up with, instead of the scale, it was about innovation, which was how should this be if we were to design it based around the needs of students and specifically use design thinking.
If you could reimagine that whole college to career handoff around the needs of students where you could actually make up for privilege. Meaning if you looked at the data that a student from a low income background would have the same kind of results or outcomes as a high income student who comes in with a deep social network, etc.
And so we got seven universities together to first we started with process mapping, as always, to understand just how bad is this?
because the system seemed really dysfunctional for students you have a office in some basement somewhere with like a tiny budget um that nobody wants to go to other than to get their resume looked at and so we first started with this false assumption we quickly checked which was let's see all the things that career services is responsible for and then let's like map those things and let's look at their kpis and then we would be able to benchmark against those and try and improve those that's what we thought it turns out
step one is we didn't have any kpis because nobody was actually tracking any data we had no idea that if you wanted to measure the number of students who go into career services from certain backgrounds they don't have that data they don't even know how many people come in depending on who you're talking to like they just they're overwhelmed the i one of my institutions had 70 000 students and they had two people in the office of career services
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