AI & Education at a Crossroads

Speaker 1 0:00

You know, it really strikes me how we're at this fascinating crossroads in education right now. It feels a bit like where we were maybe decades ago. There's all this buzz around generative AI and it reminds me of the excitement around universal design for learning UDL when that first came on the scene.

Speaker 2 0:18

That's a great parallel. Yeah, it really is that feeling of a potentially huge shift, isn't?

Speaker 1 0:23

it yeah.

Speaker 2 0:23

We're looking today at the abstract for a keynote speech. It's for the ADC UDL conference in Sydney.

Speaker 1 0:30

Okay.

Speaker 2 0:30

And the main theme is navigating these big changes AI is bringing for learning, teaching, assessment, basically making things inclusive.

Universal Design for Learning Origins

Speaker 1 0:39

Right. So our mission today is really to unpack that, to try and grasp the key things we need to consider as AI kind of reshapes education, especially looking back at UDL, and we've got two versions of the abstracts, slightly different wording, so we'll try to catch those nuances. Sounds good, okay, so let's dive in Universal design. That started what about 40 years ago? Took that idea from architecture designing buildings for everyone.

Speaker 2 1:01

Exactly.

Speaker 1 1:02

And brought it into education.

Speaker 2 1:03

And the really interesting thing was the goal with UDL from the get-go was creating learning environments, materials, everything that just worked for all students.

Speaker 1 1:13

Yeah.

Speaker 2 1:13

Right from the start. No retrofitting needed.

Speaker 1 1:16

And technology was seen as a big part of making that happen, right.

Speaker 2 1:20

Oh, absolutely. There was a huge belief that technology could be the key to making learning truly accessible for everyone, particularly disabled learners. A really ambitious vision.

Speaker 1 1:32

So fast forward to now or in another one of these big change periods, but this time it's generative AI driving it.

Speaker 2 1:38

Yeah, and it's touching everything how students actually learn, how teachers approach their lessons, how we even assess understanding. It's a similar kind of upheaval.

Speaker 1 1:45

And the abstract points out, this is especially significant for learners who might be considered at the margins or learners with disabilities.

Speaker 2 1:53

Definitely. Both versions really stress this point. They stand to gain potentially the most from AI if it's done right, but they're also maybe the most vulnerable if we mess it up.

Speaker 1 2:03

Which leads straight to that idea of AI being well a double-edged sword. The abstract uses this phrase turbo-charge, accessibility and inclusion. You can sort of imagine AI creating really personalized learning paths. Can you give us a concrete example Like what would that actually look like?

Speaker 2 2:19

Sure. Imagine, say, an AI tool that figures out where a student is struggling with a concept.

Speaker 1 2:25

Okay.

Speaker 2 2:26

And then it automatically generates maybe extra practice problems or finds a different video explaining it or simplifies the language, whatever that specific student needs. Yeah, tailored support instantly.

Speaker 1 2:37

Wow Okay.

Collaborative Campus-Wide Approach Needed

Speaker 2 2:38

But and this is the crucial but- yeah. Alongside that amazing potential, there's this very real danger the abstract flags worsening the inequalities that already exist If the tools aren't built with everyone in mind or if only some students can access them.

Speaker 1 2:52

Then the gap widens.

Speaker 2 2:54

Precisely, which is why the keynote abstract really hammers home this need to be purposeful about how we use AI. It's not just about adopting the tech.

Speaker 1 3:01

It needs direction, like serious collaboration across the whole campus.

Speaker 2 3:05

Exactly that, just about adopting the tech. It needs direction, like serious collaboration across the whole campus. Exactly that Getting different departments, educators, IT folks, disability support services all talking and working together. The goal is joined up strategies.

Speaker 1 3:15

Not just pockets of innovation.

Speaker 2 3:17

Right. We need systemic change if we want the impact to be genuinely positive and inclusive. And, crucially, the abstract says we have to put the learner's requirements right at the center of all this planning.

Speaker 1 3:29

Absolutely. It can't just be about the technology. It has to be about how it serves the students, understanding their actual needs and challenges first, and building networks is key too.

Speaker 2 3:39

The abstract mentions creating these networks of allies across campus, people who are committed to this.

Speaker 1 3:45

Because one department or one person can't possibly drive this alone, can they?

Speaker 2 3:49

No way. It's about fostering this future-aware learning environment, one that's accessible, feels authentic to students and is truly inclusive, even as the technology keeps racing ahead.

Speaker 1 4:01

The more accessible version of the abstract also touches on digging into the practical side and the research questions too, right?

Balancing Promise and Pitfalls

Speaker 2 4:07

Yes, exploring what this actually means on the ground and what we still need to figure out through research. And it adds another important dimension AI's potential to boost learners' independence.

Speaker 1 4:17

How so.

Speaker 2 4:18

Well, think about tools that help students manage their own learning or access information more easily. It could also improve professional practices for educators. Maybe AI assists with course design or helps provide richer feedback.

Speaker 1 4:31

Okay. So wrapping this up, the core tension seems to be this huge promise AI holds, echoing those early UDL hopes for technology making education accessible for all.

Speaker 2 4:42

Immense potential.

Speaker 1 4:43

But getting there requires real care, intentionality, collaboration, keeping learners front and center. Otherwise we risk making things less equal.

Speaker 2 4:51

That sums it up perfectly. It's potential versus pitfalls, and the path we take depends on deliberate action.

Speaker 1 4:57

Which really leads us to a final thought for you, the listener. As AI continues weaving itself into the fabric of education, what specific, concrete actions can you take, maybe in your own work, your studies or within your institution, to help ensure that its benefits become truly universal, actually contributing to a fairer, more equitable learning landscape for every single person?