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 .

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

Universal Design for Learning Origins

Speaker 1 0:52

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 .

AI as a Double-Edged Sword

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 .

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

Collaborative Campus-Wide Approach Needed

Speaker 2 2:47

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 ?

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

Balancing Promise and Pitfalls

Speaker 2 4:17

.

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 ?