This webinar explores the multifaceted role of Artificial Intelligence (AI) in higher education, examining its potential benefits, inherent risks, and the current landscape of its adoption, aiming to demystify whether AI is a hopeful advancement, a harmful threat, or merely a hype.
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Hello and welcome everyone to this Amber
and BGA webinar. I'm joined here with
Water St. and this session will be on
artificial intelligence in higher
education, hopeful, harmful or just a
hype. I'm looking forward to an hour of
really interesting insight and shortly
I'll be handing it over to our speaker
Stan Neil. Now before I do um I want to
remind everyone we want to keep these
webinar sessions as interactive as
possible. So there will be some slido
questions you can scan with your QR code
to answer and if you have any further
questions you can always pop it into the
zoom and Stan will be happy to answer
them at the end. Now if you miss
anything don't worry because um we are
recording this and the slides will also
be available in the postevent hot in the
postevent coms and on the ambj website.
Now without further ado, I'm going to
hand it over to Stan. Stan.
>> Brilliant. Thank you very much. I'm
assuming the screen share is working
okay for everyone still and we can we
can all see that. Fantastic. Okay. Um
yeah, thank you very much. As mentioned,
we try and keep this very inter
interactive. Um I've got a bit of an
agenda in terms of what we're going to
talk through, but um what I will do as
well is is give a bit of context on who
we are. So my name is Stan Neil. I'm
sector principal for education at uh
Watston's and I think before I get into
the detail of who waters are my own
background is is probably pretty
relevant here. So I joined um I joined
the business about three years ago and
pri prior to that I had worked a number
of different universities. So I'd spent
over a decade working in higher
education uh at different types of
institutions uh before I joined Watston.
So um I am not very much nontechnical
actually in terms of background um very
much a background in the sector
understanding higher education and the
challenges therein. Um I should say also
I'm joined on the call by my colleagues
Joan and Jen uh from Water's today as
well. So great to uh great to meet
everybody and be welcomed here. Um
hopefully this clicks through. So who
are waters? Well we are a technology and
business consultancy. Uh we've got three
offices in the UK. We've also got an
office in Australia. Um we provide
services in a range of different areas.
So we've got some of our service areas
on screen here, ranging from things like
cyber security, um mergers and
acquisitions, managed services, data and
AI, digital solutions and development.
Um and so we offer a range of different
of service areas. Um, and as I
mentioned, uh, we we've got three
offices in the UK, one in Australia. Um,
but we've got a real specialism in
higher education. And so I've got on on
screen there in terms of as a size of an
organization, just under 300
consultants. Um, and but across the
portfolio different clients that we have
when I just checked today, we've got 62
live projects in in education with 23
different institutions and organizations
in the sector. So that sector experience
is really relevant to what we do. And
one of the taglines I've got there is
that we're we're big enough to deliver
as an organization. We're a
reasonablesized technology consultancy.
Um but also small enough to care and
small enough to understand the
challenges that are kind of unique to to
education. So um and I've got some of
our different education clients on here
on the screen here as well. And you
probably know there's quite a range of
different types of institution. I know
not all of the um people on this call
might be as familiar with with the UK
higher education space uh you know
people from different contexts but just
to give a bit of of context to that
we've got institutions here such as uh
Manchester Durham St Andrews that are
kind of um the more elite Russell Group
institutions um and of varying sizes and
then we've got some of the newer
institutions such as T-side University
or University of Sunderland but then
we've also got alternative providers. So
for example, NCF on there um have an
entirely different model focused around
further education or that the lower end
of education. So we've got a real range
of of different clients in education. As
I said, we've got a range of different
technical service areas and over the
last couple of years, the big topic um
has been artificial intelligence and
it's something that is increasingly
cut across all of our service areas. So
there's a cyber angle to this, there's a
data angle to this, there's a
development angle to this, but it also
um touches all of our clients in terms
of whether it's those an institution
that's uh got one profile where they've
got, you know, a large number of
students, University of Manchester who
on there, they've got 50 60,000
students. We've got some smaller
institutions. We've got some that are
dealing on those 16 to 18 year olds,
some of the more conventional degree
ages where they're 18 to 21. they're all
uh I suppose grappling with the
challenges of AI. So I'm going to talk a
little bit first about the context of of
AI and education. Then some of the use
cases we're seeing in the sector and
those that we've been involved in
developing, some of the risks,
challenges, limitations that we face
when we're dealing with AI and some
effective strategies actually for
implementing AI solutions. And I will
try to answer the original question that
we posed I suppose as well around
whether this is uh harmful, hopeful or
hype. Um so so that's the rough agenda.
As we said at the outset, I want to keep
this quite interactive. So I will talk
for a bit but then there's a slide where
it'd be great if people could um could
participate in that and and a few points
where um we can have some interaction
and and hopefully some questions and
discussion at the end. So um really
looking forward to what people's
opinions are because actually the other
thing that I would say I referenced at
the start the different teams that we
have now with my background in education
one of the things I specialize in is
doing digital strategy work. So working
with um education clients to develop a
digital strategy for their institution
and that often involves going out and
speaking to staff and students and
different stakeholders who actually sit
out of it.
outside of the technology space. Um, in
doing so, I would say I AI is definitely
the most divisive topic um that we go
out and speak to people about. Um, in
the context of a digital strategy, if
you're talking about something like
Wi-Fi and network connectivity, everyone
agrees they would like the Wi-Fi to be
good. Um, when we're talking about AI,
some people use it all the time. They're
really familiar with it. They're really
confident. They're annoyed that they
can't use it more. and some people are
really worried about it, really hate it,
um, etc. So, you get a real breadth of
of um, I guess um, approaches and views
on AI in terms of people. Generally the
um maybe quality isn't great on these
memes but generally when we when we
think of AI in education one of the
challenges here is um the immediate
recourse and lots of conversations is to
think about plagiarism uh to think about
academic integrity. So we've got a
couple of relating to people using chat
GPT to make up scholarly articles that
they can reference or uh you know using
it to plagiarize and then failing. So
that is typically where a lot of
people's heads go immediately when they
think of education they think of AI it's
thinking about academic integrity would
students you know be cheating on
assignments how can we um guard against
that and to some extent what I would say
is starting that from that quite risk
averse um basis so a lot of institutions
that we work with they may already have
policies around AI but it's been very
much developed by those in the kind of
teaching and learning domain. So the
academic staff, it's not really
necessarily been involved with the wider
organization and engagement say with it
or the technical teams um who might be
looking after systems that have AI
capabilities etc. Um so we're often
starting from quite a worried risk
averse space which is focused
particularly on as I say student usage
uh plagiarism and those kinds of issues. So
So
thinking then about in a bit of broader
context what are some of the use cases
and other areas that we might want to
think about. Um
and I've broken these into three
categories. I think broadly it falls
into use cases that relate to students,
use cases that relate to teaching staff
and use cases that relate to I guess the
operational function of of um different institutions.
institutions.
So starting with students on the left, I
mentioned there the worries about
plagiarism. One of the things that we
have seen is people starting to figure
out how you could have tools that are
approved tools that students can use to
assist with, for example, research,
right? Finding those articles they need
to they need to reference, helping them
in terms of providing feedback. Feedback
often in quite a general sense in terms
of writing advice, that kind of thing.
um can it make their experience better?
Can it speed up the enrollment process?
For example, I've got a use case which
kind of linked to that in this slide
deck. Um also thinking about from a
student perspective, you know, these
students are going to be entering a
workplace um where there's an assumption
that they'll understand a little bit
about AI and how to use AI as that
becomes increasingly part of everyone's
working lives. And so setting them up to
actually be able to to use AI tools. In
terms of teaching staff, we're thinking
about things around feedback and
marking. Okay. So, how can AI enable
that, speed that process up, um allow
them to spend more time, quality time
with students? Can it help with lesson
planning, course planning, um that kind
of stuff? Can it help with target
setting and different differentiation?
and the sort of data analysis
capabilities of AI um be an aid to
teaching staff as well. So that's some
of the conversations we're seeing there.
Um but we're also seeing I suppose
operationally more of those backend
functions. How can AI support those? So
can it be used as a chatbot for
marketing and recruitment? Can it help
to attract students to an institution?
Can it be used to speed up I've
mentioned enrollment a couple of times
but can it be used to speed up
enrollment processes that might be slow
and that's where institutions are losing
learners because actually that taking
too long they're going somewhere else um
what about things like room bookings or
resource optim optimization
um and there's a use case that that I've
encountered around policy documents for
example right institute has loads and
loads of policy documents they all cross
reference each other can you use AI to
make sure that if a if a policy changes
in one domain that has a knock- on
effect elsewhere that it's tracking that
and making sure that everything remains
aligned. Um so lots of different ideas
and use cases there. Um and this is
where I would encourage people as I say
we've got a slideo
um where the question is what are the AI
use cases you think have potential to
improve student experience save staff
time or deliver operational efficiency
in education. So, um I'll leave that on
the screen for a moment so people can um
can follow that QR code uh to to leave
some comments. And what I'm going to do,
I'll leave that up for a moment. I'm
going to talk in detail about two of the
actual use cases that we've implemented.
Um and whilst I'm doing that, hopefully
you guys are able to throw some of your
ideas into that slideo and then I'll re
revisit that um after I've spoken
through the use cases. So hopefully that
makes sense. I've thrown some ideas out.
I'd be keen to hear what ideas people
have, what people are interested in,
throw those on there. Um and then as I
talk through some of the examples, we
can return to them um and sort of see
what see what's coming from the the
wider community and particularly
interested to get the perspective um
from yourselves as as business schools
as well and thinking about you know I
think your students in particular um I
guess would be expected on entering the
world of work to maybe be engaging with
these technologies and having an
understanding of these technologies as well.
well.
Um so yeah I can start I can see that
there are some things coming through on
the slideo. You can also I think through
slido sort of upvote um other people's
uh suggestions as well. So feel free to
for you to do that and we'll we'll
revisit that uh in a moment. So I'm just
going to talk through two um
use cases that we've actually
implemented as as an organization. Um,
and the reason I've picked these two is
I think they're quite different ends of
the the spectrum there of how easy they
are to implement versus how complicated
they are. Um, and they highlight some of
the um benefits that can be delivered
and then hopefully this is uh an aid
memoir to us then thinking about what
the risks are and how we prioritize the
deployment of artificial intelligence.
Um, so the first example we have is
around uh document checks. So the
problem statement we had with this
client um was that they their students
were required to upload ident evidence
of identity as part of an application
process. So this wasn't actually a um
university example. It's a further
education example. So um we this is
learners doing apprenticeships and NVQs
in in the UK.
But as part of that application process,
they had to upload a passport or
alternative ID. So something like a UK
driver's license.
Um and what students were doing is a
large number of their students on
application were uploading
um any picture that they had on their
desktop or on their phone just to get
through the process, right? They
couldn't be bothered to go and get a
picture of their passport or driver
license. They just upload a picture
because they can't they can't be
bothered. They get through the
application process. That institution
then receives this application. They go,
"Yeah, this would be a great student."
However, they haven't uploaded the the
requisite documentation. The staff are
then having to contact the students and
say, "Can you upload a driver's license?
Can you upload a passport?" A lot of
people don't reply or ignored them. And
so, it's wasting uh time of the staff.
Um it's meaning that a number of
students who they would have given
places to are not actually sort of on
boarding and finishing that application
process because um you know because
they're not uploading those documents.
So the solution was using Azure document
intelligence. So that's an application
within Azure. So um those on a Microsoft
kind of infrastructure can implement
this fairly easily. It's it's already
part of that application stack. Um and
basically training document intelligence
to verify that these are the documents
that we're after. So um it's basically
saying right yeah this is a driving
license, this is a passport. Um, and the
other part of that training is to be
able to grab data from it to speed up
the application process. Um, so the the
tool is going right there's their home
address, there's their date of birth,
there's their name, and we can
prepopulate the application form. Um, so
the training is is the main challenge
here. Can you get that application to
quickly um discern that this is the
right document and pre-populate that
form? And I guess in terms of the
benefits then the benefit is that the
institution isn't having to waste as
much staff time going back to students.
And it might be that the odd one sneaks
through that isn't uh isn't the right
document. Um or that actually there's an
expired passport that's been missed and
they have to go contact the student
anyway. Um but it's certainly saving the
institution time and the staff time. But
it's also speeding up the application
process for students because you're
saying give us your driver license or
passport now and actually that'll
pre-populate the rest of the forms and
you can have a decision you know in
minutes on to whether you can get onto a
particular course. So there's two
benefits to that in terms of setting
that up. The other thing to mention on
this one is it was developed very very
quickly. Um this was sort of designed in
in an afternoon and within a couple of
weeks was a live thing that that
institution was using to to help screen
these applicants coming through for
these um these apprenticeship courses.
Moving then to the other end of the
spectrum in terms of difficulty
and I guess the size of the challenge,
we've got one around here around learner
intervention. So
students obviously withdraw from their
studies for a number of reasons. Um, and
I won't be s I wouldn't be surprised if
there is stuff um on the slideo around
this topic of of trying to understand uh
when students are are likely to to drop
out. Um, and I guess part of the
challenge here is there are a number of
different um markers of whe whether that
student's going to drop out. It could be
their attendance is poor. It could be
that they've not logged into the virtual
learning environment for a week. It
could be that they've got poor grades or
grades have actually been getting
progressively worse. There's a number of
risk factors that could uh indicate that
a student might be struggling in their
studies and uh liable to drop out. Um
and this example that we've just
finished a project. So it'll be
interesting to see this what impact this
has over time. So this this one just
finished a couple of weeks ago. Um but essentially
