Hey there,
we're really pleased to be launching our
new AI Adoption Path,
this resource critical for VET providers
to be looking at responsible
implementation and adoption of AI
solutions across all areas of their
operations.
This is coming at a critical time for
the first time.
We've got definitive guidance from the
Australian government on the expectations
for what needs to be in place for
adoption across organizations and
businesses.
And we've designed this pack specifically
to that guidance and customized to support
that providers for effective
implementation.
Before we get too far into it,
let's just start with a reflection.
What are our use cases
Who is accountable for each one and what
decision rights do they actually have when
you're thinking about AI implementation?
What harm could realistically occur in our
context to whom?
And where is our stop or pause threshold
in the implementations?
What controls do we have for data testing
and ongoing monitoring their
self-assurance?
So problems are found early rather than
after an impact occurs.
Some key questions there that are really
getting at the responsible implementation
and use of AI as we adopt
these AI solutions across our operations.
What we're going to be doing in this
short video is exploring the resources
that are available,
the tools and templates that we've put
together for that responsible AI adoption
with a specific focus on their providers.
Before I get into the pack,
I just want to cover off on some
of those key obligations.
And yes,
we do have the twenty twenty five
standards for RTOs that we need to be
thinking about.
And it does come into multiple areas.
But in particular,
I would highlight outcome standard four
point three in risk management.
Any risks to students,
to our personnel or to our organisation
are organised and managed.
And we know that there are so many
risks that are presented in terms of AI
use, whether it's students using AI,
for example,
in terms of assisting with assessments,
whether it's our personnel using AI,
are we doing BYO AI?
bring your own along,
or are they actually using tested and
approved solutions that the organisation
is paying for, monitoring and managing?
And then what are the risks for the
organisation overall?
Privacy risks, governance risks,
implementation risks, compliance risks,
reputational risks.
And so the list goes on.
So really,
when we're thinking about outcome standard
four point three in the first instance,
we must make sure that we've got a
handle on our usage.
And when we think about the guidance for
the Australian from the Australian
government,
twenty twenty five has been a very big
year.
As we know, in twenty nineteen,
we had some of the first guidance,
Australia's ethics principles,
and then that was followed up with the
advent of the voluntary safety standard.
Now just be thinking about November,
was the release of CHAT-GPT.
So if we're sitting here now in ,
we're three years on from that release and
counting and really taking some time to
get the formal guidance in place.
In ,
the Office of the Australian Information
Commissioner looked at the privacy
implications for AI and released.
privacy considerations when using
commercially available ai products and
that was updated again in for a current
version and then our national artificial
intelligence center has released critical
documentation through we now have the
guidance for ai adoption these are the
essential
implementation requirements for all
organisations in the Australian region,
including VET providers,
on what we need to be doing to
ensure that we are implementing effective
AI practices in our organisations.
We also now have the National AI Plan
and that is a very strong focus in
skills development that is useful to be
considering from a vet provider
perspective and we also most recently have
new guidance for business on being clear
about ai generated content and what we
should be doing in terms of labeling
or having other transparency mechanisms in
place for when we are using AI generated
content.
And when we think about vet providers,
if we think about learning resources,
assessment resources, marketing resources,
images, videos, the list goes on,
we may well be using much AI generated
content within our operations.
So again,
twenty twenty five has been a really
critical year towards the end of twenty
twenty five with all of this key guidance
coming out about now the effective
implementation of AI for Australian
organisations, including their providers.
And as we know,
we are operating in the era of
self-assurance.
This is the way that we manage our
operations,
including our AI usage for a focus on
quality,
including compliance to not only the
twenty twenty five standards,
but to other government guidance and for
ongoing continuous improvement.
And we have to have systems in place
to critically examine our performance.
What is it we're doing?
with AI across our operations and also the
outcomes.
What impact is our AI usage having in
our operations?
And we know with outcome standard four
point four in the twenty twenty five
standards,
when we're thinking about continuous
improvement,
we're required to have in place systematic
monitoring and evaluation.
We need to understand what is the outcome.
or the outcomes that we're aiming to
achieve with AI usage?
And what indicators are we going to put
in place to measure those outcomes?
What data and feedback are we going to
be collecting?
And how are we going to be monitoring
the AI usage on an ongoing basis to
ensure that we are achieving the outcomes
that we're expecting?
And then at periodic reviews,
how are we going to evaluate the
effectiveness of our AI usage?
We need to be engaging with our
stakeholders in those monitoring and
evaluation activities to be getting their
advice and feedback on what we're doing.
And then we need to be communicating and
reporting all of those findings back to
our key stakeholders,
including our governing persons,
so that we can use that information for
our decision making,
and for improvements to our systems and
our services delivery.
So there's a very,
very high bar there with systematic
monitoring and evaluation across all of
our operating functions within our VET
provider and importantly, obviously,
with our use of AI as well.
Now,
I'm going to pause there and we'll get
into the AI pack.
And there's some key documentation that I
want to be looking at here as part
of the pack.
We're going to kick off with the area
of governance.
So let me just bring that pack back
up here again.
And we're going to be looking at the
area of governance and planning.
Then we're going to be considering
templates and tools for implementation.
And then we're going to be considering
templates and tools as well for that
monitoring and evaluation function.
All of these are included within our AI
adoption pack.
Before I do that,
I do just want to mention as well,
we've also included a folder with all of
the supporting documents.
So if you jump in there,
all of that government guidance that I
mentioned during the presentation and more
tools
are included,
government tools are included in that
supporting documentation folder as well.
So let's click off with governance and
planning.
And there's probably no surprises here.
We're starting off with a stakeholder
impact analysis.
So how do our stakeholders feel about the
implementation of AI and what impacts may
this implementation have on our
stakeholders?
Remembering quality,
quality services delivery,
quality is fulfilling stated and implied
needs,
is fulfilling the requirements that we've
identified from our stakeholders,
the very definition under the
international standard.
So we've got a stakeholder analysis tool
looking at who our stakeholders are,
their types, their interests,
their influence, their expectations.
What are the goals and communication
needs?
How might they influence our strategy?
and then we're taking an impact
assessment.
We're using the AI screening tool or we're
using our standard risk assessment to do
an impact assessment on how will this use
of AI impact our stakeholders and then be
thinking about the effective
implementation of AI from that basis.
So again,
don't want to spend too much more time
on that,
but really starting off with that
stakeholder expectation.
We've also included a stakeholder survey
on the use of AI.
Now,
this could be used as part of your
implementation.
This obviously is to be used as part
of your monitoring and evaluation
activities,
but we're including it here in governance
upfront, again,
to get that information from stakeholders
in the first instance,
to support your governance and planning
for the implementation.
And again, it's a stakeholder survey,
so it could be getting used with
personnel,
could be getting used with students,
with employers, with industry,
with community representatives, et cetera.
And it's just a couple of pages there
thinking about key aspects of the
stakeholders' use and expectations around
AI.
We've got the standard four-point Likert
scale there to make that quantitative
analysis really easy and to be gathering
the data that you need to be supporting
that stakeholder analysis.
as i mentioned that survey is obviously
then also available for use with both
implementation and also monitoring and
evaluation once we've got all of that
information in place we can be putting
together our ai policy and it is an
expectation that all organizations will
have an ai policy in place part of
our guidance for ai adoption from the
national ai center and i won't spend too
much time on this obviously we've got a
policy format so we've got the purpose
the scope of the policy,
who it applies to.
With all of our VETA policies,
and we recommend for all VET providers for
your policy positions,
include a rationale.
Make sure that you've got that summary or
that justification,
that rationale for how you're approaching
that particular operating function of your
organisation.
Very,
very handy to give that summary for all
of your policy users,
including both internal but also external
and right through to your regulator
auditors.
So get that rationale in there.
We've got the governance of AI and key
components here for governance, again,
aligned directly to the Australian
government guidance for the
implementation.
We're mentioning here the use of an AI
register to be tracking.
and keeping that monitoring going of all
AI that's been approved and has been used
in the organisation.
We've got the roles and responsibilities
for AI use,
how you go about implementing new AI
tools,
including the use of the AI screening
tool,
how you go about the management of AI
generated content directly aligned to the
new guidance from the Australian
government for the transparency measures
that need to be in place for your
use of AI generated content.
You can see information there on the
transparency measures,
also incident reporting when something
goes wrong and that standard monitoring
and evaluation approach with your policy
positions.
You can see there we refer out,
we're not repeating huge amounts of
monitoring and evaluation information that
should be in your policy,
your quality policy and procedures.
So just reference all the other policies
that you have across there and make sure
that that's covered off.
So you do need to have your AI
policy position in place.
Hopefully you found that useful.
We're also including a risk matrix on AI
adoption.
And this is a collection of all of
the government guidance on the common
risks for use of AI.
And it covers here, as you can see,
the risk, the effect, the risk rating,
and then the mitigating actions that you
might consider.
And we've got the adoption of AI.
we have both for the organisation,
we've got the system purpose deployment of
AI, we're talking about data management,
system design, security,
communications with our customers and
stakeholders,
using AI enabled products and so the list
goes on.
So again,
we've collated all of the common risks
that have been identified in various
government documentation.
you might find that really,
really useful if you're populating your
risk management plan or indeed your risk
register in capturing this area of AI.
The other governance and planning tool
that we've included is our generative AI
implementation strategy.
And this is a strategy that's sitting
across the organisation.
I'm not going to spend too much time
on this, but just in general,
the format for all of our planning
documentation strategies and plans,
we should have, again, the purpose,
the scope, the vision,
the objectives or the goals that we're
aiming to achieve.
We also, if we're thinking about risk,
we need to have our risk management
section in there.
outcome standard four point three outcome
standard four point four systematic
monitoring and evaluation how are you
going to be monitoring and evaluating this
policy and in the middle you should have
the the key components of what the topic
is if it's the risk management plan it's
going to be talking about risk in this
case it's the generative ai plan strategy
so we're going to be talking about our
generative ai strategic initiatives so if
i just quickly jump there
we can see just that format there,
what we are doing,
what are the strategic initiatives that
we're implementing in the organisation and
what are we aiming to achieve with those
initiatives and what performance and
outcome indicators, self-assurance,
critical examination,
of our performance and our outcomes,
outcome standard four point four,
our systematic monitoring and evaluation.
We've got our performance and outcome
indicators specified on how we're going to
measure whether our AI use is achieving a
quality implementation.
So again,
I won't spend too much more time on
that,
but hopefully you can see they're very,
very robust approach,
talking with our stakeholders,
identifying the risks,
implementing a policy position,
and then planning out with our generative
AI strategy,
how we're going to go about the effective
implementation within our organisation.
From there,
if I then jump into that implementation,
there's a whole range of tools here that
I want to just quickly mention that are
included in the pack.
First one here,
we've got a risk assessment record.
This is our standard risk assessment
approach.
It is aligned to ISO-XXX for risk
management as well.
and is helping us to do any kind
of risk assessment in the organisation.
Obviously, in this case,
we'd be looking at the risk assessment on
the use of AI.
So we're covering off the general use
case,
we're then looking at the likelihood,
the consequence,
we're coming up with a risk rating for
the implementation of the tool,
and then we're considering the treatment
or the mitigating actions to ensure that
we can implement that
tool effectively,
looking at the hierarchy of control there
and then the treatment plan for that
particular risk to get to the residual
risk rating and then the monitoring and
review arrangements of that risk.
So a standard risk assessment process that
we'd be applying for each AI tool before
it's implemented within the organisation.
The other big assessment tool that I just
quickly mentioned here as well is the
privacy impact assessment tool.
So it is an expectation for major
implementations that we would be
undertaking a privacy impact assessment as
well.
This one is aligned to the expectations
from the Office of the Australian
Information Commissioner,
as you would expect.
And again, we've got a key detail there,
not only on what the key questions are
that we should be asking,
but we've got key prompt information here
as you're completing your privacy impact
assessment on how you're going about the
use of that tool.
So again,
won't spend too much more time on that,
but just to note that the tool is
there.
So just to summarise that expectation that
if you are implementing a new AI tool
within your organisation,
that you're undertaking a stakeholder
impact analysis,
that you're undertaking a privacy impact
analysis and that you're undertaking your
risk analysis.
So those three tools going together are
really bringing down,
working through and bringing down to a
final decision around whether you can
effectively implement safely this AI for
the use case that you're talking about.
From there,
we've got some of the other tools that
I mentioned before.
We have an AI implementation checklist.
This is based on the guidance for AI
adoption from the National AI Centre.
So as you can see there,
and it is an extensive checklist.
This one is covering off six pages of
detailed requirements around those six
essential functions that must be completed
for your AI implementation,
deciding who is accountable,
and the requirements here,
and you can see the format.
It is designed as a checklist for your
implementation.
It's also very,
very handy as a checklist to monitor or
to audit what's happened if you've perhaps
done AI implementations without following
all of these steps prior to the guidance
being available.
Who is accountable?
And we've got the requirements there for
you to consider.
Understanding the impacts and planning out
that implementation.
measuring and managing the risks of the
implementation,
sharing the essential information with
your stakeholders,
including our students primarily,
testing and monitoring the implementation
on an ongoing basis,
making sure that it's still working the
way that we're expecting it to be working
and finally maintaining that human control
so there is that ongoing human oversight
of what is going on with the ai
implementation so very very useful tool
that is used for every implementation when
you get to that implementation stage of
your ai usage we've also got the ai
register there so what we're talking about
is actually tracking and monitoring all of
the AI tools in use in the organisation.
Let me just bring that up.
And you can see here,
just to fly through this quickly, the ID,
the date,
the name and version of the AI being
used,
who is the owner in the organisation,
what's the status,
we're including dropdowns,
relevant to the uh to the register
wherever possible what's the source of the
ai what goals are we aiming for the
intended use cases and the links to our
strategic objectives so again that link
back to our strategy
the known limitations of the solution and
prohibited use,
what can't we do with the solution,
any foreseeable misuse that we need to be
managing,
the data sources and types we're going to
be using,
the risk rating that we've come up with
and who's the risk owner that's going to
be monitoring the risk of the solution,
who are the stakeholders that have been
affected and the screening that we've
undertaken and the controls that we've
implemented to make sure that this is
going to be safe.
and is going to deliver an effective
outcome as we expect it to do.
And then finally,
the residual risk rating and the review
dates for that ongoing review.
So again, as you would expect,
fairly straightforward register,
keeping a track of what's going on,
allowing us to monitor and regularly
review what's happening with our AI
implementations.
The next one I want to quickly mention
here is the AI screening tool.
This is a tool from the National AI
Centre.
You can see it was released in October,
twenty twenty five.
And this is very,
very straightforward tool to be
considering, again, the risk level.
and expectations before you implement any
AI solution.
I'll just quickly run through the tool.
It's based on some key questions here.
Does the AI system access,
process or handle any personal,
sensitive or confidential information?
Does the AI interact directly with end
users?
Does it act autonomously at scale?
Is it making or supporting decisions that
are affecting your stakeholders?
Does the AI system operate in a regulated
area or have legal effect on individuals?
Would harm from a wrong decision be
difficult to contest,
reverse or provide redress for?
And is it designed for multiple purposes
or easily adapted beyond its intended use?
If you answer no to all questions,
then you can continue for implementation
within your standard organisational
approach.
If you answer yes to any of those
questions,
then it's hard to think about an AI
implementation that wouldn't have yes to
one or more of those.
then you need to do that full
implementation checklist that I just
demonstrated that we've pulled together
from those six essential requirements for
the guidance for AI adoption.
So we've got a screening tool there that
gives you the starting point.
And then we've got that extensive
implementation checklist that we're
suggesting you're probably going to be
using with every single AI implementation,
because you're going to have one or more
yeses in that screening tool where you've
to help guide and manage that AI
implementation effectively in the
organisation.
Now,
we do have a range of other implementation
examples included in the pack,
and I think this is very,
very useful for VET providers.
For example,
we've got the example AI inclusions for
your privacy policy.
So we've got the key privacy information
there.
We've actually included our full example
privacy policy, eleven pages.
And within that privacy policy,
you'll see key sections that are talking
about AI usage in the relevant Australian
Privacy Principle areas.
If I just jump right to the bottom
here,
we should be able to find a section
that jumps out on that.
So there I've got AI governance and
assurance.
There's a whole section for insertion in
the privacy policy.
But again, as I mentioned,
the information is also sprinkled
throughout the policy on how AI is
impacting privacy arrangements within your
organisation.
So if you haven't yet addressed AI within
your privacy policy,
do make sure that you are undertaking
that.
We've also included an example,
APP notice,
This is where whenever you're collecting
personal information,
think about application for enrolment as
an example,
we are required to provide an APP notice
and we have included in there a section
on the use of AI that would be
completed.
we have also included example ai
inclusions for your application for
enrollment form so we've taken that a step
further you are required to have your
privacy notice in place as part of your
application for enrollment and we've got
some example information that you might
take tailor and expand relevant to the ai
use for your organization so that you make
sure that you've got that covered at
application stage that your students or at
this point your applicants are being
effectively informed about your ai use
We've also got example inclusions for your
assessment policy,
thinking about academic integrity and the
use of AI,
permitted uses and prohibited uses overall
across your organisation when it comes to
student assessment and the students'
responsibilities.
So again,
I won't spend too much time going through
that,
but if you haven't yet built AI into
your assessment policy,
then you might find that really useful.
And similarly,
we've got example inclusions for your
assessment tools and the assessment tasks
as well.
So what instructions are you giving your
students on what they can or can't do
with AI as part of a specific assessment,
including their declarations on what they
have or haven't done with AI,
and also what instructions are you giving
your assessors
as part of your assessment tools.
That can also be adapted for instructions
for other parties who might be involved in
the evidence collection process.
Just continuing along here with the tools
that are available,
we have also included a range of
checklists for implementation,
and I'm just going to quickly grab a
couple of these and demonstrate.
The first one,
Responsible AI Adoption and Implementation
Checklist.
So this is a more extensive version of
the guidance for AI adoption from the
National AI Centre.
So I showed you before an implementation
checklist that was six pages that had the
key information.
This one is a much more detailed
implementation check if you're doing a
high risk or a wide scale adoption of
AI for a particular function.
So as you can see,
it goes up to twenty four pages.
It's still got those same essential areas.
So it starts off with area one,
decide who is accountable.
And then it's got much more detailed
information and checklists on the
implementation actions and other
information that you need to be putting in
place within your organisation for, again,
those higher risks or those wider scale.
I'm not going to go through all of
that,
but just to highlight that it's there.
Again,
it's an excellent internal auditing tool
if you want to internally audit one of
your AI implementations against the
guidance for AI adoption,
the new national guidance for what we need
to be doing.
So, again,
just connecting those two together,
we've got the shorter implementation
checklist for smaller AI implementations
and perhaps for smaller providers,
and then we've got that extensive,
twenty-four-page implementation check that
covers the full guidance from the National
AI Centre for robust AI implementations.
some of the other checklists we've got
here we have also provided a checklist on
ai generated content and this is directly
aligned to the new guidance released by
the national ai center the guide for
business on what we need to be doing
for transparency measures when we're using
ai generated content
And you can see this checklist is broken
into three areas.
They're system deployers or users of AI,
which are obviously our users in our
organizations.
There's some for system developers where
you're building AI into your applications
and you might have application suppliers,
you might have your tech suppliers who are
doing that,
or you might be building
particular applications within your
organisation where you're adding AI and
you're a system developer.
And then there's also a checklist for
model developers,
which is obviously thinking about the
likes of OpenAI and Anthropic, et cetera.
And the checklist here for system
deployers, for users,
what we need to have in place if
we're using AI-generated content for
system developers, and again,
for model developers.
And you can see the transparency
mechanisms here.
There's a three-step process.
to work out what kinds of transparency
mechanisms you need to use.
Do you need to label your AI-generated
content?
Do you need to use a watermark on
any images or videos that you might have
created with AI?
Or indeed,
what metadata do you need to be storing
on your AI-generated content?
So we've got a bit of a risk
assessment there.
Again, first of all,
isn't this risk based approach to our
operations just so evident now as expected
in the twenty twenty five standards for
RTOs?
So we're doing a risk assessment.
Then step two,
we're looking at what involvement level
was there with AI.
So we can see here the automation,
human oversight,
the extent of AI generated or modified
content,
and then the potential for AI to have
changed the meaning of content as part of
that generated content.
And then step three is to select the
transparency mechanism.
And we've got some examples here as well
in a vet provider context when you may
or may not be using labelling of the
AI generated content,
when you may or may not be required
to store the metadata,
and when you may or may not be
using watermarking of that content.
So it's quite an extensive list that
covers the full guidance that's been
provided.
If I then go to another checklist that
we've included,
the use of AI checklists here at the
bottom, I'll just quickly mention as well.
These are shorter checklists that are
being used in the flow of work.
So once you've got all of those other
arrangements in place and you've
effectively implemented your AI system,
These are the checklists that are being
used by your users as they're using AI
or are being used by your monitoring
process where you're monitoring what your
personnel or indeed what your students are
doing with AI.
In the first instance,
we've included a checklist before you act.
Before you implement anything with AI as
an individual within the organisation,
just quickly confirm that all of these
things have been covered off.
we've also then included the transparency
mechanism so if you've just generated
content from ai as a per as a
staff member personnel or as a student
this could be used for as well then
what kind of labeling do i need to
be doing
for that particular AI implementation.
So again,
you can see there sort of two checklists
in the use of AI that's designed to
be used every time AI is being used
to generate content for a particular
activity within the organisation.
So hopefully you found that really useful.
You can see some really extensive tools
there for implementation,
whether it be implementation checklists,
whether it be example inclusions in key
parts of your quality management system
within your provider, privacy,
application for enrolment, assessment,
et cetera,
and then some key checklists that you
should be using when you're using AI on
an ongoing basis for projects.
The last section I'll just quickly mention
here as well is in the monitoring and
evaluation.
Now, as I mentioned,
you've already seen checklists that can be
getting used for monitoring.
You've already seen our stakeholder survey
that you can be using for collating
feedback as part of your monitoring.
We've got two other tools here.
One is the privacy checklist for the use
of AI.
And again,
this is something that you can use
pre-confirmation when you're preparing
your AI and it's for preparing to
implement an AI tool.
And you can see the referencing there
again to those three analyses that what do
our stakeholder impact and then the
privacy impact and then the risk
assessment in order to make a judgment for
the implementation of this.
And then we've got the guidance from the
Office of the Australian Information
Commissioner around the use of AI content
from the privacy context.
So again,
it's a straightforward checklist.
If you're about to use any personal or
sensitive information,
in any AI tool, very,
very useful for you to be undertaking this
privacy checklist.
And if you're monitoring usage or indeed
doing that formal evaluation of usage,
then again,
that can be a really handy audit tool.
And speaking of audit tools,
that is the final tool that we've included
in the pack.
There's over twenty resource tools and
templates in this pack.
And the last one is our internal audit
tool for AI adoption.
We do recommend that you audit your AI
usage on an annual basis.
It is going to be across all of
your operating functions within your
organisation,
but you do need to be demonstrating that
you are reviewing and confirming that your
AI usage is compliant on an ongoing basis.
You can see in the audit criteria here,
this particular audit tool has been
designed against
the six implementation practices from our
guidance for AI adoption.
It's also aligned to the privacy
arrangements from the Office of the
Australian Information Commissioner,
and it's also aligned to the twenty twenty
five standards for RTOs.
Four point one,
looking at our governance of the
organisation,
including all of that governance required
for effective AI implementation and that
risk management from outcome standard four
point three.
So I've got the standard internal auditing
tool here.
We've got the audit plan.
We've got the sampling plan.
What parts of the organisation,
what AI implementations are you going to
sample?
We've got our effectiveness plan.
So we're looking at our quality
objectives,
our performance and outcome indicators and
targets we've set,
and then we're auditing,
are we achieving what we said we'd
achieve?
Is our performance what we expected it to
be?
Are our outcomes what we expected it to
be?
Systematic monitoring and evaluation,
critical examination for self-assurance.
And then underneath that quality audit,
we've also got the compliance audit.
So we've included the privacy guidelines,
and an auditing tool are very similar to
that checklist for that audit.
We've included the guidelines for AI
generated content and an audit tool there.
Do we have this in place for all
of our AI usage where we are generating
content?
And we then have the responsible AI
adoption checklist.
And this is the full tool.
So if you are going to do an
internal audit against the full guidance
for AI adoption from the National AI
Centre,
then we've got that full audit tool there.
I'm going to just run right through that
towards the bottom.
So that's the full twenty four pages there
to allow you to audit it.
And then we have the standards for RTO's
twenty twenty five format as well to give
you the format to be able to audit
your governance approach to AI under
outcome standard four point one and your
risk management approach to AI under
outcome standard four point three.
So very, very extensive audit tool.
You can cut and paste that audit tool
if you just want to audit the privacy
arrangements,
if you just want to audit the twenty
twenty five standards requirements,
if you just want to audit the National
Center requirements,
if you just want to audit quality
requirements and the performance and
outcome measures that you've set for the
organization.
So as I mentioned,
the extensive pack now for their providers
to make sure that we can go back,
we've all been doing lots of bits and
pieces with AI in the last three years.
Now is the time we've got the definitive
guidance that's come out
from the Office of the Australian
Information Commissioner,
from the National AI Centre.
We've got the Twenty Twenty Five Standards
for RTOs.
These are all compliance requirements and
expectations that we need to meet in our
implementation of AI.
So now that we have those confirmed
requirements,
it's time to step back and make sure
that everything that we've been doing for
the last three years with AI that we
have implemented
is meeting these requirements moving
forward and for new AI implementations
that we have the governance and the
implementation structure in place to get
that implementation right for responsible
use of AI and that we've got the
monitoring and evaluation processes in
place as well to confirm that it remains
effective,
that our use of AI is on a
quality basis,
on an ongoing basis within our operations.
So some key tips here to finish.
We need to understand all of the
requirements.
All of these requirements,
some of them are very new,
released in late,
they're now in place and we need to
make sure that we've got these
arrangements embedded within our
organisations.
We do need to approach the use of
AI in a responsible manner.
Every organisation should be doing that,
especially education institutions being
their providers should be doing that.
And we do need to justify our use
case for AI in each case.
What is that impact on our stakeholders?
What privacy impact have we undertaken to
make sure that we have got our privacy
arrangements covered?
What risk assessment have we put in place
to make sure that we are managing and
mitigating, identifying and managing?
using the twenty twenty five standards
language,
the risks that are associated with the
implementation of these tools.
We need to have all those key processes
in place.
So we do need to have that information
in place in privacy,
application for enrolment,
training delivery may require some
guidance,
certainly at the assessment delivery side
as well.
We need to be training all our personnel
in all of these expectations.
that they can't just be throwing
information into AI,
any AI tool that they want,
that they do need to be following a
robust process and they do particularly
need to be watching out for personal
information, student names, for example,
and sensitive information and what can...
and cannot be done with that information
based on your policy and procedures
framework that you put in place for them
to do.
And then we need to be tracking,
reviewing and updating that use of AI
monitoring on an ongoing basis and then
doing that periodic evaluation,
internal audit,
critical examination for self-assurance
and making sure that our AI usage is
meeting
the responsible guidelines that we expect
now within our community.
I hope you found the pack really useful
in helping you to easily understand and
implement those arrangements within your
operations.