FACE-TO-FACE • DIGITAL SKILLS & TECHNOLOGY • COMPLIANCE & ETHICS • STRATEGIC – ADVANCED
Advanced RTO AI Workflow Implementation Master Class
Build, Test and Govern an AI-Enabled Workflow for Your RTO
MASTER CLASS DETAILS
Generative AI is already changing how RTOs develop learning resources, review documentation, support learners, analyse information and manage operational workloads.
The strategic challenge is no longer whether staff will use AI. It is whether the RTO can convert fragmented experimentation into a controlled operational capability that improves performance without compromising professional judgement, assessment integrity, privacy or regulatory defensibility.
The Advanced RTO AI Workflow Implementation Master Class is a two-day, face-to-face implementation program for experienced RTO professionals who are ready to move beyond introductory AI concepts and isolated prompt use.
The master class uses a complete worked example, From Manual Resource Development to a Governed AI-Supported Workflow, to demonstrate the implementation methodology from beginning to end.
You will then apply the same methodology to one genuine workflow from your own RTO. Throughout the two days, you will progressively scope, redesign, prototype, test, govern and document that workflow.
By the conclusion of the master class, you will leave with a tested AI-supported workflow, defined human oversight controls, quality assurance criteria, evidence required
IMPORTANT
This Is Not an Introductory AI Course
This master class is not designed to explain basic AI terminology, provide introductory demonstrations or teach participants how to write isolated prompts.
It is designed for experienced RTO professionals who already use generative AI and now need to convert that capability into a reliable, repeatable and governed operational workflow.
The focus is implementation. You will examine how a complete AI-supported resource development workflow is designed, tested and governed before applying the same implementation architecture to your own selected process.
The master class will challenge participants to make practical decisions about:
WORKED EXAMPLE
From Manual Resource Development to a Governed AI-Supported Workflow
Before participants develop their own workflow, the facilitator will demonstrate the complete implementation methodology through a worked RTO example.
The example follows an RTO that wants to improve the development of learning resources for a nominated unit of competency and learner cohort.
The current manual process involves an instructional designer or trainer:
Although this process relies heavily on professional expertise, it can also involve duplicated effort, inconsistent drafting practices, extended development time and poorly documented review decisions.
The worked example demonstrates how generative AI can support selected stages of the process without replacing the instructional designer, trainer, assessor, subject-matter expert or approval authority.
The example will also demonstrate the boundaries of appropriate AI use.
Participants will examine how the RTO can use AI to assist with:
- analysis of controlled source materials
- preparation of a draft resource structure
- development of first-draft explanatory content
- generation of contextualised examples
- development of learning activities
- readability and accessibility review
- identification of potential content gaps
- preparation of review and revision records
AI will not be authorised to:
- determine whether a resource is compliant
- independently interpret ambiguous training product requirements
- approve the final resource
- replace industry or subject-matter expertise
- make unsupported assumptions about the learner cohort
- introduce information that cannot be verified
- use confidential, personal or unauthorised material
Through the worked example, participants will see how the workflow is converted from an informal series of prompts into a controlled operational process with approved sources, defined responsibilities, review criteria, version control and an evidence trail.
METHODOLOGY
The Worked Example Methodology
The resource development example will be progressively developed across both days.
YOUR WORKFLOW
Apply the Methodology to Your Own Workflow
After each stage of the worked example, you will apply the same methodology to your own selected RTO workflow.
You will not wait until the example is complete before beginning your own work.
The delivery sequence follows a structured cycle:
This approach provides a clear implementation benchmark while maximising the time available for participants to build their own operational solution.
OUTCOME
The Implementation Outcome
By the end of the master class, you will have developed an RTO AI Workflow Implementation Pack for one priority operational process.
Your implementation pack will include:
These are not generic presentation handouts. They are working implementation artefacts that you will progressively develop, test and refine during the master class.
CAPABILITY
What You Will Be Able to Do
Following the master class, you will be able to:
CHOOSE YOUR WORKFLOW
Choose the Workflow You Will Build
Each participant or RTO team must bring one genuine workflow to develop during the master class.
Suitable workflows may include:
The selected workflow should be important enough to create measurable operational value but sufficiently contained to be prototyped and tested during the master class. Participants will nominate one or two potential workflows through the pre-master-class diagnostic. The final workflow will be confirmed during Session 1 after participants have reviewed the worked example and applied the selection criteria.
AUDIENCE
Who This Is For
The master class is designed for RTO professionals who have responsibility for implementation, quality assurance or operational performance.
Primary Audience
For CEOs and senior leaders, the focus is governance, operational control and risk visibility. For compliance and quality teams, the focus is defensibility, evidence and audit readiness. For training and design teams, the focus is faster development, stronger quality assurance and more consistent implementation.
RTO Implementation Teams
RTOs are encouraged to send two or three participants representing complementary responsibilities, such as:
AI implementation is rarely owned by one person. A cross-functional team can make decisions during the master class and accelerate implementation after the program. Trainers, assessors, student support staff and administrators may also attend where they have responsibility for designing, owning or implementing the selected workflow.
DELIVERY
How the Session Is Delivered
This is a facilitated build environment, not a presentation-led workshop. Participants work on their selected RTO workflow throughout both days and progressively develop the operational design, prompt architecture, quality controls, governance requirements and implementation documentation needed for deployment.
Approximately 80 per cent of the master class is dedicated to facilitated development, testing, peer review and implementation planning.
The delivery methodology includes:
- a complete facilitator-led worked example
- concise expert briefings
- live workflow demonstrations
- facilitated process-mapping activities
- structured implementation templates
- AI prototype development
- output review and quality testing
- red-team challenge activities
- peer review and professional challenge
- facilitator feedback
- implementation planning
- presentation of the final workflow
The emphasis is on application, testing and decision-making.
AGENDA
| Time | Session | Focus: what we’ll do |
|---|---|---|
| 9:00–10:30 | Session 1: Understand the Worked Example, Select Your Workflow and Build the Implementation Case |
The master class begins with the worked example: From Manual Resource Development to a Governed AI-Supported Workflow.
The facilitator will introduce the RTO scenario, map the resource-development challenge and demonstrate how the proposed AI workflow is evaluated for value, feasibility and risk. Participants will then assess their nominated workflows and select the process they will develop during the master class. You will define:
|
| 10:30–10:45 | Break | |
| 10:45–12:30 | Session 2: Map the Current Process and Classify the Risks |
The facilitator will map the current manual resource-development process and identify duplicated effort, inconsistent practices, approval gaps and areas of avoidable rework.
Participants will then map their own current-state workflow. The activity will examine:
|
| 12:30–1:15 | Lunch | |
| 1:15–3:00 | Session 3: Design the Target-State Workflow and Governance Controls |
The facilitator will redesign the resource-development process to demonstrate where AI can support analysis, drafting, contextualisation and quality review without replacing professional accountability.
Participants will then redesign their own selected workflow. The target-state design will identify:
|
| 3:00–3:15 | Break | |
| 3:15–4:30 | Session 4: Build the Prompt Architecture and First Prototype |
The facilitator will demonstrate how the resource-development workflow is supported by a controlled prompt and instruction architecture.
Participants will examine how prompts are structured to maintain alignment with approved sources, learner requirements, quality criteria and professional review expectations. The prompt architecture will define:
|
| Time | Session | Focus: what we’ll do |
|---|---|---|
| 9:00–10:30 | Session 5: Quality Assurance and Red-Team Testing |
The facilitator will deliberately challenge the resource-development prototype using:
|
| 10:30–10:45 | Break | |
| 10:45–12:30 | Session 6: Establish Training, Assessment and Output Quality Controls |
The facilitator will demonstrate how the AI-supported learning resource is assessed against defined quality and approval criteria.
The worked example will examine:
|
| 12:30–1:15 | Lunch | |
| 1:15–3:00 | Session 7: Integrate the Workflow With the RTO’s QMS |
The facilitator will demonstrate how the resource-development prototype is converted into a controlled organisational process.
The worked example will address:
|
| 3:00–3:15 | Break | |
| 3:15–4:30 | Session 8: Prepare the Pilot and Executive Implementation Briefing |
The facilitator will demonstrate how the resource-development workflow is prepared for a controlled workplace pilot.
The worked example will establish:
|
TAKEAWAYS
What You Will Take Back to Your RTO
You will leave with a completed or substantially developed implementation package for one genuine RTO workflow.
You will also receive:
The worked example and implementation toolkit can subsequently be adapted to evaluate and develop additional AI workflows across the RTO.
These are not generic presentation handouts. They are working implementation artefacts that you will progressively develop, test and refine during the master class.
Register NowGOVERNANCE
Why Governance and Evidence Matter
AI use may intersect with training, assessment, learner support, workforce capability, governance, privacy, risk management, quality assurance and continuous improvement.
The strongest RTOs will not be those that use AI most aggressively. They will be the RTOs that use it deliberately, measure its value, retain professional accountability and maintain evidence that the workflow operates as intended.
Where AI contributes to an RTO process, management should be able to determine:
- who is authorised to use it
- what it may be used for
- which platforms are approved
- what information may be entered
- which sources must be used
- how outputs are checked
- who approves the final work
- what evidence is retained
- how incidents and exceptions are managed
- how the RTO evaluates whether the workflow remains effective
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Informal AI experimentation may create isolated efficiencies, but it does not create sustainable organisational capability.
This master class provides the structured environment, implementation tools and expert guidance required to convert one priority RTO process into a controlled AI-supported workflow.
You will first see how a manual resource-development process can be transformed into a governed AI-supported workflow. You will then build, test and document a workflow for your own RTO.
You will not leave with only ideas about what your RTO could do. You will leave with a tested workflow, documented controls and a practical pathway to implementation.
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