workshop • Compliance and Ethics

Implement AI Workflows That Improve RTO Performance and Stand Up to Audit Master Class

Dates & Locations Course Agenda View Takeaways

Webinar Details

Duration

14 Hours

Delivery

Face to Face Workshop or Online Workshop

Level

Strategic - Advanced

Location

Sydney Masonic Centre

Price

$950 - $1200

Date

July 30 - 31, 2026

Time

9:00 am AEST

Facilitator

Javier Amaro Castillo

Online Face to Face

Generative AI is already being used across RTOs to draft resources, support learners, prepare communications, streamline workflows and manage quality activity. The commercial risk is not whether AI will be used — it is whether your RTO can control, review and evidence that use when it matters.

This practical master class helps your RTO move from fragmented AI experimentation to governed, evidence-based implementation across training, assessment, learner support and quality systems — without compromising professional judgement, assessment integrity or audit defensibility.

PROBLEM

Why Uncontrolled AI Use Creates RTO Risk

AI is already influencing RTO operations — often before governance systems, staff capability and approval controls have caught up. That gap creates audit exposure.

Trainers, assessors, instructional designers, support staff and managers may already be using AI to draft learning content, assessment questions, feedback, policies, reports, emails and improvement actions. Used well, AI can improve efficiency and quality. Used without controls, it can create inconsistent outputs, evidence gaps and weak defensibility.

The risk is not theoretical. Uncontrolled AI use can compromise the integrity of training and assessment by producing content that looks polished but is inaccurate, poorly aligned to units of competency, inconsistent with the TAS, or unsuitable for the learner cohort.

It can also create governance exposure where the RTO cannot demonstrate:

Who used AI

What it was used for

How outputs were reviewed

What was approved

What evidence was retained

How risks were controlled

Under the Standards for RTOs 2025, RTOs need systems that operate effectively in practice. If AI contributes to training, assessment, student support, compliance documentation or quality assurance, your RTO must be able to show that its use is controlled, reviewed and defensible.

The problem is not AI. The problem is unmanaged AI use that leaves no reliable evidence trail.

SOLUTION

What This Workshop Will Help You Achieve

This master class positions generative AI as a controlled operational capability — not a content shortcut, novelty tool or replacement for professional judgement.

You will be able to identify where AI can safely support RTO work, where it should be restricted, and what governance controls are required to protect consistency, compliance and evidence integrity.

This will enable your RTO to use AI to improve productivity while maintaining clear human oversight, defensible review processes and reliable records.

The outcome is a stronger operating model for AI: practical enough for daily work, disciplined enough for compliance scrutiny, and flexible enough to support innovation.

You will be able to:

identify AI use cases that are safe, useful and aligned to RTO operations
distinguish low-risk productivity uses from high-risk assessment and compliance uses
design AI-supported workflows with documented review and approval points
evaluate AI-generated training and assessment content against the Principles of Assessment and Rules of Evidence
detect hallucinations, bias, unsupported claims and weak evidence alignment
establish practical controls for prompt use, output review, version control and evidence retention
support staff to use AI consistently within clear RTO-approved boundaries

TAKEAWAY

What You Will Take Back to Your RTO

This is a working master class. The value is not just in understanding AI — it is in leaving with practical artefacts your RTO can adapt, implement and evidence.

You will take back a practical implementation toolkit to help your RTO convert AI from informal staff experimentation into a governed operational capability.

This includes an AI use case selection framework to help your team decide which tasks are appropriate for AI support, which require tighter controls, and which should remain outside AI use because of assessment, privacy, authenticity or compliance risk.

You will also receive a prompt design framework and practical prompt library for RTO-specific tasks, including learning resource drafting, assessment support, learner communication, quality review, compliance documentation, feedback analysis and administrative workflows.

To strengthen defensibility, the master class provides an AI output review checklist that can be used to test outputs before they are relied on. This supports review for accuracy, hallucination, bias, unit alignment, TAS consistency, learner suitability and evidence sufficiency.

You will also take back:

AI Workflow Mapping Tool

For documenting how AI is used in a process.

Human Oversight and Approval Model

To clarify who reviews, approves and retains evidence.

AI Risk Control Checklist

For identifying audit exposure before implementation.

Evidence Retention Guide

For documenting prompts, outputs, reviews and decisions.

30–90 Day AI Implementation Action Plan

To move from trial use to controlled practice.

AI-supported Workflow Examples

For resource development, learner support, reporting and quality assurance.

These tools are designed to help your RTO build consistency across staff, reduce undocumented AI use, and create a clearer evidence trail for governance, internal audit and continuous improvement.

Online Face to Face

AUDIENCE

Who This Webinar Is For

This master class is designed for RTO professionals who need AI to deliver practical value without increasing compliance risk.

Primary Audience

The primary audience is RTO leaders and implementation owners responsible for quality, compliance, training systems, learning design and operational performance.

RTO CEOs and Senior Leaders

Compliance and Quality Managers

Training Managers and Heads of Learning

Instructional Designers and Resource Developers

Managers Responsible for Digital Transformation, Quality Systems or Operational Improvement

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.

Secondary Audience

This session is also relevant for staff who use or support AI-enabled work in day-to-day RTO operations.

Trainers and Assessors

Student Support Staff

Administration and Operations Staff

VET Consultants Supporting RTO Systems, Resources or Compliance

Corporate Training and L&D Professionals Working in Regulated or Quality-driven Environments

For these roles, the master class provides clear boundaries around what AI can support, what must be reviewed, and how professional judgement remains central to defensible RTO practice.

How the Session Is Delivered

The master class is delivered as a structured, facilitated working session focused on practical implementation.

You will work through real RTO scenarios, develop prompts, review AI outputs, identify risk points and consider how AI-supported workflows can be governed in your own operating context.

The session includes live facilitation, guided activities, practical examples, templates, checklists and Q&A. You will examine how AI can support training design, assessment support, learner services, reporting, quality assurance and routine administration — while maintaining human oversight and evidence control.

You will also practise reviewing AI-generated content for accuracy, compliance risk, assessment integrity and suitability before use.

The program is delivered either face-to-face over two full days or online over four consecutive half-day sessions.

The workshop includes:

RTO-specific AI use cases
guided prompt development
workflow design activities
output review and risk-checking exercises
templates, checklists and implementation tools
facilitated discussion on governance and audit defensibility
Certificate of Participation and 25 CPD points

AGENDA

Workshop Agenda

The workshop follows a structured pathway from compliance architecture to operational control. Each session builds toward a practical compliance model your RTO can adapt and implement.

Governed AI Use, Risk Controls and Prompt Capability

Time	Session	Focus: What We'll Do
9:00 – 10:30	Session 1 AI in RTOs — From Uncontrolled Use to Governed Operating Model	Establish a shared baseline for how generative AI works and where it creates risk in RTO operations. We will clarify the difference between traditional AI, machine learning and generative AI in practical RTO language, then examine how AI can affect training design, assessment support, learner communication, compliance documentation and quality systems. The focus is on moving from informal experimentation to controlled implementation.
10:30 – 10:45	Morning Break
10:45 – 12:30	Session 2 AI Use Cases That Improve Performance Without Creating Audit Exposure	Translate AI capability into practical, defensible RTO workflows. Participants will identify where AI can safely augment work and where it must be restricted or tightly controlled. Examples include TAS drafting support, learning activity prototyping, assessment item ideation, validation evidence packaging, student communications, compliance reporting and administrative workflows.
12:30 – 1:15	Lunch Break
1:15 – 3:00	Session 3 Governance, Privacy, Ethics and QMS Controls	Build the governance layer required to make AI use defensible. We will examine privacy, confidentiality, bias, fairness, data security, version control, approval pathways and QMS alignment. Participants will draft practical controls that define who can use AI, for what purpose, with what level of review, and what evidence must be retained.
3:00 – 3:15	Afternoon Break
3:15 – 4:30	Session 4 Prompt Design for Consistent, Reviewable RTO Outputs	Move from ad hoc prompting to structured prompt design. Participants will learn how to define role, audience, task, source material, constraints, quality criteria and review requirements. The focus is on producing usable first drafts that can be checked, improved and evidenced — not relying on AI outputs at face value.

Assessment Integrity, Workflow Documentation and Implementation

Time	Session	Focus: What We'll Do
9:00 – 10:30	Session 5 AI-Supported Training Resource Development With Quality Controls	Apply AI to accelerate resource development while protecting quality, contextualisation and TAS alignment. Participants will work through how to use AI for first-draft learning content, learner guide refinement, activity design, readability improvement, contextualised examples and resource adaptation. The emphasis is on controlled acceleration, not autopilot content creation.
10:30 – 10:45	Morning Break
10:45 – 12:30	Session 6 Assessment in an AI Era — Validity, Sufficiency and Evidence Defensibility	Stress-test AI-generated outputs against competency-based assessment expectations. Participants will review AI-generated assessment content and identify risks related to validity, sufficiency, authenticity, reliability, learner fairness, unit alignment and TAS consistency. The objective is to know what to improve, what to reject and what must be validated before implementation.
12:30 – 1:15	Lunch Break
1:15 – 3:00	Session 7 Prompt Libraries and AI Workflow Blueprints	Convert useful prompts into repeatable RTO intellectual property. Participants will build a practical prompt library with categories, ownership, versioning, quality controls and review cadence. We will then connect prompts into governed workflows, such as source material analysis → outline → learning activity → assessment support → validation evidence package.
3:00 – 3:15	Afternoon Break
3:15 – 4:30	Session 8 AI Rollout Roadmap — From Pilot to Controlled Implementation	Bring the master class together into an implementation pathway. We will examine where assistants, agents and conversational AI may fit into RTO operations, including learner support and internal knowledge workflows, while defining clear boundaries. Participants will then develop a 30–60–90 day implementation roadmap with pilot scope, governance sign-offs, staff enablement, evidence capture and impact measures.

IMPLEMENTATION

Implementation Model: Learning → Application → Impact

This master class is designed to support workplace implementation, not just awareness.

Learning

You will build practical capability in using generative AI across RTO workflows, including training design, assessment support, learner communication, documentation, reporting and quality assurance.

You will also strengthen your ability to critically review AI outputs before they are used in training, assessment, student support or compliance contexts.

Application

Within 30–90 days, you will be able to implement at least one AI-supported workflow in your area of responsibility.

That workflow should include a clear use case, defined staff responsibilities, documented review points, approval criteria, evidence retention expectations and quality checks before implementation.

Impact

This will enable your RTO to improve productivity while maintaining stronger control over quality, consistency and compliance evidence.

With disciplined implementation, your RTO can reduce resource development time, lower rework caused by poor first-draft materials, improve staff confidence in approved AI workflows, and reinvest time savings into learner support, validation, continuous improvement and innovation.

URGENCY

Why This Matters Under the 2025 Standards

The Standards for RTOs 2025 increase the importance of self-assurance, governance, workforce capability, quality training and assessment, student support, risk management and continuous improvement.

AI now intersects with all of these areas.

If AI is used to draft training resources, support assessment preparation, prepare student communications, analyse feedback, write policies or produce compliance documentation, your RTO needs to demonstrate that outputs are reviewed, approved, fit for purpose and supported by evidence.

The RTOs that gain the most from AI will not be those that use it fastest. They will be the RTOs that implement it with operational discipline, human oversight and an evidence trail that can withstand scrutiny.

This matters because AI can affect the quality and defensibility of:

training resources and learning activities
assessment content and assessment support materials
student communication and support information
compliance records and reporting
continuous improvement documentation
staff workflows and role accountability

Upcoming Dates

Choose Your Session

Date	Time	Location	Mode