Alan Maguire

Another deep dive into unpacking an ASK unit of competency

Introduction

The Training Package Organising Framework 2025 has introduced two different formats for units of competency:

Element and Performance Criteria (EPC) format
Application of Skills and Knowledge (ASK) format.

The adoption of the ASK format by Jobs and Skills Councils shifts the responsibility of defining detailed performance criteria directly onto RTOs. Because this framework provides only high-level overviews of performance requirements, individual RTOs must now undertake extensive interpretation and contextualisation. This shift creates a heavy administrative burden across the VET sector and risks producing highly inconsistent training outcomes and compliance standards between different training organisations.

Explicit, industry-approved performance requirements are essential to give these new units functional meaning and operational viability. RTOs must have clear task descriptions to collect the observable or measurable evidence required to validly judge competency. Furthermore, these detailed benchmarks form the absolute foundation needed to design compliant training strategies, valid assessment tools, and high-quality learning resources.

My approach to unpacking an ASK unit

This is another deep dive into unpacking an ASK unit of competency. The previous deep dive focused on the ACMGEN3X03 Maintain cleaning, hygiene and sterility standards in animal care workplaces unit, developed by Skills Insight.

This time I’m focusing on the CHCECEXXX Support play based learning with intentionality unit, developed by HumanAbility.

Important note: Both of these units are in draft. They haven’t yet been finalised, endorsed, or released for implementation.

1. Start by analysing the Unit outcomes

Here are the Unit outcomes for the CHCCEXXX Support play based learning with intentionality unit.

Analyse by highlighting key words and writing notes.

Extract relevant information that relates to performance requirements.

2. Quick read of Knowledge and Skills

A quick read of Knowledge items and Skills items is a prelude to the analysis of the Application of knowledge and skills. A useful technique is to give each Knowledge item a ‘K’ number and each Skills item a ‘S’ number.

The following shows the Knowledge items and the allocation of ‘K’ numbers.

The following shows the Skills items and the allocation of ‘S’ number.

3. Go to the Application of knowledge and skills

Here is the Application of knowledge and skills for the CHCCEXXX Support play based learning with intentionality unit.

Analyse by highlighting key words and writing notes.

The following shows the relevant information that has been extracted from the Application of knowledge and skills, and added to the Performance requirements.

4. Go to the Performance evidence

Here is the Performance evidence for the CHCCEXXX Support play based learning with intentionality unit.

Analyse by highlighting key words and writing notes.

The following shows the relevant information that has been extracted from the Performance evidence, and added to the Performance requirements.

5. Go to Assessment conditons

The draft CHCCEXXX Support play based learning with intentionality unit has been published without the Assessment conditions being completed. Therefore, currently there is insufficient information to be used relating to Performance requirements.

6. Go to Knowledge evidence

The following shows Knowledge evidence for the CHCCEXXX Support play based learning with intentionality unit.

The ASK format for units of competency has a list of Knowledge items and a list of Knowledge evidence items. This means there is an unnecessary duplication of information. A useful technique is to use a 2-column table to show the connection between to two lists (as shown below).

Note: The above two examples are incomplete lists because the number of Knowledge evidence items very long.

7. Describe the Performance requirements

There is still work to be done to clearly describe the work tasks to be performed. A technique for describing the performance of work tasks is to use a 4-column approach. This provides a clear and structured approach to describing performance.

The following is an example of the Performance requirements described for the CHCCEXXX Support play based learning with intentionality unit.

Further work can be done to develop ‘Performance criteria’ for each step. Instead of calling them ‘Performance criteria’, they could be called ‘Assessment criteria’. This would then lead into the development of an assessment tool, such as, an observation checklist.

In conclusion

The primary shortcoming of the ASK format for units of competency is that its Performance Evidence provides only a high-level conceptual summary, lacking explicit, step-by-step requirements. Because the ASK format treats knowledge and skills as isolated pieces of information, relying on it alone makes practical competency assessment nearly impossible. We must reconstruct these broad pieces of information into explicit, industry-approved task descriptions that yield observable, measurable evidence. Only by describing these clear procedural elements can we design compliant training strategies, develop valid assessment tools, and build fully aligned training resources.

Next, I’ll explore the drastic shifts underway as the Future Skills Organisation re-engineers units of competency using the ASK format. In my opinion, it’s wild! It’s wacky! It’s un-workable! [insert smiley face]

Do you want more information?

Are you an RTO manager or course coordinator?

Could your RTO team benefit from professional development about changes to the Australian VET system? In particular, how the Training Package Organising Framework or how the new EPC and ASK formatted units impact our work as VET practitioners?

Ring Alan Maguire on 0493 065 396 to discuss.

Contact now!

Training trainers since 1986

Deep dive into unpacking an ASK unit of competency

The Training Package Organising Framework 2025 has introduced two different formats for units of competency:

Element and Performance Criteria (EPC) format
Application of Skills and Knowledge (ASK) format

Stating the obvious, a unit of competency is a unit of competency regardless of it being in an Element and Performance Criteria (EPC) format of an Application of Skills and Knowledge (ASK) format. It is a document that describes ‘competency’, and the Australian VET system is a competency-based system.

Definition of competency

Before diving deep into one of the new ASK units of competency, I would like to quickly define ‘competency’ as it applies to the Australian VET system.

Competency is defined as the consistent application of knowledge and skill to the standard of performance required in the workplace. It embodies the ability to transfer and apply skills and knowledge to new situations and environments. [1]

I want to highlight the importance of ‘performance’ in the above definition.

The Australian VET system has not been created as an education system to develop a person’s knowledge. Knowledge is needed but being able to apply that knowledge is what’s required. Likewise, skills are needed but a person must be able to apply those skills to perform work tasks.

A clear description of performance is essential

The ASK format for units of competency does not explicitly specify or describe performance requirements (however, limited or overview information is provided under the Performance evidence heading). We still need clear and industry-approved descriptions of work tasks to be performed. This is essential for collecting observable or measurable evidence to determine whether a person is competent. Also, descriptive Performance requirements are needed to design training and assessment strategies, develop assessment tools, and develop training resources.

I am assuming that an expected outcome of the Australian VET system is to develop people with the ability to perform work tasks to an expected standard (as per the definition of competency) and these people are work-ready when deemed competent.

Jobs and Skills Councils (JSCs) that use the ASK format to document units of competency are pushing the responsibility to determine Performance requirements onto all RTOs who use those units of competency. Extensive interpretation, contextualisation, and description will need to be done before the unit of competency has any usable meaning.

Several JSCs have drafted units of competency using the ASK format. I would expect the first of the ASK formatted units to be endorsed and released about August or September 2026.

The following is an example of unpacking an ASK unit of competency. Please note that the unit being used is a draft of an ASK unit developed by Skills Insight.

Before unpacking the ASK unit of competency, here is some background information.

Skills Insight is conducting a review of the Certificate IV in Veterinary Nursing. It has drafted some units of competency using the new formats specified by the Training Package Organising Framework 2025. [2]

Unpacking the ASK unit of competency

The draft ACMGEN3X03 Maintain cleaning, hygiene and sterility standards in animal care workplaces unit has been documented using the ASK format, and it was randomly selected for this example.

1. Start with the Unit outcomes

This deep dive into unpacking the ASK unit of competency starts with looking at the Unit outcomes.

2. Highlight key words and write notes

The analyse of the Unit outcomes begins with highlighting key words and writing notes.

3. Extract relevant information

The following shows the relevant information that has been extracted from the three paragraphs of the Unit outcome.

4. Quick read of Knowledge and Skills

A quick read of Knowledge items and Skills items is a prelude to the analysis of the Application of knowledge and skills. A useful technique is to give each Knowledge item a ‘K’ number and each Skills item a ‘S’ number.

The following shows the Knowledge items and the allocation of ‘K’ numbers.

The following shows the Skills items and the allocation of ‘S’ numbers.

5. Go to the Application of knowledge and skills

After a quick read of the Knowledge and Skills, go to the Application of knowledge and skills because this should guide what needs to be performed to determine a person’s competence.

6. Highlight key words and write notes

The analyse of the Application of knowledge and skills begins with highlighting key words and writing notes.

7. Extract relevant information and add to Performance requirements

The following shows the relevant information that has been extracted from the Application of knowledge and skills, and added to the Performance requirements.

8. Go to the Performance evidence

The Performance evidence gives an overview of the required performance to be assessed.

9. Highlight key words and write notes

The analyse of the Performance evidence begins with highlighting key words and writing notes.

10. Extract relevant information and add to Performance requirements

The following shows the relevant information that has been extracted from the Performance evidence, and added to the Performance requirements.

11. Go to the Assessment conditions

The Assessment conditions may provide relevant information relating to required performance.

12. Highlight key words and write notes

The analyse of the Assessment conditions begins with highlighting key words and writing notes.

13. Extract relevant information and add to Performance requirements

The following shows the relevant information that has been extracted from the Assessment conditions, and added to the Performance requirements.

14. Read and analyse the Knowledge items

The ASK format describes knowledge twice: Knowledge items and Knowledge evidence items. Sometimes there may be the same or similar items, and sometimes it takes some time to analyse and identify the connection between the two lists of knowledge items.

A useful technique is to match items from both lists of knowledge items.

Here are two examples:

A 2-column table with the Knowledge items in the left column
A 2-column table with the Knowledge evidence items in the left column.

Please note: These two examples are showing incomplete tables matching Knowledge items and Knowledge evidence items (above) and Knowledge evidence items and Knowledge items (below).

15. Read and analyse the Skills items

In this example for the ACMGEN3X03 Maintain cleaning, hygiene and sterility standards in animal care workplaces unit, I find that the Skills items are more like description of performance rather than descriptions of skills. The reading, writing, oral communication, and numeracy skills are not explicitly described. Also, I think there is some confusion between what is a skill and what is an application of skills.

16. Describe work tasks to be performed

The following is an incomplete example of how the work tasks can be described. It focuses on performing routine environmental hygiene.

In conclusion

While the ASK format for units of competency provides a broad overview under the Performance Evidence heading, it lacks explicit and detailed performance requirements. To accurately determine competency, we require clear and industry-approved descriptions of work tasks that yield observable and measurable evidence. Furthermore, these detailed performance requirements are essential for designing structured and compliant training and assessment strategies, developing effective assessment tools, and creating aligned learning resources.

By adopting the ASK format, JSCs effectively shift the responsibility of defining explicit performance requirements onto RTOs. Consequently, RTOs must undertake extensive interpretation, contextualisation, and detailed task description before these units of competency can be meaningfully applied in practice.

The ASK format for units of competency are problematic. This article primarily looked at unpacking an ASK unit of competency, without detailing the problems and additional complexity that with comes from having ASK formatted units. Also, not all JSCs are using the ASK unit format in the same way.

References

[1] Training Package Organising Framework 2025, page 13, paragraph 2

[2] https://skillsinsight.com.au/project/veterinary-nursing-review/ accessed 28 May 2026

Do you want more information?

Are you an RTO manager or course coordinator?

Ring Alan Maguire on 0493 065 396 to discuss.

Contact now!

Training trainers since 1986

Writing learning objectives for the TAESS00026 Foundation Skills Integration Skill Set

Introduction

Recently, I have been developing training and assessment resources for the TAESS00026 Foundation Skills Integration Skill Set. This skill set consists of three units of competency:

TAELLN421 Integrate core skills support into training and assessment
TAELLN423 Integrate employability skills support into training and assessment
TAELLN422 Use foundation skills resources, strategies and advice.

The following are the ‘Application’ statements for these three units.

At first glance, there seems to some common topics between the three units, and overlapping topics between ‘TAELLN421 Integrate core skills support into training and assessment’ and ‘TAELLN423 Integrate employability skills support into training and assessment’.

Exploring alignment

A practical way to explore the alignment between the three units is to place information about each unit beside each other. The following shows the there ‘Application’ statements in a 3-column table.

In the following table, key topics have been highlighted and different colours have been used to identify alignment.

Writing learning objectives

First, I identified three overlapping topics between ‘TAELLN421 Integrate core skills support into training and assessment’ and ‘TAELLN423 Integrate employability skills support into training and assessment’

The next step was to simplify the convoluted terminology and add some extra details extracted from performance criteria, in particular from ‘TAELLN422 Use foundation skills resources, strategies and advice’.

I thought that having five objectives with two sub-objectives was less overwhelming and easier to communicate than seven objectives

I think these learning objectives capture the essence of the training program. Also, I think these learning objectives gives a clear direction to be understood by learners when they commence the ‘TAESS00026 Foundation Skills Integration Skill Set’ training course.

What do you think?

Conclusion

Writing and communicating learning objectives for an entire training course is requires a different approach to learning objectives for a training session. In this article, I have demonstrated an approach that I have used recently to write learning objectives for an entire training course.

Do you like what you have seen?

AI agents in VET: A shortcut to non-compliance?

Introduction

After recently reviewing a suite of VET training and assessment materials purchased from a well-known commercial supplier, I published an article titled, ‘Human versus AI: The future of assessment design’.

The resources I had reviewed were disappointingly unfit for purpose. I identified several critical issues, including:

Overly complex numbering and an excessive amount of fragmented documents made navigation difficult.
The content was cluttered with unnecessary instructions and jargon that is neither learner-friendly nor used in actual workplaces.
The training and assessment materials lacked details and felt like generic templates had been used rather than materials tailored for the Unit of Competency.

The overall quality was bland and disconnected. This is highly characteristic of AI generated content. I later confirmed that this supplier is a ‘leading’ user of AI agents to produce their materials.

This is a following-on article warning all who use, or are considering to use, an AI agent to develop training and assessment materials. Also, it is a warning to RTOs who are intending to purchase training and assessment materials that have been produced by an AI agent.

I am not against using AI. I design and develop training and assessment materials, and I use an AI chatbot to assist me.

Let’s first look at the difference between an AI chatbot, AI assistant, and AI agent.

What is the difference between an AI chatbot, AI assistant, and AI agent?

An AI chatbot describes the ‘chat’ format or interface with AI. An AI assistant describes the overall role of helping the user. And an AI agent describes an AI that can act autonomously.

In the Australian VET system, the distinction between these three tools is defined by their autonomy and integration into an RTO compliance workflow.

Here is one specific example of how an instructional designer might use each of the three AI applications.

AI chatbot: The conversational researcher

When unpacking a new unit of competency, a chatbot acts as a reactive sounding board. You manually copy technical jargon or Performance Criteria into a separate window to request plain-English explanations or workplace scenarios. It requires a constant back-and-forth exchange, where the AI only knows what you explicitly provide in the chat. This manual ‘copy-paste’ workflow makes it a useful external tool for brainstorming and simplifying complex training requirements.

AI assistant: The integrated co-writer

As you draft learner guides or assessment tools within your word processor, an AI assistant works alongside you in real-time. Because it is context-aware, it ‘sees’ your active document, allowing it to suggest knowledge checks or generate marking rubrics based on your specific text. You can refine your tone or create content without switching windows. This integrated approach streamlines the design process by providing immediate, relevant support inside your workspace.

AI agent: The autonomous worker

For complex tasks like gap analysis, an AI agent operates with high autonomy. Once you set a goal, such as auditing assessment documents against a unit’s requirements from training.gov.au, it proactively executes a multi-step workflow. The agent navigates sites, downloads requirements, and identifies evidence gaps across files without further prompting. Unlike reactive tools, it completes the entire project independently and delivers a finished mapping matrix directly to your inbox.

The following is a summary comparing the above three AI applications.

Using AI agents to develop training and assessment materials

While AI agents offer significant efficiency in automating high-volume tasks, their use within the Australian VET sector, specifically under the 2025 Standards for RTOs, poses significant risks when developing training and assessment materials.

Here are five ways that relying on an AI agent can degrade the quality of training and assessment materials

The compliance illusion

AI agents excel at keyword matching but lack the expert judgment to determine if a task measures competency. An agent might incorrectly flag an assessment tool as ‘fully mapped’ just because it identifies specific terms from a Performance Criteria. However, it cannot determine if the task actually represents a valid or authentic measure of competency in a real-world workplace. This creates a ‘compliance illusion’ that can lead to non-compliance during a compliance audit.

Compromised intellectual property

Developing high-quality, training and assessment materials requires significant investment. Unless you are using a private AI system, uploading an RTO’s documents can mean your IP is used to train external AI models. For many RTOs, this is not just a quality issue but a major breach of data sovereignty and a loss of competitive advantage.

Pedagogically flawed

Training Packages on training.gov.au are complex and frequently updated. An AI agent may inadvertently pull historic definitions or draw from outdated datasets. Furthermore, it often lacks the ability to interpret the Companion Volume Implementation Guide, which provides the essential context for how a unit should actually be delivered and assessed, leading to mapping that may be technically correct but pedagogically flawed.

Lack of accountability for ‘hallucinated’ mapping

If an AI agent produces a mapping matrix that claims a specific content or assessment item covers a Performance Criteria or Foundation Skill when it actually doesn’t, the responsibility still rests entirely with the RTO. Unlike a human instructional designer who can provide an evidence-based rationale, an agent cannot justify its professional judgment. This lack of accountability results in unreliable mapping.

Erosion of contextualisation

A core requirement of the VET sector is contextualisation. This means tailoring training and assessments to a specific industry or learner cohort. AI agents tend to produce generic, one-size-fits-all training and assessment materials. Relying on an autonomous agent risks producing ‘cookie-cutter’ materials that fail to meet compliance or contextualised requirements.

Conclusion: Efficiency must not replace expertise

The allure of ‘set and forget’ AI agents for resource generation and compliance mapping is tempting for the time-poor VET sector. However, there is a vast chasm between functional automation and quality materials. Speed is irrelevant if the output fails a compliance audit.

Outsourcing instructional design to autonomous AI agents risks sacrificing human professional judgment. While AI can complete complex tasks at lightning speed, it lacks the capacity to understand workplace nuances, specific learner cohorts, or the pedagogical depth of a Training Package.

For RTOs, the warning is clear. Investigate how developers of training and assessment materials have used AI. Is it a chatbot for research, an assistant for drafting, or an agent for autonomous creation? As human oversight decreases, the risks to compliance and learner outcomes increase.

Technology should be embraced as a tool, not a replacement. Use chatbots to brainstorm or assistants to refine prose, but keep the human instructional designer at the centre of the development process. In an era of AI agents, human expertise is the only safeguard against a ‘cookie-cutter’ future.

Please tell me what you think!

Human versus AI: The future of assessment design

Introduction

Recently, I reviewed a suite of VET training and assessment materials purchased from a well-known commercial supplier. Despite the provider’s reputation, the resources were disappointingly unfit for purpose. Focusing specifically on the assessment components, I identified several critical issues:

Poor usability: Overly complex numbering and an excessive amount of fragmented documents made navigation difficult.
Language and literacy barriers: The content was cluttered with unnecessary instructions and jargon that is neither learner-friendly nor used in actual workplaces.
Lack of context: Assessments lacked specific scenario details and felt like generic templates rather than materials tailored to the unit of competency being assessed.

The overall quality was bland and disconnected. This is highly characteristic of AI generated content. I later confirmed that this supplier is indeed a ‘leading’ user of AI to produce their materials. This serves as a stark reminder: while AI is a powerful tool, it cannot replace the human expertise required to create meaningful, compliant VET resources.

Structuring assessment tasks

While there are typically multiple ways to structure assessment tasks, the quality of that design varies significantly. At the highest level, a structure is effective, efficient, and compliant, balancing regulatory requirements with a smooth user experience. Other designs may be adequate and compliant but ultimately burdensome, creating unnecessary hurdles for both the learner and the assessor. More concerning are structures that are inadequate but appear compliant on the surface, masking deeper flaws. Finally, some structures are simply inadequate and obviously non-compliant, failing to meet the basic standards required for a valid assessment.

To illustrate these differences in practice, I have provided the following three distinct comparisons between AI-generated and human-designed assessment tasks across various industry sectors. These three examples highlight how a human-led strategy ensures that the structure remains both pedagogical and practical. While the AI versions may tick boxes in a literal sense, the human-designed versions demonstrate a deeper understanding of how to weave complex requirements into a logical, streamlined workflow that supports an effective, efficient and compliant assessment process.

Example 1. BSBCMM411 Make presentations

The following is the Performance Evidence for the BSBCMM411 Make presentations unit of competency.

The following are assessment tasks generated by AI.¹

The following show the assessment tasks generated by a human.²

The following is a list¹ of five reasons why the human-generated assessment structure for BSBCMM411 unit is superior to the AI-generated version.

Logical chunking of workflow: The human version groups the planning, delivery, and review into a single cohesive task for each presentation (Task 2 and Task 3), whereas the AI splits the planning and delivery into entirely separate tasks.
Reinforcement of the full cycle: By requiring the candidate to complete the entire cycle (Plan-Deliver-Review) for the first presentation before moving to the second, the human structure allows for immediate application of “lessons learned”.
Explicit material development: The human-generated structure explicitly includes the “development of presentation aids” within the planning phase, ensuring this critical requirement is not overlooked, while the AI description is more generic.
Clarity on “different” scenarios: The human structure clearly mandates that Task 3 must be a second presentation that is “different to the presentation delivered in Task 2”, providing a clear instruction for meeting the unit’s diversity requirements.
Reduced administrative confusion: In the AI structure, an assessor must jump back and forth between Task 2 (Planning) and Task 3 (Delivery) to grade one presentation. The human structure allows an assessor to finalise all evidence for “Presentation 1” within a single task block.

Example 2. CHCECE037 Support children to connect with the natural environment

The following is the Performance Evidence for the CHCECE037 Support children to connect with the natural environment unit of competency.

The following are assessment tasks generated by AI.¹

The following show the assessment tasks generated by a human.²

The following is a list¹ of three reasons why the human-generated assessment structure for CHCECE037 unit is superior to the AI-generated version.

1. Direct alignment with assessment requirements

The Performance Evidence explicitly requires evidence of supporting children’s knowledge on three occasions.

Human Design: Tasks 2, 3, and 4 in the human version clearly provide these three distinct opportunities (Indoor, Outdoor, and Aboriginal/Torres Strait Islander focused).
AI Design: The AI version only lists two clear implementation experiences (Experience A and B) in Task 3, potentially failing to meet the “three occasions” mandate.

2. Specific inclusion of cultural perspectives

The unit requires that at least one occasion must involve Aboriginal and/or Torres Strait Islander peoples’ use of the natural environment.

Human Design: Dedicates a specific, standalone task (Task 4) to ensure this mandatory requirement is met and observed.
AI Design: Completely omits this specific cultural requirement in its brief descriptions, focusing instead on generic activities like “seed growing” or “scavenger hunts”.

3. Clear Indoor/Outdoor distinction

The unit requires one indoor and one outdoor opportunity.

Human Design: Explicitly structures Task 2 as an indoor activity and Task 3 as an outdoor activity, ensuring the candidate covers both environments.
AI Design: Focuses heavily on the outdoor environment (Task 2 audit and Task 3 “nature play”), without clearly designating or requiring a specific indoor engagement.

Example 3. CPCCCA3010 Install windows and doors

The following is the Performance Evidence for the CPCCCA3010 Install windows and doors unit of competency.

The following are assessment tasks generated by AI.¹

The following show the assessment tasks generated by a human.²

The human-generated assessment tasks ensure full compliance with the specific Performance Evidence for CPCCCA3010 unit. The following is a list¹ of three reasons why the human-generated assessment structure is superior to the AI-generated version.

1. Inclusion of specific door types

The Performance Evidence requires the installation of a sliding cavity door unit and door and a pair of doors.

Human Design: Includes “Task 4” specifically for the sliding cavity door and “Task 5” for the pair of doors.
AI Design: Uses generic categories like “External Door” and “Internal Door”, which fails to explicitly require these two specialised installation types.

2. Accurate quantity of installations

Human Design: The human-generated tasks align perfectly with the requirement to install “a” (single) standard window
AI Design: The AI-generated Task 2 requires the candidate to install two windows, which adds an unnecessary burden not specified in the performance evidence.

3. Integration of planning and installation

Human Design: Integrates the “plan” and “prepare” requirements directly into every individual practical task (Tasks 2, 3, 4, 5, and 6). This ensures that the planning is context-specific to the unique requirements of a window, a sliding cavity door, or a pair of doors.
AI Design: Separates “Planning & Compliance” into a standalone Portfolio (Task 3). By treating planning as a generic administrative exercise rather than an embedded part of the installation process, the AI version risks a disconnect between the candidate’s theoretical plan and the actual technical preparation required for different types of frames and doors.

Conclusion: Why the human designer is irreplaceable

The examples above highlight a consistent pattern: while AI can generate a list of tasks that look like an assessment, it lacks the professional judgment to design a strategy that is actually fit for purpose.

The disparity between these two approaches boils down to three critical factors:

Nuance and compliance: As seen in the CPCCCA3010 and CHCECE037 examples, AI frequently misses specific requirements that are essential for a finding of competency. A human designer reads between the lines of a Training Package to ensure no mandatory evidence is overlooked.
Pedagogical workflow: AI tends to “atomise” tasks into clinical, disconnected steps. In contrast, human designers understand how a job actually functions. By grouping planning, execution, and review into a single cohesive task, as seen in the BSBCMM411 example, humans create a natural assessment flow that mirrors real-world workplace practice rather than a fragmented digital checklist.
The “Goldilocks” principle of evidence: AI often oscillates between two extremes: providing too little detail or creating “assessment bloat” by requiring more work than is necessary. A human expert knows how to design a strategy that is “just right”, meeting every requirement specified by the unit of competency without placing an unnecessary administrative burden on the learner or the assessor.

AI is a powerful assistant for brainstorming or drafting, but it is a poor architect. In the high-stakes environment of VET compliance, an assessment strategy is more than just a document. It is a roadmap that needs to be accurate and compliant. The “human-in-the-loop” must remain the “human-at-the-helm.”

Investing in human-led design isn’t just about avoiding “bland” materials; it’s about ensuring that our VET students are truly competent and that our RTOs remain compliant.

Footnotes:

¹On the 2nd of March 2026, Gemini was the AI platform used to generate the assessment tasks for the three examples. It was also used to compare the assessment structure generated by AI and the human.

² Alan Maguire was the human who generated the assessment tasks for the three examples. He has had more than 40 years experience designing training and assessment. Alan may be getting older, but he is not yet redundant.