Practical capability that can be observed, tested and improved.
We are not building training around completion alone. The Academy is designed to help learners use AI more effectively, recognise where it can fail, and retain responsibility for the final result.
From awareness to workplace application.
The intended outcomes focus on practical behaviour, not just remembering definitions.
Recognise appropriate AI use
Identify lower-consequence tasks where AI can reasonably assist, and recognise when a task requires additional review or should remain outside AI decision-making.
Give better instructions
Provide useful context, facts, audience, objective, format and limitations so AI has a better chance of producing a useful first result.
Challenge AI output
Identify unsupported assumptions, invented details, missing context, uncertainty and statements that require verification.
Protect workplace information
Recognise that commercial availability does not equal workplace approval, and apply employer requirements around confidentiality, personal information and proprietary data.
Use AI to improve work
Apply AI to suitable communication, planning, summarising, first-draft and administrative tasks while preserving intended meaning and workplace context.
Retain human accountability
Understand that AI can assist with the work but does not become the competent person, supervisor, engineer, trainer, statutory decision-maker or final approver.
Three questions should guide every pilot.
Measure appropriate tasks before and after training to see whether AI reduces unnecessary administration without introducing unacceptable rework.
Compare clarity, structure, usefulness and completeness of human-reviewed outputs rather than relying on learner satisfaction alone.
Test whether learners are better at identifying unreliable, inappropriate or high-consequence AI use after the training.
A repeatable way to move from task to accountable outcome.
RISE™ is intended to create observable behaviour that can be practiced and reviewed.
What an employer pilot can capture.
The exact measures should be agreed with the employer and matched to the pilot cohort and task type.
| Area | Possible evidence | What we are looking for |
|---|---|---|
| Time | Baseline vs post-training task time | Reduction in unnecessary drafting or organisation time without quality loss. |
| Quality | Human-reviewed work samples | Clearer structure, better communication and fewer avoidable omissions. |
| Judgement | Scenario-based assessment | Better identification of hallucinations, unsupported statements and high-consequence uses. |
| Information handling | Scenario responses | Improved recognition of confidentiality, privacy and employer-approved system requirements. |
| Confidence | Pre/post learner self-rating | Confidence that is matched by better review behaviour—not blind trust in AI. |
| Workplace relevance | Supervisor and participant feedback | Evidence that the examples and workflows reflect real industrial work. |
Judgement matters more than multiple-choice recall.
Knowledge checks have a place, but practical assessment should increasingly ask learners to work through realistic scenarios.
Scenario
Provide rough shift notes, a workplace email or supplied information that needs organising.
AI-assisted task
Ask the learner to use AI appropriately to create a useful first result.
Human review
Require the learner to identify what must be corrected, verified, withheld, escalated or approved before use.
Want to test these outcomes with a real workforce?
We welcome conversations with mining, trades, maintenance, industrial and training organisations interested in a controlled pilot.