Our values
Integrity before appearance
We prefer an honest limitation to an impressive but unsupported claim. An assessment result is evidence about a particular set of responses, not a complete judgement of a person.
Respect for people
Treat learners fairly across experience levels, functions and backgrounds. Avoid harassment, credential inflation and discriminatory language. Seek consent before sharing another person’s information.
Confidentiality in context
Manufacturing examples must respect employer, customer and supplier obligations. Use fictional or properly authorised information instead of proprietary drawings, process settings, safety work products or incident records.
Accountability for technology
AI assistance does not transfer responsibility for accuracy, fairness or engineering decisions. Human accountability, source checks and an opportunity to challenge material errors remain important.
