ahmad rashid · Ahmad.Rashid@viftraining.com
Overall snapshot
3.5 / 5
PractisingPercentile unlocks at 50 respondents (4 so far)
AI Sense-Check
3.5 / 5
Treats AI output as a draft to be checked, not a finished answer. Tests claims against domain knowledge, catches fabricated citations and confidently-wrong facts, and decides what to keep, edit, or discard before it leaves your hands.
AI Working Practice
3.7 / 5
Builds AI into the way you already work - writes clear prompts, iterates when the first answer misses, and folds the tool into recurring tasks. Measures success by faster, better deliverables, not by how often the tool is opened.
AI Collaboration
3.5 / 5
Helps the team move with AI rather than around it. Explains what the tools can and can't do without overselling, shares prompts and patterns that worked, and pushes back when teammates take an output at face value or use it where they shouldn't.
AI Adaptive Mindset
3.2 / 5
Stays open as AI changes how the work gets done - relearns familiar workflows when something better appears, asks where models can fail you, and keeps confidentiality, fairness, and policy in view when deciding what to feed into a system.
VIFM courses that address the gaps in your lower-scoring factors, ranked by fit (gap size × course relevance).
How to read these - each chip is one match between a course and a factor where you're below the target of 4 / 5. The notation gap N · ×R means your gap to target ( N ) multiplied by how strongly the course is tagged to that factor's competencies (relevance ×1 light, ×2 medium, ×3 strong). The fit score is the sum of all those matches. ★ High fit marks programmes at fit ≥ 4.
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How we built this assessment
Item development, content validity, reliability plan, reference frameworks, and explicit limitations - documented in the methodology brief.
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