Work-sample evidence for AI capability

See who can actually work with AI — before you rely on it.

AISkillProof puts employees in realistic AI tasks and turns their work into capability evidence, targeted learning actions and a fresh reassessment that proves improvement.

Work

Realistic assignments instead of knowledge questions only.

Evidence

Every task maps output to concrete skills and review rules.

Action

Gaps drive learning and a fresh equivalent reassessment.

In formal assessments, objective items are scored on the server and answer keys stay there. Public examples are practice material.

Live sample task

Judgment · 90 sec · work example

Context

You have just finished a customer call. The customer wants to start on 3 November. You said you still have to check the schedule. Then you ask AI to write the follow-up email.

A wish becomes a promise

Your notes and the email disagree

Your notes: ‘Start wanted on 3 November. Schedule still to check. Nothing confirmed.’ The email: ‘As agreed, we start on 3 November.’

What do you do before you send that email?

What this task measures

Whether you notice that a smooth email promises more than was agreed, and whether you check that before it is sent.

In the news · HLN

“Four in ten Belgians lack digital skills while AI advances rapidly”

AI is changing work faster than skills can keep up. For a company, that means knowing who on your team really uses AI safely and well, instead of assuming it. That is exactly what Aiskillproof makes visible.

Read the article on HLN (in Dutch)

Not a quiz bank

This is what employees are actually asked to do.

AISkillProof does not measure confidence. The employee has to make, check, decide or structure something. That produces much richer evidence than asking ‘how good are you with AI?’

01 · Prompt repair

Turn a chaotic briefing into a usable AI instruction.

Sample scenario

A sales manager dumps loose notes, pricing information and customer context into an AI tool and asks ‘write email’. You need to restructure the assignment so the output is useful and controllable.

What the employee does

Writes a prompt with goal, audience, context, constraints, output structure and a verification instruction.

What the employer gets

Prompting & Instruction, task decomposition, context management and controllability.

02 · Verification challenge

Find the error hidden inside convincing AI output.

Sample scenario

An AI summary uses two conflicting figures and presents one conclusion as certain. The employee must decide what needs verification first.

What the employee does

Investigates source provenance, recency, calculation and uncertainty before the output is used.

What the employer gets

AI Judgment, AI Literacy, verification behaviour and risk awareness.

03 · Workflow design

Build an AI workflow that does not break when reality deviates.

Sample scenario

Every Friday three data sources are combined into an operational report. The employee must place input validation, AI steps, exceptions and human controls.

What the employee does

Designs the sequence, defines checkpoints and states when automation must stop or escalate.

What the employer gets

Workflow & Automation, reliability thinking and human-in-the-loop design.

04 · Risk triage

Decide which data may and may not enter the AI workflow.

Sample scenario

An HR team wants AI to summarize CVs, performance notes and salary data. Not all data and tools are appropriate for that use.

What the employee does

Selects minimum necessary data, approved environment, access rights and required review before processing starts.

What the employer gets

AI Governance, data minimization, tool judgment and policy awareness.

These are public, simplified work samples. Real assessments use versioned items, server-side answer keys and auditable review.

Try a short exercise

From task to decision

Not another score. An evidence chain a manager can act on.

The value is not the number itself, but the traceable path from work to evidence, gap, learning action and reassessment.

01→

Work task

Resolve source conflict

The employee receives output, sources and time pressure — not just a theoretical question.

02→

Evidence

Verification is missing

The answer produces skill-level evidence with weight, item version and audit trail.

03→

Diagnose

AI Judgment is the gap

The gap follows from multiple evidence points, not one wrong answer.

04→

Learning

Small targeted intervention

No generic three-hour AI course; only the missing capability gets priority.

Proof

Fresh reassessment

Same skill construct, new items. Baseline and reassessment make improvement visible.

Synthetic example

An employee shows weak verification behaviour across three AI Judgment tasks. The system recommends a short source-checking intervention, then schedules a fresh equivalent reassessment. Only the real delta is reported.

Trust

Measure employees without watching them.

AISkillProof is for learning and measurement. A score can be explained. A manager only sees what they need to help the team.

PER ORGANISATION · EXPLAINABLE · TRACEABLE

Learning comes first

The normal experience helps people improve. There is no public ranking.

Each company only sees its own data

A company only sees its own results. That boundary is checked on the server.

A score you can explain

Only tasks with a fixed, checkable answer earn points. Free text is not scored: length or keywords prove nothing, and no model guesses a score.

Privacy from the start

Little data, access by role, and limited retention. That is part of the product, not a footnote.

AI Act Article 4

Demonstrate AI literacy across your company

Since 2025, every company that uses AI must take measures for its staff's AI literacy. See what the law asks, what we measure per department and how to document it.

What Article 4 asks of you →Free record template →See all AI tests →Custom test on your own AI tools →AI test for job applicants →

By department

From the knowledge base

All articles →

MEASURE → IMPROVE → PROVE

Three tasks that show what AISkillProof actually measures.

No screenshot product tour. Make a judgment call, repair a bad prompt and design a reliable AI workflow yourself. These are simplified public work samples — not a formal assessment.

See who can actually work with AI — before you rely on it.

This is what employees are actually asked to do.

Turn a chaotic briefing into a usable AI instruction.

Find the error hidden inside convincing AI output.

Build an AI workflow that does not break when reality deviates.

Decide which data may and may not enter the AI workflow.

Not another score. An evidence chain a manager can act on.

Resolve source conflict

Verification is missing

AI Judgment is the gap

Small targeted intervention

Fresh reassessment

Measure employees without watching them.

Learning comes first

Each company only sees its own data

A score you can explain

Privacy from the start

Demonstrate AI literacy across your company

By department

From the knowledge base

Three tasks that show what AISkillProof actually measures.

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