The Tool Desk
Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Managers can assess employees’ AI skills by observing them complete realistic, role-relevant work—not by relying on confidence ratings or claims of experience. Give employees comparable tasks using approved tools, then score both how they approach the work and the quality and safety of the result with a shared rubric. Treat this as a practical assessment design, not a validated universal employee test.
What should an employee AI assessment measure?
Assess the actions a person must perform in their job, rather than general familiarity with AI. The AI Competence Framework defines professional competence in terms of selecting, directing, integrating and judging AI systems while remaining accountable for the result. It organizes observable professional behaviors across eight domains:
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- AI foundations
- Data and information
- Interaction and elicitation
- Automation and integration
- Solution design
- Evaluation and assurance
- Ethics, governance and professional practice
- Security
Use those domains as a menu, not a checklist every employee must complete. For example, a role that drafts routine internal material may need to demonstrate clear instructions, verification and appropriate data handling; a role that integrates AI into a workflow may also need to show automation and assurance skills.
The framework identifies four proficiency levels—Aware, Applied, Proficient and Advanced—and includes 255 published competency statements. Those figures describe the framework, not a validated test or a required scoring scale for employers. The framework says it specifies competence but leaves examinations, pass marks, duration and delivery methods to implementers.
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Role and responsibility should determine the expected depth. The OECD’s account of AI competencies for public-sector workers groups them as technical, managerial, and policy/legal/ethical, and distinguishes attitudinal, operational and literacy dimensions. It emphasizes that requirements vary by user group and role.
How to design a practical assessment
- Define the job scope. List the AI-related tasks the role may perform, the decisions that must remain with a person, the data restrictions that apply and the consequences of mistakes.
- Turn requirements into observable behaviors. State what a capable employee does—for instance, identifies the task and its constraints, chooses an appropriate tool, checks important claims, and flags uncertainty. Avoid vague criteria such as “AI savvy.” The framework’s observable principle is that each statement describes something another person can observe.
- Create an authentic work sample. Use a task resembling actual job work and approved tools, with information appropriate for the exercise. Include enough variation to see whether the employee can adapt, not just repeat a familiar prompt. OECD discusses complex occupational tasks as one possible way to examine AI’s application at work; UNESCO’s student competency framework recommends authentic, project-based assessment. Adapt those principles to adult roles rather than treating a student framework as a workplace standard.
- Score the process as well as the deliverable. A polished output alone may conceal weak verification or unsafe handling. Observe the employee’s choices and review the final result against explicit criteria.
- Calibrate and record ratings. Have assessors apply the same rubric to sample responses, document the evidence behind each rating and note whether a shortfall may reflect a skill gap, unclear policy, missing access or an unsuitable tool. Explicit criteria support consistent judgment, but the cited frameworks do not prescribe a particular calibration protocol.
- Check development with a parallel task. After training, use a different but similarly difficult task and the same rubric. Compare observed behaviors and output quality. UNESCO discusses progression assessment and adaptable practical tasks, but the reviewed sources do not establish a workplace retest interval or a validated change score.
What a shared scoring rubric can cover
Choose criteria that match the role and task. A practical rubric might rate each behavior on a defined scale, such as not demonstrated, partly demonstrated and consistently demonstrated, with written evidence for the rating. The scale below is an implementation example, not a quoted or validated instrument.
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| Criterion | Evidence to look for |
|---|---|
| Task framing | Identifies the goal, audience, constraints and what a correct result must include. |
| Tool choice and direction | Selects an approved tool suited to the task, gives relevant instructions and adjusts them when the first result falls short. |
| Verification | Checks material facts, reasoning and completeness against appropriate sources or requirements; does not treat a plausible answer as proof. |
| Adaptation | Recognizes when the approach is not working and changes course rather than relying on a memorized prompt. |
| Safe and responsible use | Follows data restrictions, recognizes relevant risks and observes applicable policy and review requirements. |
| Accountability | Uses human judgment on consequential decisions and takes responsibility for the deliverable rather than attributing errors to the tool. |
Not every criterion needs equal weight. A role handling sensitive information may require a strong data-handling threshold; another may place greater emphasis on integration or evaluation. Set expectations before the assessment and apply them consistently.
How to interpret results fairly
A work sample provides direct evidence of performance on a particular task, but it does not establish every aspect of a person’s AI capability. OECD’s AI and the Future of Skills, Volume 2 describes exploratory occupational tests for assessing AI capabilities and discusses limits of expert-judgment surveys. Those studies concern assessment of AI capabilities on job-related tests—not a validated procedure for measuring employees’ AI skills, nor a head-to-head finding that work samples outperform employee self-reports.
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Self-reports can still help identify confidence, perceived experience or concerns, but they do not show what someone can do in a defined work situation. Use them as context alongside observed evidence, not as the primary measure. A low reported level of use may reflect access, policy or opportunity rather than low skill. NIST notes that generative AI can affect workplace tasks positively and negatively and that employees may hold varied views about its use; its TEVV-Athlon material evaluates AI systems, not employees.
- Keep task conditions as consistent as the role allows, including tool access, instructions and time.
- Provide reasonable accessibility accommodations; do not confuse an assessment barrier with an AI skill gap.
- Separate lack of permission or experience with a particular tool from inability to reason about AI use.
- Use results to guide training and role-specific development unless a separate, validated process supports a consequential employment decision.
The UNESCO AI competency framework for students offers useful assessment-design principles, including authentic tasks, explicit criteria and progression, but it is not a validated adult workplace exam. Similarly, the abstract for AICOS describes an objective AI-literacy scale; the available abstract does not establish its validity for employee selection or performance management.
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Choosing an assessment method
Whichever format a manager uses, assess it against these questions before relying on the result:
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- Role relevance: Does the task resemble work the employee is actually expected to do?
- Observable evidence: Does it show behavior and output, rather than only knowledge or confidence?
- Responsible practice: Does it test relevant safety, ethical judgment and critical evaluation?
- Consistency and fairness: Are conditions and scoring criteria clear, repeatable and accessible?
- Development: Can a comparable task be used later to examine progress without simply repeating the same exercise?
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