<h2 id="00c7fb3a-7392-44cf-896f-6bd57b4ba5c9" data-toc-id="00c7fb3a-7392-44cf-896f-6bd57b4ba5c9" style="text-align: left; font-size: 24px; line-height: 1.2;">Measure what people can do</h2><p>Course completion is useful information. It is not a complete picture of whether someone can apply AI well at work. Some certificates include substantial assessment; others mainly record participation. Look at the actual evidence behind the credential.</p><h2 id="0ad48513-0bca-4a87-b3fa-ba5375a1e704" data-toc-id="0ad48513-0bca-4a87-b3fa-ba5375a1e704" style="text-align: left; font-size: 24px; line-height: 1.2;">Capture a baseline</h2><p>Before training, choose a representative task and save an example of the work. Agree on what acceptable quality means, how sources should be checked and when a person should escalate rather than proceed.</p><p>Record time spent, errors, rework and support needed. Ask people how confident they feel, too, but treat confidence as a separate measure from demonstrated skill.</p><h2 id="1d014ea3-2bae-4f6e-acb8-208a45118486" data-toc-id="1d014ea3-2bae-4f6e-acb8-208a45118486" style="text-align: left; font-size: 24px; line-height: 1.2;">Use a small assessment scorecard</h2><ul><li><p><strong>Quality:</strong> does the result meet the task's requirements?</p></li><li><p><strong>Verification:</strong> can the learner identify and correct unsupported claims, wrong numbers or missing context?</p></li><li><p><strong>Judgment:</strong> do they use an approved tool and know what requires a human decision?</p></li><li><p><strong>Effort:</strong> how much time, correction and assistance does an accepted result require?</p></li><li><p><strong>Application:</strong> are they using the skill appropriately on relevant work?</p></li></ul><h2 id="c73aac7e-bebb-4f54-a0d4-de463e0e5db2" data-toc-id="c73aac7e-bebb-4f54-a0d4-de463e0e5db2" style="text-align: left; font-size: 24px; line-height: 1.2;">Compare similar work after training</h2><p>Repeat a comparable task using the same rubric. Review the output rather than relying on a self-rating alone. Include a realistic mistake for the learner to detect and explain. A faster draft that creates more correction work is not an automatic improvement.</p><p>You could review progress at 60 and 90 days if that fits the work cycle. Those are proposed checkpoints, not universal research-backed deadlines. Document changes in task difficulty, tool access and support so the comparison stays meaningful.</p><h2 id="8ce582b1-00df-4d16-b897-d37d9ab02393" data-toc-id="8ce582b1-00df-4d16-b897-d37d9ab02393" style="text-align: left; font-size: 24px; line-height: 1.2;">Use the result to improve the pathway</h2><p>If people struggle with checking sources, add practice and feedback there. If an approved tool is inaccessible, resolve the access problem. Confidence that lags performance may have several explanations; ask the learner rather than diagnosing it from a score.</p><p>External adoption statistics cannot tell you which individuals on your team need help. Your own assessed tasks can.</p><p>What is one real piece of work your team could complete and explain before and after training? Start with that, and give the learning somewhere useful to land.</p>

Assess AI fluency through task quality, verification, judgment, rework and appropriate use—not course completion or confidence alone.

Measuring AI Fluency: How to Know If Your Team's Training Is Actually Working

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Cre8ive Origins Blog | AI, Education & Entrepreneurship Insights

Exploring the intersection of AI, education, entrepreneurship, and human potential, from the team at Cre8ive Origins.

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Measuring AI Fluency: How to Know If Your Team's Training Is Actually Working

Measuring AI Fluency: How to Know If Your Team's Training Is Actually Working

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