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Leadership guide/Decisions/Human capability & workforce relevance

Human capability & workforce relevance

Build the capability to use the permission.

Access and confidence are useful beginnings. Competent judgment has to be demonstrated in context.

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HumanSkills recommendation · September 2026

In this guideRecommended positionYour local choicesA campus situationWho is responsibleLanguage to useEvidence of implementationSupporting sourcesDownload PDF→

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A considered starting position.

HumanSkills recommendationConnect preparation to the responsibilities people will carry. Give students and employees practice in framing tasks, checking evidence, handling information, recognizing limits and exercising human judgment. Assess capability through relevant work, with support matched to the role.

A generic orientation may not prepare an advisor to identify a harmful recommendation or an instructor to evaluate an AI-assisted assessment. Workforce preparation should involve employers and program expertise while protecting the institution’s educational purpose.

What the evidence supportsNorthcentral connects AI to curriculum and employer needs. North Shore includes development opportunities, and Purdue assigns training responsibilities. Our role-based demonstration approach develops those ideas into an implementation recommendation.

• Define the responsibility before selecting the training.
• Practice realistic situations, including errors and uncertainty.
• Require appropriate evidence of readiness for consequential responsibilities.

What your institution decides.

The right arrangement depends on the purpose, consequences, applicable requirements and capacity of your institution. Use these options to make the choice explicit.

A common foundation

Consider this when: Everyone needs shared understanding of permissions, data handling and accountability.

The tradeoff: Keep it practical and accessible; completion alone does not establish readiness for specialist tasks.

Role-specific practice

Consider this when: The work involves distinct professional judgments or operational risks.

The tradeoff: Use realistic cases and qualified feedback; give staff time to participate.

Program-integrated learning

Consider this when: Students need AI-related capability as part of disciplinary and workforce preparation.

The tradeoff: Keep faculty ownership and assess transferable judgment alongside tool use.

Walk through the situation.

Illustrative campus caseAn employer wants graduates who can use AI.

An employer advisory group asks a technical program to add AI skills. The first idea is a short tool demonstration.

The program asks which tasks graduates must perform and which judgments they must retain.

1. Identify representative workplace tasks and the consequences of error.
2. Map the knowledge, independent performance and AI-supported work students need to demonstrate.
3. Design practice and assessment with faculty and employer input, including safety and ethical judgment.

What the team produces: A capability requirement linked to program outcomes and observable performance.

Put responsibility in the right place.

Board & trustees

Ask what evidence shows that students and employees are prepared for the responsibilities AI creates.

Institutional leaders

Fund preparation, support and time to practice as part of adoption.

AI task force

Connect policy permissions to role-specific readiness and existing professional development.

Apply these roles within your institution’s actual governance and delegated authority.

Language to build on.

Illustrative model clause

The institution must provide preparation appropriate to the AI responsibilities assigned to students and employees. Consequential uses require evidence that responsible personnel can evaluate outputs, apply safeguards and escalate concerns. Educational programs should address relevant human and AI-supported capabilities through appropriate learning and assessment.

Copy the clause

Review this clause with the full policy and local requirements. It is an implementation starting point, not a statement of measured consensus.

Model policy §7→Model policy §10→Model policy §12→

Know whether it is working.

• Role-specific capability expectations.
• Practice and feedback using relevant scenarios.
• Evidence of performance and a route for additional support.

When to reconsiderUpdate preparation when responsibilities, tools, professional expectations or observed failures change.

Read the supporting sources.

Selected precedents and guidance supporting this chapter. These sources do not imply institutional endorsement of HumanSkills or agreement with every recommendation.

• Northcentral Technical College: Artificial Intelligence ↗Employee guidance
• North Shore Community College: Artificial Intelligence Principles and Policies ↗Institutional policy
• Purdue University: Artificial Intelligence Use ↗Institutional policy

Source review: 24 September 2026. Read the editorial approach.

Continue the workMake correction possible.

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See the wider
evidence and choices.

Read the research comparisons and explore the annotated library across US colleges, universities and international institutions. Use the model language as a starting point for your own decision.

Support people in using AI responsibly.→Make the policy yours→

Build capability into the whole effort.

Explore preparation by role→