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Leadership guide/Decisions/Teaching, learning & assessment

Teaching, learning & assessment

Protect the learning inside the work.

Permission to use a tool should follow the learning purpose of the task.

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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 recommendationHave faculty and academic governance define learning expectations and acceptable assistance within institutional safeguards. Make assignment rules explicit, design evidence of student understanding, and support accessible participation when AI use is required.

The same assistance can support learning in one task and remove the practice that another task was designed to provide. Students need to understand both the boundary and the educational reason for it. Faculty need support to change assessment without lowering the learning standard.

What the evidence supportsCarteret uses explicit course and assignment expectations within a tiered approach. North Shore addresses instructor permissions and AI-supported grading and feedback. Their different approaches illustrate choices that academic governance must resolve locally.

• Connect each AI-use rule to a learning objective.
• Communicate expectations before the work begins.
• Collect evidence of student understanding appropriate to the discipline.

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.

Independent demonstration

Consider this when: The task assesses knowledge or judgment students must exercise themselves.

The tradeoff: Explain the boundary and address accommodations and permitted support before assessment.

Bounded assistance

Consider this when: AI can support a defined stage while students remain responsible for the intellectual work.

The tradeoff: Specify what may be assisted, what must be independent and what process evidence is reasonable.

Integrated AI practice

Consider this when: The learning objective includes directing, evaluating or working responsibly with AI.

The tradeoff: Assess judgment and verification as well as the final product; provide equitable access.

Walk through the situation.

Illustrative campus caseA technical student produces a polished troubleshooting plan.

A student uses AI to prepare an equipment-diagnosis plan. The written result is convincing, but the instructor needs to know whether the student can recognize an unsafe recommendation.

The assessment must reveal the student’s judgment, not only the quality of the submitted plan.

1. State which AI assistance is permitted for the preparation.
2. Ask students to explain the diagnostic sequence and challenge an unsafe or unsupported suggestion.
3. Assess the practical demonstration and reasoning against the program’s learning and safety requirements.

What the team produces: An assignment rule, assessment method and explanation of the capability being assessed.

Put responsibility in the right place.

Board & trustees

Ask whether AI adoption strengthens learning and whether leadership can show credible evidence.

Institutional leaders

Provide faculty time, support and appropriate access for assessment redesign.

AI task force

Bring academic governance, accessibility, privacy and student perspectives into the institutional framework.

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

Language to build on.

Illustrative model clause

Faculty will state permitted AI assistance and disclosure expectations for learning and assessment activities within institutional policy. Assessment must provide appropriate evidence of student understanding and the capabilities required by the program. Required AI use must address access, privacy and applicable accommodations.

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 §8→Model policy §10→

Know whether it is working.

• Clear assignment expectations available in advance.
• Assessment evidence of student reasoning or performance.
• A supported route for access barriers and accommodations.

When to reconsiderReview when assessment no longer reveals the intended learning, program requirements change or tool use creates new barriers.

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.

• Carteret Community College: Artificial Intelligence Policy ↗Institutional policy
• North Shore Community College: Artificial Intelligence Principles and Policies ↗Institutional policy

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

Continue the workMake integrity a fair process.

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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.

Explain the rules for learning.→Make the policy yours→