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Leadership guide/Decisions/Academic integrity & authorship
Clear expectations and fair inquiry are essential when authorship or permitted assistance is in question.
HumanSkills recommendation · September 2026
In this guideRecommended positionYour local choicesA campus situationWho is responsibleLanguage to useEvidence of implementationSupporting sourcesDownload PDF→
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HumanSkills recommendationDefine the relevant conduct, publish assistance and disclosure expectations, and investigate concerns through existing fair procedures. Treat automated detection as information requiring scrutiny, never as sufficient proof by itself. Use proportionate process evidence and give the student an opportunity to respond.
A vague suspicion can damage trust. Demanding every prompt, draft and personal account record can create new privacy burdens. A sound process identifies the rule, examines relevant evidence and distinguishes a learning-support need from established misconduct.
What the evidence supportsCarteret links unauthorized student AI use to its academic-integrity process. North Shore addresses authorship and instructor expectations. The specific evidence safeguards recommended here are HumanSkills recommendations, not a measured sector consensus.
The right arrangement depends on the purpose, consequences, applicable requirements and capacity of your institution. Use these options to make the choice explicit.
Clarify and teach
Consider this when: The expectations were unclear or the concern can be addressed through learning support.
The tradeoff: Document the clarification and apply it consistently without converting uncertainty into an accusation.
Review the work and process
Consider this when: There is a specific concern requiring more information.
The tradeoff: Use relevant drafts, discussion or demonstration without demanding unnecessary personal records.
Use the conduct process
Consider this when: Evidence supports a potential violation of an established rule.
The tradeoff: Follow institutional procedures, evidence standards, accommodations and appeal rights.
An instructor receives a high automated score and suspects prohibited assistance. The student disputes it.
The score cannot establish the student’s conduct on its own.
What the team produces: A reasoned institutional decision based on relevant evidence and a fair process.
Board & trustees
Seek assurance that integrity processes are consistent, fair and open to review.
Institutional leaders
Align academic-integrity procedures, staff development and privacy safeguards.
AI task force
Clarify how AI-related concerns fit existing procedures without creating an unsupported shortcut.
Apply these roles within your institution’s actual governance and delegated authority.
Illustrative model clause
AI-related academic-integrity concerns must be addressed through established institutional procedures. Automated detection results must not be treated as conclusive evidence or the sole basis for an adverse finding. Review must consider relevant evidence, provide an opportunity to respond and protect applicable review and appeal rights.
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→
When to reconsiderRevisit inconsistent outcomes, excessive evidence requests, unclear rules or new assessment practices.
Selected precedents and guidance supporting this chapter. These sources do not imply institutional endorsement of HumanSkills or agreement with every recommendation.
Source review: 24 September 2026. Read the editorial approach.
Continue the workKeep opportunity within reach.
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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.