Responsible AI.A clearer path forward.
A research-backed starting point for AI policy and governance. Built for your institution to make its own.
A practical route through the workA foundation for
your institution’s policy.
This resource brings together a synthesis of published higher education AI policies, an adaptable model policy and practical guidance for the decisions that remain yours.
It covers the institution as a whole: teaching and learning, student services, administration, data protection and the oversight of AI systems, including systems that take action on people’s behalf.
Whether you are developing a first policy or improving one already in place, use the materials to build a draft your institution can review, approve and put into practice. Downloadable documents let your team adapt the work in its own systems.
A practical route
through the work.
Begin with the step that matches your situation. An existing policy may need a focused update; a new effort may need the full sequence.
Understand the shared foundation
See which provisions recur across the policies we reviewed, where approaches differ and where more work is needed. Agree which starting positions fit your institution.
Research & findingsThe resultAn agreed foundationAdapt the policy
Use the editable model to develop your institutional draft. Match its purpose, scope, authority and responsibilities to your mission, culture and existing policies.
Adapt the policyThe resultA working institutional draftResolve the local choices
Work through the decisions that need your team’s judgment. Record the position you recommend, why it fits and who has authority to approve it.
Explore the decisionsThe resultDecisions ready for approvalTest the arrangements
Apply the draft to a real campus use. Check data protection, vendors, human oversight and what happens when something goes wrong.
Operations & assuranceThe resultEvidence that the arrangements workApprove and keep learning
Use your established governance process. Prepare the people who will put the policy into practice, assign owners and agree when to review it.
Monitoring & renewalThe resultAn adopted policy with accountable practice
Every month should
move the work forward.
In our work with leadership teams and AI task forces around the country, we hear a recurring concern. Capable people meet each month, gather more examples and work toward an AI policy. Many are spending precious time researching the same questions and arriving at similar starting positions.
There is value in doing that research together. We compare publicly available policies and guidance, examine recurring provisions and meaningful differences, and bring the findings into an adaptable policy and practical recommendations.
This gives your team a foundation to examine, accept and improve. Your time can go toward the questions that need local judgment: what serves your learners, how your institution makes decisions, what risks it can responsibly manage and how people will put the policy into practice.
Our Human-First, AI-Forward approach connects safeguards with human responsibility and capability. A useful policy should protect people, enable appropriate AI use and help the institution learn as the technology and its uses change.
The research informs the starting point. Your institution owns the decisions.