Institutional (regional) accreditors
Reach AI through existing standards, integrity, effectiveness, resources, faculty role, rather than AI-specific ones (so far).
SACSCOC · HLC · MSCHE · WSCUC · NECHE · NWCCU
For accreditors & external assessors
A different dimension: not the people running AI inside the institution, but the review of how well it's governed, and the institution's job of showing that it is. One backbone, read from either side.
Accreditation and external assessment are a different dimension of this framework. Every other view here is internal, the people inside the institution doing the work. This one faces outward: the third parties who evaluate how well an institution governs AI, and the institution's job of demonstrating that it does. The two directions share one backbone, so this page holds both. Read it as a campus preparing for review, or as an assessor looking in.
You're the institution. A reviewer, regional accreditor, programmatic body, state authorizer, or external assessor, is going to ask how AI is governed. Here's what they'll probe and what to have ready.
Most accreditors don't yet have AI-specific standards. They reach AI through the standards they already enforce: academic integrity, student achievement, institutional effectiveness, faculty oversight of curriculum, privacy and data stewardship, and adequacy of resources. That is good news for a governance-first institution, you don't need a separate AI compliance regime, you need to show that AI is governed inside the structures you already have. This page maps the framework to the questions a reviewer is likely to ask through those existing standards.
The crosswalk: pillars × review
For each pillar, the question a reviewer is likely to ask, and the evidence that answers it well. Use it to find your gaps before someone else does.
PillarWhat review will askEvidence that answers it
1Mission & visionMission · institutional effectiveness
Is AI use tied to the institution's mission and educational purpose, or bolted on?
AI strategy or principles that reference mission; board/cabinet acknowledgment that AI serves teaching, learning, and operations, not the reverse.
2PrinciplesIntegrity · ethics
Are there stated values governing AI (fairness, transparency, human oversight, accountability)?
Published AI principles or a values statement, adopted through governance, not a single administrator's memo.
3Policies & guidelinesAcademic integrity · faculty oversight
Is AI addressed in policy, and is it integrated with existing policy rather than a detached one-off?
AI addressed inside academic-integrity, data, privacy, and procurement policy; technology-neutral language that survives the next tool; a review/update cadence.
4Governance, risk & dataGovernance · resources · privacy
Who governs AI, how are risks reviewed, and how is data protected?
A named governance body with a charter; a documented risk-review process; data classification and an AI inventory; FERPA and privacy obligations mapped.
5Engagement & collaborationShared governance · faculty role
Were faculty, students, and staff engaged, not just informed, in shaping AI decisions?
Evidence of shared governance: faculty senate involvement, student input, consultation records, not a top-down rollout.
6ReadinessFaculty support · student achievement
Are people equipped to use AI responsibly, with literacy and support funded?
AI literacy/training offerings, role-based competency expectations, and a budget line, evidence the institution invested in capacity, not just rules.
7Roles & responsibilitiesAdministrative capacity
Is accountability for AI assigned to actual people and positions?
AI responsibilities written into position descriptions and governance roles; clear ownership for decisions, review, and incident response.
8Implementation & operationsInstitutional effectiveness · continuous improvement
Does governance operate in practice, or only on paper?
Operating evidence: an AI inventory that's maintained, completed tool reviews, audit trails, monitoring, and at least one cycle of the process actually run.
Evidence to have ready
The artifacts a review tends to ask for. If you can produce most of these, your governance is real, not theoretical. Gaps here are your to-do list.
AI governance charter & bodyWho decides, scope, membership, and cadence, adopted through governance.
AI principles / values statementPublished, institution-level, not a single office's.
AI in existing policyIntegrity, data, privacy, and procurement policy that addresses AI, technology-neutral.
AI inventory / registryWhat AI is in use, including features switched on inside licensed tools, with owner and data classification.
Risk / impact assessmentsCompleted reviews for higher-stakes uses, with decisions recorded.
Audit trails & monitoringEvidence that deployed AI is logged, monitored, and reviewed over time.
Literacy & training recordsOfferings delivered, participation, and the budget behind them.
Consultation & shared-governance recordsHow faculty, students, and staff were engaged in AI decisions.
Roles & accountabilityAI responsibilities in position descriptions and named owners for review and incidents.
Incident & remediation logWhat went wrong, what was done, and that issues were tracked to closure.
Who's starting to ask
No single body owns AI in accreditation yet. It arrives through the standards and authorities that already exist, from several directions at once.
Programmatic & specialized accreditors
More likely to probe AI in discipline-specific terms, student work, clinical or professional judgment, assessment validity.
ABET · AACSB · CAEP · CCNE · LCME and others
State authorizers & system offices
May ask about AI in consumer-protection, data, and academic-quality terms, especially for online programs.
State authorization · system-level policy
Recognized standards & regulation
Increasingly the reference points reviewers and counsel point to, even when not themselves accreditors.
NIST AI RMF · ISO/IEC 42001 · EU AI Act
Accreditor names are illustrative of the landscape, not an indication that each has issued AI-specific requirements. Always work from your own accreditor's current standards and guidance.
Governed AI isn't a separate compliance regime. It's your existing structures, working.