Why This Assessment Is Different

Most AI governance frameworks for education focus on compliance and safe use — what EdTech Hub’s AI Observatory calls Horizon 1: optimizing AI adoption within existing systems. The Woolis AI Institutional Resilience & Governance Scale 1.0 works at Horizon 2 — the disruption space where AI adoption is already reshaping what institutions know how to do, who owns their data, and whether they could keep functioning if a critical vendor disappeared tomorrow. It doesn’t ask whether you have the right policies. It asks whether your institution is actually resilient — whether the capacity, data rights, infrastructure, pedagogical judgment, and accountability needed to remain an autonomous educational actor are in place, or whether they have quietly migrated to someone else.

A rapid audit of where your institution stands on AI governance — right now, based on what you actually know. Each indicator can be answered in under a minute. Yes / Partially / Not Sure / No. No documentation is required to complete the rapid audit, though institutions should expect to validate responses during formal governance review. Click ► What counts on any indicator for specific scoring thresholds and evidence examples.

This is a self-assessment instrument. It is not an audit, compliance review, or external evaluation.

🔒 Your data is secure. Responses are submitted through Jotform, which uses 256-bit SSL encryption and meets GDPR compliance standards. Your name, institution, and assessment responses are used solely by Learning Agenda for research and reporting purposes — including aggregate analysis by role and sector. No student data or personal financial information is collected through this assessment.

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Foundational (3 pts) — most critical

Strategic (2 pts) — deepens resilience

Supporting (1 pt) — reinforces governance

Standard	Indicator	Pts	Response
Capacity & Institutional ExpertiseDoes AI adoption build your institution's capabilities — or hand them to a vendor?	1.1A written inventory identifies which AI tools each unit depends on — one you could produce or point to in under 15 minutes. YesA written inventory exists and was created or reviewed within the past 12 months. You could produce or point to it in under 15 minutes. PartiallyYou can identify key tools informally, or an inventory exists for some units but not all, or it hasn't been reviewed recently. NoNo formal inventory exists and no one has mapped tool dependencies.	3
	1.2A written continuity plan exists for each critical AI vendor, names responsible personnel, and describes what happens operationally if access is lost for 30 days. YesA written plan exists, covers each critical vendor, names responsible personnel, and was reviewed within the past 12 months. PartiallyA plan exists for some vendors or is in draft form, or it exists but has never been reviewed or tested. NoNo continuity planning exists.	3
	1.3At least one documented professional development activity per year builds capability independent of any single vendor platform. YesAt least one PD session per year is documented, was delivered, and explicitly focused on transferable capabilities — not a specific vendor platform. PartiallyVendor-independent PD has occurred informally or once but is not yet an annual institutional practice. NoAll PD is tool-specific. No vendor-independent capacity building has taken place.	2
	1.4Procurement reviews include a documented assessment of how each AI adoption affects institutional capability and vendor dependence. YesProcurement approval records show that at least one recent AI adoption included a documented capability-impact assessment. PartiallyCapability impact is discussed informally in procurement but is not consistently documented. NoProcurement decisions do not address capability or vendor dependence.	2
	1.5At least one named person per operational unit can perform core AI-assisted functions without the primary vendor platform. YesYou can name that person for each unit right now. The designation is documented. PartiallyNo partial credit — scores same as No. Useful for tracking progress. NoNo backup personnel have been identified for any unit.	1
	1.6Leadership can name at least one institutional process where AI adoption has reduced internal expertise or judgment capacity. YesLeadership can name that process right now and has documented or discussed it in a formal review. PartiallyNo partial credit — scores same as No. Useful for tracking progress. NoThis question has not been examined.	1
	Capacity & Institutional Expertise Subtotal		— / 12
Data Reciprocity & Commercial GovernanceWhen your work generates commercial value for AI vendors, is that relationship on your terms?	2.1A written record — based on actual contract review, not assumption — identifies what each AI vendor can do with your institutional data. YesA written record exists, is based on actual contract review, and covers all critical vendors — not assumption or vendor marketing. PartiallyA record exists for some vendors but not all, or it has not been verified against current contract language. NoNo one has examined what vendors can do with institutional data.	3
	2.2A named person has read the relevant data-use clauses in your AI vendor contracts within the past 24 months and can describe what they say. YesA named person has read the relevant clauses within the past 24 months and can describe what the contracts say about data training, retention, and secondary use. PartiallyContracts have been reviewed generally but not specifically for data training provisions, or the review covered some vendors but not all. NoAI vendor contracts have not been reviewed for data use provisions.	3
	2.3Your institution has formally determined whether opt-out provisions exist for each vendor and documented whether you have exercised them. YesYour institution has a documented, deliberate position on opt-outs for each critical vendor — not a default or an oversight. PartiallyOpt-out provisions have been identified for some vendors but the institutional position has not been formalized for all. NoOpt-out provisions have not been examined.	2
	2.4Legal counsel or a designated governance leader has reviewed AI vendor agreements specifically for data rights — not just general contract terms. YesLegal counsel or designated governance leadership has produced a documented review specifically addressing data rights and commercial use — not a general contract scan. PartiallyA legal review is in progress or has been completed informally without documentation. NoNo legal review has taken place.	2
	2.5Data-sharing and training-use terms are reviewed at each contract renewal, with changes from the previous cycle recorded. YesRenewal records show this happened at the most recent renewal — changes from the prior cycle are noted. PartiallyNo partial credit — scores same as No. Useful for tracking progress. NoContract renewals do not include a data use review.	1
	2.6Faculty and staff have been formally notified — in writing, policy, or training — when their work may contribute to vendor AI model improvement. YesFaculty and staff have been notified formally — in writing, policy, or documented training — at least once. PartiallyNo partial credit — scores same as No. Useful for tracking progress. NoNo notification process exists.	1
	Data Reciprocity & Commercial Governance Subtotal		— / 12
Infrastructure Resilience & Platform DependencyIf a major AI platform became unavailable tomorrow, could you keep running?	3.1Written exit plans exist for each critical AI vendor, include timelines and transition steps, and name accountable personnel. YesWritten exit plans exist for each critical vendor, include timelines and transition steps, name accountable personnel, and have been reviewed within the past 12 months. PartiallyExit plans exist for some vendors, are in draft form, or exist but have never been reviewed or updated. NoNo exit plans exist.	3
	3.2A documented inventory identifies what student or institutional data each vendor holds, in what format, and under what export conditions. YesA documented inventory exists covering all critical vendors — format, data type, and export conditions are specified. PartiallyThis information has been gathered informally or for some vendors but not all. NoNo data portability inventory exists.	3
	3.3A written alternatives analysis identifies substitute options for each function currently dependent on a single vendor. YesA written alternatives analysis identifies a specific substitute for each critical vendor-dependent function. PartiallyAlternatives have been identified informally or for some functions but not all. NoNo alternatives analysis has been conducted.	2
	3.4Governance body records confirm that vendor dependencies were reviewed in the past 12 months. YesMeeting records confirm a deliberate vendor dependency review in the past 12 months — not just a mention in passing. PartiallyDependencies have been discussed but not as a formal, documented annual review. NoGovernance bodies have not reviewed vendor dependencies.	2
	3.5Procurement records show that at least one recent AI platform adoption included a documented portability assessment before approval. YesAt least one recent adoption decision is documented as having included a portability assessment before approval. PartiallyPortability is considered informally or inconsistently — not as a standard requirement. NoPortability is not part of how AI platforms are evaluated.	2
	3.6A migration exercise, tabletop review, or contractual audit of exit readiness has been completed within the past 24 months. YesA completed migration exercise, tabletop review, or contractual audit is documented within the past 24 months. Yes = tested. Partially = plan exists but has not been tested. No = no exit planning or testing. PartiallyNo partial credit — scores same as No. Useful for tracking progress. NoNo exit planning or testing has occurred.	1
	3.7A written map or inventory identifies which operational functions would fail if your most-used AI platform became unavailable. YesA written map or inventory exists and was created or reviewed within the past 12 months. PartiallyNo partial credit — scores same as No. Useful for tracking progress. NoNo mapping has been done.	1
	Infrastructure Resilience & Platform Dependency Subtotal		— / 14
Pedagogical Integrity & Human JudgmentDoes AI adoption strengthen learning — or quietly replace the human judgment that makes education work?	4.1A written policy or documented institutional position defines which educational decisions must remain under human authority regardless of AI capability. YesA written policy or documented institutional position exists and has been formally adopted or communicated to relevant staff. PartiallyBoundaries have been discussed but no written position or policy has been formally adopted. NoNo boundaries have been defined or documented.	3
	4.2Faculty participate formally in decisions about AI systems that affect teaching, assessment, advising, or student support — not advisory only. YesFaculty hold a documented formal role — committee membership, sign-off authority, or review responsibility — in decisions about AI tools affecting teaching or assessment. Not advisory only. PartiallyFaculty are consulted informally or after decisions have already been made. NoFaculty have no defined role in AI governance decisions.	3
	4.3A documented disclosure process exists and has been applied — students are informed in writing when AI significantly mediates their learning or assessment. YesA documented disclosure process exists and has been applied — students can find this information in a syllabus, course site, or institutional communication. PartiallyDisclosure occurs in some courses or contexts but is not consistent or institutionally embedded. NoNo disclosure process exists.	2
	4.4At least one AI tool adoption has been evaluated through a documented process that asks whether it supports or replaces educator judgment. YesAt least one adoption decision is documented as having been evaluated through a process that explicitly asks whether the tool supports or replaces educator judgment. PartiallyPedagogical impact is considered informally but is not a documented part of the evaluation process. NoTool evaluations do not address pedagogical impact.	2
	4.5A written policy confirms faculty can override AI-supported recommendations in instructional or assessment contexts without consequence. YesA written policy exists and has been communicated to faculty. The policy confirms override is possible without consequence. PartiallyOverride is technically possible in practice but the policy is not written or communicated. NoNo override policy exists.	2
	4.6At least one AI adoption decision in the past 12 months included a documented evaluation against learning outcomes — not solely cost or efficiency. YesAt least one adoption decision in the past 12 months included documented evaluation against learning outcomes — alongside or separate from efficiency considerations. PartiallyNo partial credit — scores same as No. Useful for tracking progress. NoAdoption decisions are driven by cost and efficiency only.	1
	Pedagogical Integrity & Human Judgment Subtotal		— / 13
Stewardship & AccountabilityWhen something goes wrong with AI and student data, does someone specific own it?	5.1A named individual or governance body is documented in institutional records as accountable for AI stewardship and data governance. YesA named individual or body is documented in institutional records — org chart, policy, or governance charter — as accountable for AI stewardship. PartiallyA stewardship role is being established but is not yet formally documented. NoNo named steward or governance body exists.	3
	5.2The steward's identity and mandate are accessible — online, in policy, or in a handbook — without requiring a formal request. YesThe steward's identity and mandate are accessible in under two minutes — online, in policy, or in a handbook — without asking anyone. PartiallyThe steward is known internally but their mandate is not easy to find without asking. NoStudents, faculty, or staff cannot identify the responsible role without a formal request.	3
	5.3The steward's documented mandate explicitly covers AI-mediated data flows and automated systems — not only pre-AI compliance obligations. YesThe steward's documented mandate explicitly names AI-mediated data flows, algorithmic systems, or automated decision processes — not only FERPA-type compliance. PartiallyThe mandate probably covers AI but this is not explicitly stated in documentation. NoStewardship scope covers legacy compliance only.	2
	5.4AI governance documentation carries a revision date within the past 12 months and a defined review cycle is in place. YesGovernance documentation carries a revision date within the past 12 months and a defined review cycle is documented. PartiallyDocumentation exists but has not been reviewed within the past 12 months. NoNo documentation exists or it has never been updated.	2
	5.5Governance leadership reviews and approves significant AI adoptions before implementation. (Significant = affects 10%+ of users; mediates a core function such as instruction, assessment, advising, admissions, or HR; involves personal data; replaces an existing process; or involves a 12-month+ commitment.) YesRecords show that at least one significant AI adoption in the past 12 months was reviewed and approved before going live. Significant = affects 10%+ of users; mediates instruction, assessment, advising, admissions, or HR; involves personal data; replaces an existing process; or involves a 12-month+ commitment. PartiallyA pre-adoption review process exists but is applied inconsistently. NoSignificant AI adoptions are not reviewed before implementation.	2
	5.6Faculty hold a documented formal role in at least one AI governance process — with standing to raise, review, or influence decisions, not advisory only. YesFaculty hold a documented formal role — committee membership, sign-off, or review responsibility — in at least one AI governance process. Not advisory only. PartiallyFaculty are consulted informally or submit feedback but do not hold a formal governance role. NoFaculty have no formal role in AI governance.	2
	5.7AI governance responsibilities are documented as spanning at least three of: academic, legal, procurement, operational, and technology leadership. YesGovernance responsibilities are documented as spanning at least three of: academic, legal, procurement, operational, and technology leadership. PartiallyNo partial credit — scores same as No. Useful for tracking progress. NoAI governance is an IT function only.	1
	Stewardship & Accountability Subtotal		— / 15
Total Score			— / 60

Yes = full points | Partially = partial credit (Foundational & Strategic) | Not Sure = 0 | No = 0 If your response is Partially but evidence is mixed — for example, a process exists for some vendors but not others — rate Partially and note the gap. Partially and Not Sure both score 0 for Supporting indicators.

Score	Interpretation
0 – 15	Reactive Adoption — AI adoption is occurring with minimal governance coordination or continuity planning.
16 – 30	Emerging Governance — Governance structures exist but remain uneven, reactive, or dependent on individual leadership.
31 – 45	Structured Governance — The institution has established meaningful governance, accountability, and operational resilience structures.
46 – 60	Resilient Institutional Stewardship — AI adoption is governed through durable capacity, cross-functional stewardship, and continuity-oriented design.

Score	Interpretation
0 – 15	Reactive Adoption — AI adoption is occurring with minimal governance coordination or continuity planning.
16 – 30	Emerging Governance — Governance structures exist but remain uneven, reactive, or dependent on individual leadership.
31 – 45	Structured Governance — The institution has established meaningful governance, accountability, and operational resilience structures.
46 – 60	Resilient Institutional Stewardship — AI adoption is governed through durable capacity, cross-functional stewardship, and continuity-oriented design.

Woolis AI Institutional Resilience & Governance Scale 1.0

Why This Assessment Is Different

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Woolis AI Institutional Resilience & Governance Scale 1.0

Why This Assessment Is Different

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