Beyond Upgrade: Woolis Institutional Resilience Scale
Education in the Age of AI
https://www.thetransmitter.org/ with permission
What Your Institution Actually Knows
Two recurring questions have run through recent UNBOUND posts.
The first, from "Pedagogy and Governance": what has the institution agreed to before an AI tool ever reaches a classroom? The second, from "The Breach Is Not the Story": when a critical platform fails, what does the institution discover it doesn't control?
Both questions point toward the same gap — not a knowledge gap, but a practice gap. Institutions may have policies without having examined them. They may have data agreements without having read them. They may have named someone responsible for AI governance without that person's authority actually extending to the AI tools in use.
The gap is not between what institutions know and what they should know. It is between what they assume and what they would find if they looked.
Where the Research Points
The field is not without frameworks. EdTech Hub's AI Observatory, in "Beyond the Upgrade: A Theoretical Framework for Education in the Age of AI," organizes institutional decisions across three horizons: Horizon 1, where AI optimizes existing systems; Horizon 2, the disruption space where adoption is already reshaping what institutions know how to do; and Horizon 3, where education is redesigned for a world in which AI is structural. Most governance conversations are operating at Horizon 1. Most of the consequential institutional risk is accumulating at Horizon 2.
Crompton et al.'s 2026 global Delphi study, drawing on experts across 22 countries, identified eight consensus areas for AI governance in higher education — including academic integrity, privacy, and human oversight. That consensus matters. It establishes that the field has identified what governance requires. What it does not yet tell institutions is whether those requirements are actually in place — in their contracts, in their staffing, in their vendor relationships, in their procurement decisions made before anyone asked the governance questions.
The Woolis Institutional Resilience Scale 1.0 works at that intersection: where the research consensus meets institutional reality, and where the distance between them becomes visible.
An Instrument for Looking
The Scale is a free self-assessment: 26 indicators across five areas — Capacity & Expertise, Data Reciprocity & Commercial Governance, Infrastructure Resilience, Pedagogical Integrity & Human Judgment, and Stewardship & Accountability. Each indicator can be answered in under a minute — Strong, In Progress, or Absent — based on what your institution can demonstrate today, not what its documentation claims.
It is not a compliance checklist. It does not ask whether policies exist. It asks whether the capacity, data relationships, infrastructure, pedagogical judgment, and accountability structures needed to function as an autonomous educational actor are actually in place — and whether they would hold when conditions change.
This Is a Pilot
The Scale is being released now as a pilot, which means the instrument itself is still developing. Completing the assessment contributes to that development in two ways.
The aggregate responses — analyzed by role and sector — will build a picture of where institutions actually stand across the field. That evidence base does not yet exist in a form that is systematically gathered and publicly available.
The feedback section at the end of the form asks two questions I particularly want to hear from practitioners: whether there are areas of institutional AI risk or dependency the Scale didn't reach, and whether the indicators accurately reflected how these issues present in your institution. Both are questions of validity — whether the instrument measures what it claims to measure, for the people it is designed to serve. Your responses there are the most useful contribution you can make at this stage.
Who This Is For
The Scale is designed for anyone whose work involves institutional decisions about AI: faculty, administrators, institutional and executive leadership, policy advisors, funders, and program officers. It is free for non-commercial educational use with attribution.
It takes ten to fifteen minutes. No documentation is required.
Resources
On the Three Horizons Framework
EdTech Hub AI Observatory, Luz and Simpson, "Beyond the Upgrade: A Theoretical Framework for Education in the Age of AI." The source for the Horizon 1/2/3 distinction used throughout this piece.
edtechhub.org/2025/08/19/a-theoretical-framework-for-education-in-the-age-of-ai
On the Global Governance Consensus
Crompton et al., 2026 global Delphi study on AI governance in higher education. Eight consensus areas identified across experts in 22 countries — the foundation on which the Scale builds. Cited with full reference on the Scale page.
Institutional AI Readiness and Maturity
These instruments ask how prepared institutions are to adopt AI strategically. They are a necessary starting point — and a different question than the one the Scale asks.
EDUCAUSE Generative AI Readiness Assessment (developed with AWS). Covers strategy, governance, technology, workforce, and teaching and learning. Designed for cross-functional teams.
library.educause.edu/resources/2024/4/higher-education-generative-ai-readiness-assessment
Digital Education Council Ten Dimension AI Readiness Framework. Ten dimensions, four guiding principles, four levels of readiness. Designed for institution-wide AI integration across teaching, research, governance, and operations.
digitaleducationcouncil.com/post/ten-dimension-ai-readiness-framework
CoSN/CGCS K-12 Generative AI Maturity Tool (developed with AWS). The K-12 equivalent — a framework for school districts navigating AI adoption across instructional and operational functions.
Inside Higher Ed / Anthology AI-Digital Maturity Index 2025. Global benchmarking data across strategy, people, technology, and utilization.
insidehighered.com — AI-Digital Maturity Index 2025
Policy Frameworks and Compliance
These frameworks ask whether institutions have the right policies in place. They are essential — and distinct from asking whether those policies hold in practice.
Azevedo et al., "Institutional Policies on Artificial Intelligence in Higher Education: Frameworks and Best Practices for Faculty." New Directions for Adult and Continuing Education, 2025. Analyzes institutional AI policies across five dimensions: academic integrity, data privacy, equity, transparency, and pedagogical autonomy.
onlinelibrary.wiley.com/doi/10.1002/ace.70013
Mississippi AI Network AI Policy and Guidance Template for Higher Education (2026). A planning framework for developing institution-specific approaches to AI governance across academic, procurement, legal, and operational functions.
mainms.org/ai-policy-guidance-template-higher-education-2026
AI in Teaching and Learning
These resources address the pedagogical dimension: whether and how AI strengthens learning. They are the companion to — not a substitute for — the governance questions the Scale asks.
UNESCO, AI and the Future of Education: Disruptions, Dilemmas and Directions (2025). Eight major themes on AI as a structural force in education, with direct implications for the role of the teacher and the purposes of schooling.
OECD Digital Education Outlook 2026. State-of-the-art review of generative AI in teaching, learning, and institutional management.
oecd.org/en/publications/oecd-digital-education-outlook-2026
OECD AI Literacy Framework (final version 2026). Maps the competencies students need to evaluate, adapt to, and responsibly use AI.
Related UNBOUND Posts
Pedagogy and Governance: What Responsible AI Adoption Requires
A note on what these resources share
Taken together, the readiness tools, policy frameworks, and pedagogical guidance above represent the current state of the field's self-examination on AI in education. They ask whether institutions are prepared to adopt AI, whether their policies are in order, and whether AI is being used to strengthen learning.
None of them systematically addresses what happens when a critical vendor becomes unavailable, whether data agreements have been read and not merely signed, whether accountability structures actually extend to the AI tools now in use, or whether the capacity to function as an autonomous educational actor would hold when conditions change. That is the specific territory the Woolis Institutional Resilience Scale 1.0 was built to cover — not as a replacement for the frameworks above, but as a necessary companion to them.
