The sixlens AI readiness framework
9 weighted dimensions, three cross-cutting scores, and a 0–100 scale. This is the whole instrument. Use it, adapt it, or run it with us.
No sign-up and no download form. A framework you have to trade an email for is not a framework, it is an advertisement.
What it measures, and how much each counts
Readiness is not one number and the parts are not equal. Curriculum weighs more than culture because curriculum is where change either happens or does not. Weights are shown as the assessment applies them, and sum to 100%.
Curriculum & Pedagogy
15%Whether AI has been integrated into course design, learning objectives and teaching methods, or is being used around the edges of a curriculum that has not changed.
AI Infrastructure & Technology
13.5%Computing resources, data systems and tool availability. The dimension that most often turns out to be the real constraint behind an ambition nobody can act on.
Faculty Readiness & Development
13.5%AI literacy among the people who teach, the training they have had, and the training they say they need.
Equity & Access
13.5%Whether access to AI tools is even across the organization, including accessibility support and digital inclusion. Scored separately because an average hides the gap it is averaging.
Workforce Alignment
11.3%How well what is taught matches what employers and the wider labor market are asking for.
Data Governance & Ethics
10.5%Data management practices, privacy policy, and whether anyone has written down what acceptable AI use actually means.
Student Support & Services
7.5%AI-enhanced student services, accessibility and learning support, and whether the people using them were consulted.
Institutional Strategy & Leadership
7.5%Strategic planning, leadership commitment and resource allocation. Usually the dimension that explains the others.
Culture & Overall Readiness
7.5%How the organization feels about AI as a whole: confidence, concern, and whether people believe leadership has a plan.
Three scores computed across dimensions
Some questions are only answerable by looking at several dimensions together. An organization can score well on infrastructure and badly on who actually reaches it, and a single average hides exactly that.
Equity
Whether AI is reaching everyone, or only the parts of the organization that were already well resourced.
Computed from: Equity & Access · Student Support & Services
Workforce readiness
Whether what people are being taught lines up with what they will be expected to do.
Computed from: Workforce Alignment · Curriculum & Pedagogy · Faculty Readiness & Development
Policy maturity
Whether there is governance behind the activity, or activity without governance.
Computed from: Data Governance & Ethics · Institutional Strategy & Leadership
Reading a score
Responses are collected on a five-point scale and normalized to 0–100. A score with no interpretation attached is a number somebody will interpret for themselves, usually generously, so the bands are stated.
Practice is established and documented. The work is extending it, not starting it.
Real activity is underway, unevenly. Usually strong in pockets and unsupported elsewhere.
Individuals are experimenting without a framework around them.
Little activity, and little agreement on what should happen next.
No meaningful activity in this area.
How to run it yourself
- Ask about each dimension separately. A single “how ready are we?” question returns a mood, not a measurement.
- Survey widely, including the people who are not in the room when AI gets discussed. The gap between what leadership believes and what everyone else reports is usually the finding.
- Weight the dimensions before averaging them. An unweighted average lets a strong showing on something cheap disguise a weak one on something that matters.
- Report the spread as well as the average, or the equity picture disappears into the mean.
- Turn each weak dimension into one decision with an owner and a date. A score that produces no decision changes nothing.
Or have it run for you
Running this across a whole organization means writing the questions, chasing responses, weighting the results, and turning them into something a board will read. That is the part we do.